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Many studies have focused on understanding the drivers of soil moisture droughts in Europe when the events have already intensified. Still, how atmospheric circulation changes throughout the entire life cycle of droughts, particularly during the transition periods to drought initiation and termination, has not been thoroughly investigated. Therefore, this study investigates temporal characteristics and atmospheric circulation associated with onsets (a transition period from a normal condition to drought) and terminations (a transition period from drought to a normal condition) of soil moisture droughts in Europe during 1980–2020. The typical duration, preferred seasons of occurrence, and atmospheric circulation during onsets and terminations are examined. The regions of study are central (CEU) and Mediterranean (MED) Europe, and soil moisture from ERA5-Land, GLEAM version 3, SoMo.ml, Noah-LSM, and a simulation from the Community Land Model version 5 (CLM-TRENDY) are utilized. Our findings indicate that the duration of onsets and terminations depends on the dataset: the five soil moisture datasets exhibit different mean duration of onsets and terminations, with ERA5-Land showing shorter and GLEAM longer duration across Europe. Nevertheless, within the same dataset, onsets and terminations exhibit similar durations, implying that onsets and terminations can occur at the same speed. Regarding the preferred seasons for onsets and terminations, onsets occur more during the wet seasons, specifically summer in CEU and autumn and winter in MED. Nevertheless, the frequencies of occurrence during these seasons only slightly exceed that during other seasons. Terminations tend to occur more during the driest seasons. For atmospheric circulation, onsets come with large-scale anticyclonic atmospheric circulation patterns. Terminations do not exhibit a dominant mean circulation pattern over the region where terminations occur but instead show geopotential height anomalies with reduced magnitudes in Europe and a high-pressure system over the North Atlantic. The anticyclonic circulation during onsets is anomalously persistent and shows linkages with the North Atlantic Oscillation (NAO). Positive NAO occurs much more frequently during onsets compared to other drought phases. This finding implies an important role for this large-scale mode of variability in initiating dry periods by reducing soil moisture and its potential to serve as an early warning for droughts during the period prior to the events. [ABSTRACT FROM AUTHOR]

The Mediterranean region is expected to experience significant changes in hydroclimate, reflected in increases in the duration and severity of soil moisture droughts. While numerous studies have explored Mediterranean droughts in coupled climate models under present and future scenarios, understanding droughts in past-climate simulations remains relatively underexplored. Such simulations can offer insights into long-term drought variability that observational records cannot capture. Therefore, our study investigates circulation patterns in the Euro-Atlantic domain associated with multi-year soil moisture droughts over the Mediterranean region during the last millennium (850–2005 CE) in climate simulations. For this, we use the fifth phase of the Climate Model Intercomparison Project–Paleoclimate Model Intercomparison Project Phase 3 (CMIP5–PMIP3) and the CESM Last Millennium Ensemble Project. Primarily, we examine the differences among the models in representing drought variability and related circulation patterns. For the analysis, we exclude the anthropogenic trends from 1850–2005 CE, and to detect the circulation patterns, we perform k -means clustering combined with linear correlation analyses. The findings confirm that Mediterranean drought occurrence during the last millennium is associated with internal variability in the climate system. Drought variability, the associated circulation patterns, and the frequencies of these patterns vary across the models. Some climate models exhibit a multidecadal anti-phase occurrence of some drought periods between the western and eastern Mediterranean regions, although the exact periods of coherence differ among the models. This anti-phase co-variability, which agrees with some proxy records, can be explained by the dominant circulation patterns in each region detected by the models: western Mediterranean droughts are dominated by a high-pressure system over central Europe and a North Atlantic Oscillation (NAO)-like pattern, while eastern Mediterranean droughts are linked to positive pressure anomalies in the southern and eastern Mediterranean and negative NAO, East Atlantic, and East Atlantic–western Russia-like patterns. The frequencies of these modes of climate variability are strongly model-dependent; i.e., some patterns occur more frequently or only in some models, suggesting that the main drivers of droughts differ among the models. Although it is complicated to evaluate the representation of droughts and associated circulation among the models, in general, the models with lower horizontal and vertical spatial land resolutions exhibit drought variability and patterns that distinctly differ from other models. These model differences and preferences toward some circulation patterns can be a source of uncertainties in the model–proxy comparison of Mediterranean droughts and potentially influence future climate projections. [ABSTRACT FROM AUTHOR]

Knowing that internal climate variability is the principal driver of Mediterranean droughts during the last millennium, in this study, we investigate circulation patterns in the Euro-Atlantic domain associated with multi-year droughts over the Mediterranean region in CMIP5-PMIP3 and CESM-LME simulations. The focus is on the natural variability of droughts during 850-2005, thus excluding the anthropogenic trends from 1850 CE onward. The results re-confirm that Mediterranean drought occurrence during the last millennium is associated with internal variability. In terms of the temporal variability, all climate models exhibit a multi-decadal anti-phase occurrence of droughts between the western and eastern Mediterranean agreeing with some proxy records. This anti-phase occurrence of droughts can be explained by the dominant circulation patterns in each region: western Mediterranean droughts are dominated by a high-pressure system and ridge over central Europe and an NAO-like pattern, while eastern Mediterranean droughts are linked to positive pressure anomalies in the southern and eastern Mediterranean, negative NAO, negative EA and EA-WR-like patterns. However, these modes of climate variability are strongly model-dependent, i.e. each model has its preferred circulation patterns that occur more frequently during droughts, suggesting that the main drivers of droughts differ between the models. The models' preference is therefore a potential source of uncertainties in Mediterranean droughts in model-proxy comparison and may have implications for future climate projections. [ABSTRACT FROM AUTHOR]

Knowing that internal climate variability is the principal driver of Mediterranean droughts during the last millennium, in this study, we investigate circulation patterns in the Euro-Atlantic domain associated with multi-year droughts over the Mediterranean region in CMIP5-PMIP3 and CESM-LME simulations. The focus is on the natural variability of droughts during 850–2005, thus excluding the anthropogenic trends from 1850 CE onward. The results re-confirm that Mediterranean drought occurrence during the last millennium is associated with internal variability. In terms of the temporal variability, all climate models exhibit a multi-decadal anti-phase occurrence of droughts between the western and eastern Mediterranean agreeing with some proxy records. This anti-phase occurrence of droughts can be explained by the dominant circulation patterns in each region: western Mediterranean droughts are dominated by a high-pressure system and ridge over central Europe and an NAO-like pattern, while eastern Mediterranean droughts are linked to positive pressure anomalies in the southern and eastern Mediterranean, negative NAO, negative EA and EA-WR-like patterns. However, these modes of climate variability are strongly model-dependent, i.e. each model has its preferred circulation patterns that occur more frequently during droughts, suggesting that the main drivers of droughts differ between the models. The models' preference is therefore a potential source of uncertainties in Mediterranean droughts in model-proxy comparison and may have implications for future climate projections. [ABSTRACT FROM AUTHOR]

In this study, we hypothesized that shifts in the kinetic parameters of extracellular hydrolytic enzymes may occur as a consequence of seasonal environmental disturbances and would reflect the level of adaptation of the bacterial community to the organic matter of the ecosystem. We measured the activities of enzymes that play a key role in the bacterial growth (leucine aminopeptidase, β‐ and α‐glucosidases) in surface coastal waters of the Eastern Cantabrian Sea and determined their kinetic parameters by computing kinetic models of distinct complexity. Our results revealed the existence of two clearly distinct enzymatic systems operating at different substrate concentrations: a high‐affinity system prevailing at low substrate concentrations and a low‐affinity system characteristic of high substrate concentrations. These findings could be the result of distinct functional bacterial assemblages growing concurrently under sharp gradients of high‐molecular‐weight compounds. We constructed an ecological network based on contemporaneous and time‐delayed correlations to explore the associations between the kinetic parameters and the environmental variables. The analysis revealed that the recurring phytoplankton blooms registered throughout the seasonal cycle trigger the wax and wane of those members of the bacterial community able to synthesize and secrete specific enzymes. [ABSTRACT FROM AUTHOR]

The performance of the Weather Research and Forecasting (WRF) model version 3.8.1 at convection-permitting scale is evaluated by means of several sensitivity simulations over southern Peru down to a grid resolution of 1 km, whereby the main focus is on the domain with 5 km horizontal resolution. Different configurations of microphysics, cumulus, longwave radiation, and planetary boundary layer schemes are tested. For the year 2008, the simulated precipitation amounts and patterns are compared to gridded observational data sets and weather station data gathered from Peru, Bolivia, and Brazil. The temporal correlation of simulated monthly accumulated precipitation against in situ and gridded observational data show that the most challenging regions for WRF are the slopes along both sides of the Andes, i.e. elevations between 1000 and 3000 m above sea level. The pattern correlation analysis between simulated precipitation and station data suggests that all tested WRF setups perform rather poorly along the northeastern slopes of the Andes during the entire year. In the southwestern region of the domain the performance of all setups is better except for the driest period (May–September). The results of the pattern correlation to the gridded observational data sets show that all setups perform reasonably well except along both slopes during the dry season. The precipitation patterns reveal that the typical setup used over Europe is too dry throughout the entire year, and that the experiment with the combination of the single-moment 6-class microphysics scheme and the Grell–Freitas cumulus parameterization in the domains with resolutions larger than 5 km, suitable for East Africa, does not perfectly apply to other equatorial regions such as the Amazon basin in southeastern Peru. The experiment with the Stony Brook University microphysics scheme and the Grell-Freitas cumulus parameterization tends to overestimate precipitation over the northeastern slopes of the Andes, but enforces a positive feedback between the soil moisture, air temperature, relative humidity, mid-level cloud cover and, finally, precipitation. Hence, this setup provides the most accurate results over the Peruvian Amazon, and particularly over the department of Madre de Dios, which is a region of interest because it is considered a biodiversity hotspot of Peru. The robustness of this particular configuration of the model is backed up by similar results obtained during wet climate conditions observed in 2012. [ABSTRACT FROM AUTHOR]

The performance of the Weather Research and Forecasting (WRF) model version 3.8.1 at convection-permitting scale is evaluated by means of several sensitivity simulations over southern Peru down to a grid resolution of 1 km, whereby the main focus is on the domain with 5 km horizontal resolution. Different configurations of microphysics, cumulus, longwave radiation and planetary boundary layer schemes are tested. For the year 2008, the simulated precipitation amounts and patterns are compared to gridded observational data sets and weather station data gathered from Peru, Bolivia and Brazil. The temporal correlation of simulated monthly precipitation sums against in-situ and gridded observational data show that the most challenging regions for WRF are the slopes along both sides of the Andes, i.e., elevations between 1000 and 3000 m above sea level. The pattern correlation analysis between simulated precipitation and station data suggests that all tested WRF setups perform rather poorly along the northeastern slopes of the Andes during the entire year. In the southwestern region of the domain the performance of all setups is better except for the driest period (May-September). The results of the pattern correlation to the gridded observational data sets show that all setups perform reasonably well except along both slopes during the dry season. The precipitation patterns reveal that the typical setup used over Europe is too dry throughout the entire year, and that the experiment with the combination of the single-moment 6-class microphysics scheme and the Grell-Freitas cumulus parameterization in the domains with resolutions larger than 5 km, suitable for East Africa, does not perfectly apply to other equatorial regions such as the Amazon basin in southeastern Peru. The experiment with the Stony-Brook University microphysics scheme and the Grell-Freitas cumulus parameterization tends to overestimate precipitation over the northeastern slopes of the Andes, but allows to enforce a positive feedback between the soil moisture, air temperature, relative humidity, mid-level cloud cover and finally, also precipitation. Hence, this setup is the one providing the most accurate results over the Peruvian Amazon, and particularly over the department of Madre de Dios, which is a region of interest because it is considered the biodiversity hotspot of Peru. The robustness of this particular parameterization option is backed up by similar results obtained during wet climate conditions observed in 2012. [ABSTRACT FROM AUTHOR]

The ability of two downscaling experiments to correctly simulate thermodynamic conditions over the Iberian Peninsula (IP) is compared in this paper. To do so, three parameters used to evaluate the unstable conditions in the atmosphere are evaluated: the total totals index (TT), convective available potential energy (CAPE), and convective inhibition (CIN). The Weather and Research Forecasting (WRF) model is used for the simulations. The N experiment is driven by ERA-Interim's initial and boundary conditions. The D experiment has the same configuration as N, but the 3DVAR data assimilation step is additionally run at 00:00, 06:00, 12:00, and 18:00 UTC. Eight radiosondes are available over the IP, and the vertical temperature and moisture profiles from the radiosondes provided by the University of Wyoming and the Integrated Global Radiosonde Archive (IGRA) were used to calculate three parameters commonly used to represent atmospheric instability by our own methodology using the R package aiRthermo. According to the validation, the correlation, standard deviation (SD), and root mean squared error (RMSE) obtained by the D experiment for all the variables at most of the stations are better than those for N. The different methods produce small discrepancies between the values for TT, but these are larger for CAPE and CIN due to the dependency of these quantities on the initial conditions assumed for the calculation of a lifted air parcel. Similar results arise from the seasonal analysis concerning both WRF experiments: N tends to overestimate or underestimate (depending on the parameter) the variability of the reference values of the parameters, but D is able to capture it in most of the seasons. In general, D is able to produce more reliable results due to the more realistic values of dew point temperature and virtual temperature profiles over the IP. The heterogeneity of the studied variables is highlighted in the mean maps over the IP. According to those for D, the unstable air masses are found along the entire Atlantic coast during winter, but in summer they are located particularly over the Mediterranean coast. The convective inhibition is more extended towards inland at 00:00 UTC in those areas. However, high values are also observed near the southeastern corner of the IP (near Murcia) at 12:00 UTC. Finally, no linear relationship between TT, CAPE, or CIN was found, and consequently, CAPE and CIN should be preferred for the study of the instability of the atmosphere as more atmospheric layers are employed during their calculation than for the TT index. [ABSTRACT FROM AUTHOR]

Several sensitivity experiments with the Weather Research and Forecasting (WRF) model version 3.8.1 have been performed to find the optimal parameterization setup for precipitation amounts and patterns around Mount Kenya at a convection-permitting scale of 1 km. Hereby, the focus is on the cumulus scheme, with tests of the Kain–Fritsch, the Grell–Freitas, and no cumulus parameterizations. In addition, two longwave radiation schemes and two planetary boundary layer parameterizations are evaluated, and different nesting ratios and numbers of nests are tested. The precipitation amounts and patterns are compared against a large amount of weather station data and three gridded observational data sets. The temporal correlation of monthly precipitation sums show that fewer nests lead to a more constrained simulation, and hence the correlation is higher. The pattern correlation with weather station data confirms this result, but when comparing it to the most recent gridded observational data set the difference between the number of nests and nesting ratios is marginal. The precipitation patterns further reveal that using the Grell–Freitas cumulus parameterization in the domains with resolutions >5 km provides the best results when it comes to precipitation patterns and amounts. If no cumulus parameterization is used in any of the domains, the temporal correlation between gridded and in situ observations and simulated precipitation is especially poor with more nests. Moreover, even if the patterns are captured reasonably well, a clear overestimation in the precipitation amounts is simulated around Mount Kenya when using no cumulus scheme in all domains. The experiment with the Grell–Freitas cumulus parameterization in the domains with resolutions >5 km also provides reasonable results for 2 m temperature with respect to gridded observational and weather station data. [ABSTRACT FROM AUTHOR]

A new diagram is proposed for the verification of vector quantities generated by multiple models against a set of observations. It has been designed with the objective, as in the Taylor diagram, of providing a visual diagnostic tool which allows an easy comparison of simulations by multiple models against a reference dataset. However, the Sailor diagram extends this ability to two-dimensional quantities such as currents, wind, horizontal fluxes of water vapour and other geophysical variables by adding features which allow us to evaluate directional properties of the data as well. The diagram is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. This matrix is separated in a part corresponding to the bias and the relative rotation of the two orthogonal directions (empirical orthogonal functions; EOFs) which best describe the vector data. Since there is no truncation of the retained EOFs, these orthogonal directions explain the total variability of the original dataset. We test the performance of this new diagram to identify the differences amongst the reference dataset and a series of model outputs by using some synthetic datasets and real-world examples with time series of variables such as wind, current and vertically integrated moisture transport. An alternative setup for spatially varying time-fixed fields is shown in the last examples, in which the spatial average of surface wind in the Northern and Southern Hemisphere according to different reanalyses and realizations from ensembles of CMIP5 models are compared. The Sailor diagrams presented here show that it is a tool which helps in identifying errors due to the bias or the orientation of the simulated vector time series or fields. The R implementation of the diagram presented together with this paper allows us also to easily retrieve the individual diagnostics of the different components of the mean squared error and additional diagnostics which can be presented in tabular form. [ABSTRACT FROM AUTHOR]

The ability of two downscaling experiments to correctly simulate the instability conditions that can trigger thunderstorms over the Iberian Peninsula is compared in this paper. To do so, three instability indices are evaluated: TT index, CAPE and CIN. The WRF model is used for the simulations. The N experiment is driven by ERA-Interim's initial and boundary conditions; The D experiment has the same configuration as N, but the 3DVAR data assimilation step is additionally run at 00, 06, 12 and 18 UTC. Eight radiosondes are available over the IP, and the values for these indices calculated from the University of Wyoming were chosen as reference in the validation of both simulations. Additionally, measured variables at different pressure levels from the radiosondes provided byWyoming were used to calculate the three instability indices by our own methodology using the R package aiRthermo. According to the validation, the correlation, SD and RMSE obtained by the experiment D for all the indices in most of the stations are better than those for N. The different methodologies produce small discrepancies between the values for TT, but these are larger for CAPE and CIN due to the dependency of these indices on the initial conditions assumed for the calculation of an air parcel's vertical evolution. Similar results arise from the seasonal analysis concerning both WRF experiments: N tends to overestimate or underestimate (depending on the index) the variability of the reference values, but D is able to capture it in most of the seasons. The heterogeneity of the indices is highlighted in the mean maps over the Iberian Peninsula. According to those from D, the ingredients for the development of convective precipitation during winter are found along the entire Atlantic coast, but in summer they are located particularly in the Mediterranean coast. The chances of developing thunderstorms in those areas at 12 UTC is much higher than at 00 UTC; The convective inhibition is more extended towards inland at 00 UTC in those areas, which prevents storms from developing. However, high values are observed near Murcia also at 12 UTC. [ABSTRACT FROM AUTHOR]

A new diagram is proposed for the verification of vector quantities generated by multiple models against a set of observations. It has been designed with the idea of extending the Taylor diagram to two dimensional quantities such as currents, wind, or horizontal fluxes of water vapour, salinity, energy and other geophysical variables. The diagram is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. This matrix is separated in a part corresponding to the bias and the relative rotation of the empirical orthogonal functions of the data. We test the performance of this new diagram to identify the differences amongst the reference dataset and the different model outputs by using examples with wind, current, vertically integrated moisture transport and wave energy flux time series. An alternative setup is shown in the last examples, where the spatial average of surface wind in the Northern and Southern Hemispheres according to different reanalyses and realizations of CMIP5 models are compared. The examples of use of the Sailor diagram presented show that it is a tool which helps in identifying errors due to the bias or the orientation of the simulated vector time series or fields. The R implementation of the diagram presented together with this paper allows also to easily retrieve the individual diagnostics of the different components of the mean squared error. [ABSTRACT FROM AUTHOR]

There have been numerous statistical and dynamical downscaling model comparisons. However, differences in model skill can be distorted by inconsistencies in experimental set-up, inputs and output format. This paper harmonizes such factors when evaluating daily precipitation downscaled over the Iberian Peninsula by the Statistical DownScaling Model (SDSM) and two configurations of the dynamical Weather Research and Forecasting Model (WRF) (one with data assimilation (D) and one without (N)). The ERA-Interim reanalysis at 0. 75 ∘ resolution provides common inputs for spinning-up and driving the WRF model and calibrating SDSM. WRF runs and SDSM output were evaluated against ECA&D stations, TRMM, GPCP and EOBS gridded precipitation for 2010–2014 using the same suite of diagnostics. Differences between WRF and SDSM are comparable to observational uncertainty, but the relative skill of the downscaling techniques varies with diagnostic. The SDSM ensemble mean, WRF-D and ERAI have similar correlation scores ( r = 0.45 –0.7), but there were large variations amongst SDSM ensemble members ( r = 0.3 –0.6). The best Linear Error in Probability Space ( LEPS = 0.001 –0.007) and simulations of precipitation amount were achieved by individual members of the SDSM ensemble. However, the Brier Skill Score shows these members do not improve the prediction by ERA-Interim, whereas precipitation occurrence is reproduced best by WRF-D. Similar skill was achieved by SDSM when applied to station or gridded precipitation data. Given the greater computational demands of WRF compared with SDSM, clear statements of expected value-added are needed when applying the former to climate impacts and adaptation research. [ABSTRACT FROM AUTHOR]

Abstract: An analysis of the atmospheric branch of the hydrological cycle by means of a 15 km resolution numerical integration performed using Weather Research and Forecasting (WRF) nested in ERA Interim is presented. Two WRF experiments covering the period 2010–2014 were prepared. The first one (N) was configured as in standard numerical downscaling experiments. The second one (D), with the same parameterizations, included a step of 3DVAR data assimilation every 6 h. Apart from comparing our results with ERA Interim data, several observational data sets were used to validate the precipitable water (radiosondes and MODIS data), precipitation (EOBS, ECA&D, TRMM, and GPCP), or evaporation (GLEAM). The verification results showed that the D experiment systematically performs better than N and in many instances, too, better than the forcing reanalysis. According to the results, the leading terms of the water balance are the tendency of the precipitable water, the divergence of moisture flux, evaporation, and precipitation. No spatial patterns were recognizable for the annual accumulated evaporation, but the effect of the Atlantic fronts was detected in the precipitation patterns. The transboundary moisture fluxes through the contour of the Iberian Peninsula behave differently depending on the season during 2010–2014. During winter, they show a net moisture import through the boundaries. During spring, summer, or autumn moisture is exported specially through the Mediterranean coast, and only during midday, this feature is reversed due to sea breezes. [ABSTRACT FROM AUTHOR]

In this paper, we have estimated the spatiotemporal distribution of moisture recycling over the Iberian Peninsula (IP). The recycling ratio was computed from two simulations over the IP using the Weather Research and Forecasting (WRF) model with a horizontal resolution of 15 km spanning the period 2010–2014. The first simulation (WRF N) was nested inside the ERA-Interim with information passed to the domain through the boundaries. The second run (WRF D) is similar to WRF N, but it also includes 3DVAR data assimilation every six hours (12:00 a.m., 6:00 a.m., 12:00 p.m. and 6:00 p.m. UTC). It was also extended until 2018. The lowest values of moisture recycling (3%) are obtained from November to February, while the highest ones (16%) are observed in spring in both simulations. Moisture recycling is confined to the southeastern corner during winter. During spring and summer, a gradient towards the northeastern corner of the IP is observed in both simulations. The differences between both simulations are associated with the dryness of the soil in the model and are higher during summer and autumn. WRF D presents a lower bias and produces more reliable results because of a better representation of the atmospheric moisture. [ABSTRACT FROM AUTHOR]

A constant value of air density based on its annual average value at a given location is commonly used for the computation of the annual energy production in wind industry. Thus, the correction required in the estimation of daily, monthly or seasonal wind energy production, due to the use of air density, is ordinarily omitted in existing literature. The general method, based on the implementation of the wind speed's Weibull distribution over the power curve of the turbine, omits it if the power curve is not corrected according to the air density of the site. In this study, the seasonal variation of air density was shown to be highly relevant for the computation of offshore wind energy potential around the Iberian Peninsula. If the temperature, pressure, and moisture are taken into account, the wind power density and turbine capacity factor corrections derived from these variations are also significant. In order to demonstrate this, the advanced Weather Research and Forecasting mesoscale Model (WRF) using data assimilation was executed in the study area to obtain a spatial representation of these corrections. According to the results, the wind power density, estimated by taking into account the air density correction, exhibits a difference of 8% between summer and winter, compared with that estimated without the density correction. This implies that seasonal capacity factor estimation corrections of up to 1% in percentage points are necessary for wind turbines mainly for summer and winter, due to air density changes. [ABSTRACT FROM AUTHOR]