Mean Sea Level Pressure Research Articles

AATTENUATION—The Atmospheric Attenuation Model for CSP Tower Plants: A Look-Up Table for Operational Implementation

Attenuation of solar radiation between the receiver and the heliostat field in concentrated solar power (CSP) tower plants can reduce the overall system performance significantly. The attenuation varies strongly with time and the average attenuation at different sites might also vary strongly from each other. If no site specific attenuation data is available, the optimal plant design cannot be determined and rough estimations of the attenuation effect are required leading to high uncertainties of yield analysis calculations. The attenuation is caused mainly by water vapor content and aerosol particles in the lower atmospheric layer above ground. Although several on-site measurement systems have been developed during recent years, attenuation data sets are usually not available to be included during the plant project development. An Atmospheric Attenuation (AATTENUATION) model to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity, and barometric pressure measurements was developed and validated by the authors earlier. The model allows the accurate estimation of attenuation for sites with low attenuation and gives an estimation of the attenuation for less clear sites. However, the site-dependent coefficients of the AATTENUATION model had to be developed individually for each site of interest, which required time-consuming radiative transfer simulations, considering the exact location and altitude, as well as the pre-dominant aerosol type at the location. This strongly limited the application of the model despite its typically available input data. In this manuscript, a look-up table (LUT) is presented which enables the application of the AATTENUATION model at the site of interest without the necessity to perform the according complex radiative transfer calculations for each site individually. This enables the application of the AATTENUATION model for virtually all resource assessments for tower plants and in an operational mode in real time within plant monitoring systems around the world. The LUT also facilitates the generation of solar attenuation maps on the basis of long-term meteorological data sets which can be considered during resource assessment for CSP tower plant projects. The LUTs are provided together with this manuscript as supplementary files. The LUT for the AATTENUATION model was developed for a solar zenith angle (SZA) grid of 1°, an altitude grid of 100 m, 7 different standard aerosol types and the standard AFGL atmospheres for mid-latitudes and the tropics. The LUT was tested against the original version of the AATTENUATION model at 4 sites in Morocco and Spain, and it was found that the additional uncertainty introduced by the application of the LUT is negligible. With the information of latitude, longitude, altitude above mean sea level, DNI, relative humidity (RH), ambient temperature (Tair), and barometric pressure (bp), the attenuation can be now derived easily for each site of interest.

Sea level prediction using climatic variables: a comparative study of SVM and hybrid wavelet SVM approaches

Climate change is expected to adversely affect the coastal ecosystem in many ways. One of the major consequences of climate change in coastal areas is sea level rise. In order to manage this problem efficiently, it is essential to obtain reasonably accurate estimates of future sea level. This study focuses essentially on the identification of climatic variables influencing sea level and sea level prediction. Correlation analysis and wavelet coherence diagrams were used for identifying the influencing variables, and support vector machine (SVM) and hybrid wavelet support vector machine (WSVM) techniques were used for sea level prediction. Sea surface temperature, sea surface salinity, and mean sea level pressure were observed to be the major local climatic variables influencing sea level. Halosteric effect is found to have a major impact on the sea level. The variables identified were subsequently used as predictors in both SVM and WSVM. WSVM employs discrete wavelet transform to decompose the variables before being input to the SVM model. The performance of both the models was compared using statistical measures such as root mean square error (RMSE), correlation coefficient (r), coefficient of determination (r2), average squared error, Nash–Sutcliffe efficiency, and percentage bias along with graphical indicators such as Taylor diagrams and regression error characteristic curves. Results indicate that the WSVM model predicted sea level with an RMSE of 0.029 m during the training and 0.040 m during the testing phases. The corresponding values for SVM are 0.043 m and 0.069 m, respectively. Also, the other statistical measures and graphical indicators suggest that WSVM technique outperforms the SVM approach in the prediction of sea level.

German Bight storm activity, 1897–2018

AbstractThis study investigates the evolution of German Bight (southeastern North Sea) storminess from 1897 to 2018 through analysing upper quantiles of geostrophic wind speeds, which act as a proxy for past storm activity. Here, geostrophic wind speeds are calculated from triplets of mean sea level pressure observations that form triangles over the German Bight. The data used in the manuscript are provided by the International Surface Pressure Databank and the national meteorological services of Denmark, Germany, and the Netherlands. The derivation of storm activity is achieved by enhancing the established triangle proxy method via combining and merging storminess time series from numerous partially overlapping triangles in an ensemble‐like manner. The utilized approach allows for the construction of robust, long‐term and subdaily German Bight storminess time series. Further, the method provides insights into the underlying uncertainty of the time series. The results show that storm activity over the German Bight is subject to multidecadal variability. The latest decades are characterized by an increase in activity from the 1960s to the 1990s, followed by a decline lasting into the 2000s and below‐average activity up until present. The results are backed through a comparison with reanalysis products from four datasets, which provide high‐resolution wind and pressure data starting in 1979 and offshore wind speed measurements taken from the FINO‐WIND project. This study also finds that German Bight storminess positively correlates with storminess in the NE Atlantic in general. In certain years, however, notably different levels of storm activity in the two regions can be found, which likely result from shifted large‐scale circulation patterns.

Análise Climatológica das trajetórias de Ciclones Extratropicais formados na região da Península Antártica

Cyclones play an important role in the general circulation of the atmosphere, enabling the meridional transport of excess heat, humidity and momentum from low latitudes to high latitudes. In the Southern Hemisphere, the area between southern Brazil and the Peninsula Antarctica (AP) is described as one of the most favorable for the formation of cyclones (30°S to 70°S) due to the existence of strong temperature gradient between the ocean and the surface air layer above the ocean and also because of pre-existing baroclinic instabilities. This study is associated with the Project ATMOS (AnTarctic Modeling Observation System) and explored the role of extratropical cyclones in teleconnections between high and medium latitudes to track the trajectories of extratropical cyclones that are formed in the Antarctic Peninsula (AP) and move towards the central sector of the South Atlantic. The analysis of the tracked trajectories showed that the cyclones reached the central sector of the South Atlantic during the months of autumn (greater number) and winter (greater displacement), while the statistical analysis indicated that the intensity of the cyclones is more linearly linked to the mean sea level pressure field than to Superficial Sea Temperature Anomalies.

Long-term variability of the mean sea level pressure field over the Black Sea

This paper aims to evaluate spatiotemporal variability of the mean sea level pressure trends over the Black Sea using the gridded 40-year (1979-2018) reanalysis mean sea level pressure data from two different datasets. These datasets are the European Centre for Medium-Range Weather Forecasts ERA-Interim with a spatial resolution of 0.25° and temporal resolution of six hours, and the National Centers for Environmental Prediction/Climate Forecast System Reanalysis with a spatial resolution of 0.5° and temporal resolution of one hour. Data from both databases show that the mean sea level pressure tends to decrease towards the recent years over the entire Black Sea. The long-term averages of mean sea level pressure reflect the spatial variability over the Black Sea with much lower pressures in the eastern part of the Black Sea than that on the western side. The long-term variation is more intense in the eastern part of the Black Sea. Sea Level Anomaly over the Black Sea, spanning 26 years between 1993 and 2018, was analyzed using satellite altimetry data. It was found that there is a high Sea Level Anomaly where the mean sea level pressure is low, described by an inverted barometer response. The 39-year long Sea Surface Temperature Anomaly data (1981-2018) indicate the rising tendency in sea surface temperature towards recent years in the entire Black Sea. An inverse relationship is found between North Atlantic Oscillation index and sea surface temperature anomaly. On a seasonal scale, mean sea level pressure in winter (high-pressure system) is larger than that in summer (low-pressure system). Our analyses show that mean sea level pressure tends to decrease, sea surface temperature anomaly and sea surface temperature anomaly tends to increase in recent years over the Black Sea.

Atmospheric Circulation as a Factor Contributing to Increasing Drought Severity in Central Europe

AbstractLong‐lasting and severe droughts seriously threaten agriculture, ecosystems, and society. Summer 2018 in central Europe was characterized by unusually persistent heat and drought, causing substantial economic losses, and became a part of a several years long dry period observed across this region. This study assesses the magnitude of the recent drought within a long‐term context and links the increased drought severity to changes in atmospheric circulation. Temporal variability of drought conditions since the late 19th century was analyzed at seven long‐term stations distributed across the Czech Republic using the Palmer Drought Severity Index and the Standardized Precipitation Evaporation Index. The Palmer Z Index and a variation of the Standardized Precipitation Evaporation Index were used to study rapidly emerging short‐term droughts and to link these episodes to atmospheric circulation. Changes in circulation were analyzed through circulation types calculated from flow strength, direction and vorticity in mean sea level pressure data from the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis for 1948–2018. Increasing drought severity across the Czech Republic with record‐low values of the drought indices during 2015–2018 was found. The trend was distinctive in both vegetation (April–September) and cold (October–March) periods. The tendency toward more severe droughts in recent decades was linked to changes in frequency of dry and wet circulation types, highlighting the important role of atmospheric circulation in regional climate. It remains an open question whether the significantly increasing frequency of dry circulation types in the vegetation period is related to climate change, or rather represents multidecadal climate variability.

Seasonal reconstructions coupling ice core data and an isotope-enabled climate model – methodological implications of seasonality, climate modes and selection of proxy data

Abstract. The research area of climate field reconstructions has developed strongly during the past 20 years, motivated by the need to understand the complex dynamics of the earth system in a changing climate. Climate field reconstructions aim to build a consistent gridded climate reconstruction of different variables, often from a range of climate proxies, using either statistical tools or a climate model to fill the gaps between the locations of the proxy data. Commonly, large-scale climate field reconstructions covering more than 500 years are of annual resolution. In this method study, we investigate the potential of seasonally resolved climate field reconstructions based on oxygen isotope records from Greenland ice cores and an isotope-enabled climate model. Our analogue-type method matches modeled isotope patterns in Greenland precipitation to the patterns of ice core data from up to 14 ice core sites. In a second step, the climate variables of the best-matching model years are extracted, with the mean of the best-matching years comprising the reconstruction. We test a range of climate reconstructions, varying the definition of the seasons and the number of ice cores used. Our findings show that the optimal definition of the seasons depends on the variability in the target season. For winter, the vigorous variability is best captured when defining the season as December–February due to the dominance of large-scale patterns. For summer, which has weaker variability, albeit more persistent in time, the variability is better captured using a longer season of May–October. Motivated by the scarcity of seasonal data, we also test the use of annual data where the year is divided during summer, that is, not following the calendar year. This means that the winter variability is not split and that the annual data then can be used to reconstruct the winter variability. In particularly when reconstructing the sea level pressure and the corresponding main modes of variability, it is important to take seasonality into account, because of changes in the spatial patterns of the modes throughout the year. Targeting the annual mean sea level pressure for the reconstruction lowers the skill simply due to the seasonal geographical shift of the circulation modes. Our reconstructions based on ice core data also show skill for the North Atlantic sea surface temperatures, in particularly during winter for latitudes higher than 50∘ N. In addition, the main modes of the sea surface temperature variability are qualitatively captured by the reconstructions. When testing the skill of the reconstructions using 19 ice cores compared to the ones using eight ice cores, we do not find a clear advantage of using a larger data set. This could be due to a more even spatial distribution of the eight ice cores. However, including European tree-ring data to further constrain the summer temperature reconstruction clearly improves the skill for this season, which otherwise is more difficult to capture than the winter season.

The impact of weather patterns on offshore wind power production

Large-scale weather systems have the potential to modulate offshore wind energy production. The Northern European sea areas have recently seen a rapid increase in wind power capacity and thus there is a need to understand how different weather systems affect offshore production from the perspective of energy system integration. In this study, mean sea level pressure data from a new-generation reanalysis (ERA5) are utilised to classify synoptic systems into 30 different weather patterns using a self-organising map (SOM) approach. ERA5 wind speeds are then used in conjunction with a reference 8 MW wind turbine power curve to estimate wind power values at selected offshore sites. We assess how wind power output varies for different weather patterns, specifically, the impact on power production and power ramps.

Determinants of fog and low stratus occurrence in continental central Europe – a quantitative satellite-based evaluation

The formation and development of fog and low stratus clouds (FLS) depend on meteorological and land surface conditions and their interactions with each other. While analyses of temporal and spatial patterns of FLS in Europe exist, the interactions between FLS determinants underlying them have not been studied explicitly and quantitatively at a continental scale yet. In this study, a state-of-the-art machine learning technique is applied to model FLS occurrence over continental Europe, using meteorological and land surface parameters from geostationary satellite and reanalysis data. Spatially explicit model units are created to test for spatial and seasonal differences in model performance and FLS sensitivities to changes in predictors, and effects of different data preprocessing procedures are evaluated. The statistical models show good performance in predicting FLS occurrence during validation, with R2>0.9 especially in winter high pressure situations.The predictive skill of the models seems to be dependent on data availability, data preprocessing, time period, and geographic characteristics. It is shown that atmospheric proxies are more important determinants of FLS presence than surface characteristics, in particular mean sea level pressure, near-surface wind speed and evapotranspiration are crucial, together with FLS occurrence on the previous day. Higher wind speeds, higher land surface temperatures and higher evapotranspiration tend to be negatively related to FLS. Spatial patterns of feature importance show the dominant influence of mean sea level pressure on FLS occurrence throughout the central European domain. When only high pressure situations are considered, wind speed (in the western study region) and evapotranspiration (in the eastern study region) gain importance, highlighting the influence of moisture advection on FLS occurrence in the western parts of the central European domain. This study shows that FLS occurrence can be accurately modeled using machine learning techniques in large spatial domains based on meteorological and land surface predictors. The statistical models used in this study provide a novel analysis tool for investigating empirical relationships in the FLS – land surface system and possibly infer processes.

Seasonal Forecasts of Winter Temperature Improved by Higher‐Order Modes of Mean Sea Level Pressure Variability in the North Atlantic Sector

AbstractThe variability of the sea level pressure in the North Atlantic sector is the most important driver of weather and climate in Europe. The main mode of this variability, the North Atlantic Oscillation (NAO), explains up to 50% of the total variance. Other modes, known as the Scandinavian index, East Atlantic, and East Atlantic/West Russian pattern, complement the variability of the sea level pressure, thereby influencing the European climate. It has been shown previously that a seasonal prediction system with enhanced winter NAO skill due to ensemble subsampling entails an improved prediction of the surface climate variables as well. Here, we show that a refined subselection procedure that accounts both for the NAO index and for the three additional modes of sea level pressure variability is able to further increase the prediction skill of wintertime mean sea level pressure, near‐surface temperature, and precipitation across Europe.

Influence of synoptic scale atmospheric circulation on the development of urban heat island in Prague and Bucharest

This study has analysed the development of the urban heat island (UHI) under various synoptic scale atmospheric circulation for two large cities – Prague in central Europe and Bucharest in south-eastern Europe, including seasonal differences and long-term changes. At the best of our knowledge, it is the first comparison between two European cities from this perspective. Analysis was conducted on the base of minimum air temperature data from pairs of urban and peri-urban stations. The average UHI intensity is 2.3 °C for Prague and 1.8 °C for Bucharest, it exceeds 4 °C in 6–10% of cases, and the highest values occur in August in both cities. The annual course of monthly mean values of UHI intensities has higher amplitude in Bucharest (1.2 °C) than Prague (0.6 °C). Synoptic scale circulation is classified according to mean sea level pressure data from ECMWF Era-Interim reanalysis using cost733class software. The results show that UHI is more intense under anticyclonic situations with southern winds in the both cities. Over 1981–2016, we found that the UHI intensity followed statistically significant increasing trend, much larger trend for Bucharest than Prague (3.3 °C vs. 1.3 °C / 100 years).

Weather type classification and its relation to precipitation over southeastern China

AbstractIt is very important to identify the possible mechanism of precipitation generation in southeastern China. To determine the weather type (WT) that is likely to generate precipitation, the Jenkinson–Collison classification method was applied to wintertime daily mean sea level pressure data across southeastern China. We found that all WTs with easterly components are prone to generate precipitation. These WTs were merged into one type named the ensemble of easterly wind components (the EE type). The persistence and transformation rules of the EE type are studied. Anticyclone type is the easiest one to convert to EE type. The persistence and transformation rules of the EE type provide favourable conditions for the formation of precipitation. Furthermore, we examined the influence of two common teleconnections (Western Pacific [WP] and Eurasian [EU]) in East Asia in winter on the WTs. When WP is in the positive phase and EU is in the negative phase, EE type weather patterns will appear frequently in southeastern China. At this time, it is most conducive to the formation of precipitation. On the one hand, warm and cold air converge in this area. On the other hand, the prevailing east wind can provide sufficient water vapour. The study implies that whether a WT is easy to produce precipitation can be judged by wind direction. The results will help us better understand the physical mechanism of precipitation generation in this area.

Study of an Extremely Severe Cyclonic Storm “Fani” over Bay of Bengal using regional NCUM modeling system: A case study

This study assesses the predictive capability of the high resolution National Centre for Medium Range Weather and Forecasting (NCMRWF) regional Unified Model (NCUM-R) for pre- and post-genesis characteristics associated with a TC. We have selected an Extremely Severe Cyclonic Storm (ESCS) “Fani”, which formed over Bay of Bengal (BoB) during 26 April–04 May, 2019 for the study. To examine the pre-genesis of the storm, we used the model analyses and forecasts of NCUM-R three days prior to the genesis (i.e 26th April 2019) of the TC. The results clearly show the establishment of a moisture conveyor belt (MCB) during the pre-genesis period, which connects the TC inner core with underlying oceanic region. The MCB plays an important role in the transport of large amounts of moisture towards the storm inner core from the oceanic regions and subsequently for the release of latent heat in the vicinity of the storm, resulting the intensification of TC. These processes were clearly predicted by the NCUM-R model during the pre-genesis period.The study also examined role of various dynamic and thermodynamic variables; intensity; track and its errors and various indexes at different stages of TC. The evolution of intensity based on mean sea level pressure (MSLP) and 10 m maximum sustainable wind (MSW) clearly shows the rapid intensification and dissipation of the storm in NCUM-R simulations, the pattern is reasonably well matched with the observations. The area averaged thermodynamical variables around the eyewall of the storm in the forecast based on different initial conditions (ICs) are well simulated by the model and the pattern is well matched with the ERA5 reanalyses. The model predicted track from different ICs are reasonably well matched with the observed track. The large scale environmental flow plays a vital role in the recurvature of the TC. The study clearly suggested that the NCUM-R model is able to predict the intensity, movement and structure of the storm during pre- and post-genesis period. However, the model results show intense storm during early stage as compared to observation.

The importance of North Atlantic Ocean transports for seasonal forecasts

The Atlantic Meridional Overturning Circulation (AMOC) is a main driver for predictability at decadal time scales, but has been largely ignored in the context of seasonal forecasts. Here, we show compelling evidence that AMOC initialization can have a direct and strong impact on seasonal forecasts. Winter reforecasts with SEAS5, the current operational seasonal forecasting system by the European Centre for Medium-Range Weather Forecasts, exhibit errors of sea-surface temperature (SST) in the western part of the North Atlantic Subpolar Gyre that are strongly correlated with decadal variations in the AMOC initial conditions. In the early reforecast period 1981–1996, too warm SST coincide with an overly strong AMOC transporting excessive heat into the region. In the ocean reanalyses providing the forecast initial conditions, excessive heat transport is balanced by additional surface cooling from relaxing towards observed SST, and therefore the fit to observations is acceptable. However, the additional surface cooling contributes to enhanced deep convection and strengthens the AMOC, thereby establishing a feedback loop. In the forecasts, where the SST relaxation is absent, the balance is disrupted, and fast growth of SST errors ensues. The warm SST bias has a strong local impact on surface air temperature, mean sea-level pressure, and precipitation patterns, but remote impact is small. In the late reforecast period 2001–2016, neither the SST in the western North Atlantic nor the AMOC show large biases. The non-stationarity of the bias prevents an effective forecast calibration and causes an apparent loss of skill in the affected region. The case presented here demonstrates the importance of correctly initializing slowly varying aspects of the Earth System such as the AMOC in order to improve forecasts on seasonal and shorter time scales.

Climatological Large-Scale Circulation Patterns over The Middle Americas Region

In this study, twenty large-scale circulation patterns are identified to generate a synoptic classification of Weather Types (WT) over a region that comprises Mexico, the Intra-Americas Seas, Central America, and northern South America. This classification is performed using Self-Organizing Maps (SOMs) with mean sea-level pressure standardized anomalies from reanalysis. The influence of quasi-permanent pressure centers over the region, such as North Atlantic Subtropical High (NASH) and North Pacific High (NPH) are well captured. Seasonal variability of high-pressure centers for dry (November–April) and wet (May–October) periods over the entire region are also well represented in amplitude and pattern among the WTs. The NASH influence and intensification of the Caribbean low-level jet and the North American monsoon system is well captured. During the dry period, a strong trough wind advects cold air masses from mid-latitudes to the subtropics over the western Atlantic Ocean. High-frequency transitions among WTs tend to cluster around the nearest neighbors in SOM space, while low-frequency transitions occur along columns instead of rows in the SOM matrix. Low-frequency transitions are related to intraseasonal and seasonal scales. The constructed catalog can identify near-surface atmospheric circulation patterns from a unified perspective of synoptic climate variability, and it is in high agreement with previous studies for the region.

Linking weather patterns to regional extreme precipitation for highlighting potential flood events in medium‐ to long‐range forecasts

AbstractMedium‐ to long‐range precipitation forecasts are a crucial component in mitigating the impacts of fluvial flood events. Although precipitation is difficult to predict at these lead times, the forecast skill of atmospheric circulation tends to be greater. The study explores using weather patterns (WPs) as a preliminary step in producing forecasts of upper‐tail precipitation threshold exceedance probabilities for the UK. The WPs are predefined, discrete states representing daily mean sea‐level pressure (MSLP) over a European–North Atlantic domain. The WPs most likely to be associated with flooding are highlighted by calculating upper‐tail exceedance probabilities derived from the conditional distributions of regional precipitation given each WP. WPs associated with higher probabilities of extreme precipitation are shown to have occurred during two well‐known flood events: the 2014 Somerset Levels floods in southwest England; and Storm Desmond over the northern UK in December 2015. To illustrate the potential of this WP‐based prediction framework, a forecast guidance tool called Fluvial Decider is introduced. It is intended for use by hydro‐meteorologists in the England and Wales Flood Forecasting Centre (FFC). Forecasts of the MSLP from ensemble prediction systems (EPSs) are assigned to the closest‐matching WP, providing daily probabilistic forecasts of WPs out to the chosen lead time. Combining these probabilities with observed precipitation threshold exceedance probabilities provides a parsimonious tool for highlighting potential periods with increased risk of flooding. Model forecasts using the European Centre for Medium‐range Weather Forecasts (ECMWF) EPS highlighted both flood events as being at a higher than average risk of heavy extreme precipitation at lead times of over five days.

The linkages between Antarctic sea ice extent and Indian summer monsoon rainfall

Teleconnection between the Antarctic sea ice and the tropical climate has been extensively investigated. This study examines the interannual relationship between the variability of sea ice extent in the Indian Ocean sector (20–90°E) and Indian summer monsoon rainfall under the influence of the Mascarene High. Sea ice extent during April-May-June (AMJ) appears to have a significant correlation with the summer monsoon rainfall over Peninsular India region during June-July-August-September from 1979 to 2013. Composites of mean sea level pressure (MSLP), 500 hPa geopotential height, and 850 hPa wind anomalies during high and low ice phases show a positive relation between the sea ice extent and the Mascarene High, revealing that high (low) ice phase corresponds respectively to the strengthening (weakening) of the Mascarene High as well as an increase (decrease) in Indian summer monsoon rainfall. During the respective high (low) ice phase years, positive (negative) MSLP anomalies were found, particularly over the Mascarene High region, associated with the eastwards (westwards) shifts of its climatology locations. Similar features were observed at 500 hPa geopotential height anomalies. In addition, strong anticyclonic (cyclonic) anomalies in the Mascarene High region were found in 850 hPa winds, which led to corresponding strong (weak) south westerlies and thus respective positive (negative) Indian summer monsoon rainfall anomalies.

A Three-Dimensional Perspective on Extratropical Cyclone Impacts

AbstractCyclones can be identified from gridded pressure data at different levels of the troposphere, with vertical structure known to influence the temporal development and impacts of midlatitude cyclones. However, studies of midlatitude cyclones typically focus on cyclones identified at a single atmospheric level. This paper examines how the frequency of vertically organized or deep cyclones varies around the world, with a focus on southeastern Australia. About 50% of global cyclones identified from mean sea level pressure show a coherent vertical structure extending to at least 500 hPa, based on ERA-Interim reanalysis data, and shallow cyclones are most common in the global midlatitudes. Using a combination of reanalysis data and satellite-based rainfall and lightning, we show that in southeast Australia deep cyclones have higher intensities, longer durations, and more severe winds and rainfall than either shallow surface cyclones or upper-level cyclones with no surface low, motivating a three-dimensional approach for future cyclone analyses.

Ground-based and OMI-TROPOMI NO2 measurements at El Arenosillo observatory: Unexpected upward trends

Eleven years, January 2008 to June 2019, of hourly nitrogen dioxide (NO2) levels recorded at El Arenosillo observatory (Southwestern Europe) were analyzed. Annual averages ranged between 4 μg m−3 and 6 μg m−3 with peaks exceeding 40 μg m−3. A slight monthly variation was observed with maximum and minimum values in the cold (∼6 μg m−3) and warm (∼4 μg m−3) seasons respectively. A diurnal pattern was found with a weak amplitude (∼3 μg m−3). The monthly trends were investigated using surface observations and OMI (Ozone Monitoring instrument) satellite measurements. An unexpected upward trend was obtained in the last five years. The periods with elevated NO2 concentrations in the last years were analyzed, showing an increase in its frequency and concentrations, linked with the upward trend observed. The weather conditions in these NO2 peaks were studied using local surface meteorology, mean sea level pressure and wind fields from the data reanalysis of ERA5. The transport of NO2 was explored using TROPOMI (Tropospheric Monitoring Instrument) measurements. The events occurred under conditions governed by high-pressure systems, which induced weak synoptic airflows or the development of mesoscale processes. Four scenarios of NO2 transport were identified, associated with weak synoptic flows from inland or Southern Portugal and with mesoscale processes. The gulf of Cadiz plays an important role as a reservoir where the NO2 coming from the south of Portugal, the Western Mediterranean Basin and urban-industrial areas can be accumulated and later transported inland. A strong correlation was found between the increase of NO2 observed in the last years and positive anomalies of the temperature and geopotential height at 850 and 500 hPa levels. These findings could indicate that the causes of the changes in the NO2 would be attributed to alterations in the weather patterns associated with a warmer climate.

A semi‐objective circulation pattern classification scheme for the semi‐arid Northeast Brazil

AbstractThe semi‐arid Northeast Brazil (NEB) is just recovering from a very severe water crisis induced by a multiyear drought. With this crisis, the question of water resources management has entered the national political agenda, creating an opportunity to better prepare the country to deal with future droughts. In order to improve climate predictions, and thus preparedness in NEB, a circulation pattern (CP) classification algorithm offers various options. Therefore, the main objective of this study was to develop a computer aided CP classification based on the Simulated ANnealing and Diversified RAndomization clustering (SANDRA) algorithm. First, suitable predictor variables and cluster domain setting are evaluated using ERA‐Interim reanalyses. It is found that near surface variables such as geopotential at 1,000 hPa (GP1,000) or mean sea level pressure (MSLP) should be combined with horizontal wind speed at the upper 700 hPa level (UWND700). A 11‐cluster solution is favoured due to the trade‐offs between interpretability of the cluster centroids and the explained variances of the predictors. Second, occurrence and transition probabilities of this 11‐cluster solution of GP1,000 and UWND700 are analysed, and typical CPs, which are linked to dry and wet conditions in the region are identified. The suitability of the new classification to be potentially applied for statistical downscaling or CP‐conditional bias correction approach is analysed. The CP‐conditional cumulative density functions (CDFs) exhibit discriminative power to separate between wet and dry conditions, indicating a good performance of the CP approach.