Climatological Averages Research Articles

Escalated Risk of Concurrent Eurasian Heatwaves Under Recurrent Rossby Wave Patterns

AbstractConcurrent heatwaves, featured by simultaneous occurrences of extreme heat in separate regions, pose significant risks to natural ecosystems and human society. In August 2022, unprecedented heatwaves struck central China and west Russia concurrently, resulting in severe energy crisis, numerous financial losses, and fatalities. Through synoptic‐scale analysis, we identify a pivotal role of recurrent Rossby wave packets (RRWPs) in shaping the event by repeatedly forming ridges and troughs over the same key areas. In this study, we further extend the investigation of the influence of RRWPs on heatwave occurrences in these two targeted regions over the past 44 years. Using a self‐organizing map to identify the RRWP patterns, we find that RRWPs with ridges over central China are closely associated with a marked increase in heatwave probabilities in both central China and western Russia. Specifically, the regional‐mean likelihood of heatwaves increases by factors of 3.0 and 2.6, respectively, relative to the climatology. Notably, the effect of RRWPs on the occurrence of concurrent heatwaves across both regions is more pronounced than on regional events alone. For example, during RRWPs, the likelihood of concurrent heatwaves affecting at least 20% of both target regions exceeds 10 times the climatological average. Our findings underscore the significant role of RRWPs in triggering concurrent Eurasian heatwaves and suggest promising synoptic predictability for such extreme events.

Current-Day and Future Dunkelflaute Risks for Belgium

Abstract Dunkelflautes, events of low renewable production and potentially high demand, are the most extreme examples of the susceptibility of a renewable energy system with respect to weather variability. Yet, these events currently lack a precise definition in the literature. Based on 41 years of weather data, we explore the resilience of the Belgian energy system with respect to the duration and extremeness of Dunkelflautes or energy drought. Although the hourly average load cannot be covered 89% of the time in our analysis, we find that Dunkelflaute occurrences are generally independent of the energy mix between photovoltaic (PV) panels and wind energy (WE) production (for fractions between 10% and 60% of PV). However, to cope with Dunkelflautes, a system with full PV or full WE provision is very ineffective. While the optimal mix is found to be independent of Dunkelflaute extremeness, it strongly depends on the Dunkelflaute duration: For subdaily or longer than two-daily events, a high WE fraction is optimal, while a high PV fraction is needed for durations of the order of days. More than 56% of Dunkelflautes over Belgium coincide with those in the surrounding countries. Finally, as opposed to weak changes in the climatological average, future climate projections reveal an increase in Dunkelflaute severity, mainly due to a reduction in WE production.

Subspace time series clustering of meteocean data to support ocean and coastal hydrodynamic modeling

Subspace time series clustering of meteocean data to support ocean and coastal hydrodynamic modeling

A Multi-Scale Analysis of the Extreme Precipitation in Southern Brazil in April/May 2024

Since 2020, southern Brazil’s Rio Grande do Sul (RS) State has been affected by extreme precipitation episodes caused by different atmospheric systems. However, the most extreme was registered between the end of April and the beginning of May 2024. This extreme precipitation caused floods in most parts of the state, affecting 2,398,255 people and leading to 183 deaths and 27 missing persons. Due to the severity of this episode, we need to understand its drivers. In this context, the main objective of this study is a multi-scale analysis of the extreme precipitation between 26 April and 5 May, i.e., an analysis of the large-scale patterns of the atmosphere, a description of the synoptic environment, and an analysis of the mesoscale viewpoint (cloud-top features and lightning). Data from different sources (reanalysis, satellite, radar, and pluviometers) were used in this study, and different methods were applied. The National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN) registered accumulated rainfall above 400 mm between 26 April and 5 May using 27 pluviometers located in the central-northern part of RS. The monthly volumes reached 667 mm and 803 mm, respectively, for April and May 2024, against a climatological average of 151 mm and 137 mm for these months. The maximum precipitation recorded was 300 mm in a single day on 30 April 2024. From a large-scale point of view, an anomalous heat source in the western Indian Ocean triggered a Rossby wave that contributed to a barotropic anticyclonic anomalous circulation over mid-southeastern Brazil. While the precipitant systems were inhibited over this region (the synoptic view), the anomalous stronger subtropical jet southward of the anticyclonic circulation caused uplift over RS State and, consequently, conditions leading to mesoscale convective system (MCS) development. In addition, the low-level jet east of the Andes transported warm and moist air to southern Brazil, which also interacted with two cold fronts that reached RS during the 10-day period, helping to establish the precipitation. Severe deep MCSs (with a cloud-top temperature lower than −80 °C) were responsible for a high lightning rate (above 10 flashes km−2 in 10 days) and accumulated precipitation (above 600 mm in 10 days), as observed by satellite measurements. This high volume of rainfall caused an increase in soil moisture, which exceeded a volume fraction of 0.55, making water infiltration into the soil difficult and, consequently, favoring flood occurrence.

An Investigation on Causes of the Detected Surface Solar Radiation Brightening in Europe Using Satellite Data

AbstractSurface solar radiation is fundamental for terrestrial life. It provides warmth to make our planet habitable, drives atmospheric circulation, the hydrological cycle and photosynthesis. Europe has experienced an increase in surface solar radiation, termed “brightening,” since the 1980s. This study investigates the causative factors behind this brightening. A novel algorithm from the EUMETSAT satellite application facility on climate monitoring (CM SAF) provides the unique opportunity to simulate surface solar radiation under various atmospheric conditions for clouds (clear‐sky or all‐sky), aerosol optical depth (time‐varying or climatological averages) and water vapor content (with or without its direct influence on surface solar radiation). Through a multiple linear regression approach, the study attributes brightening trends to changes in these atmospheric parameters. Analyzing 61 locations distributed across Europe from 1983 to 2020, aerosols emerge as key driver during 1983–2002, with Southern Europe and high elevations showing subdued effects (0%/decade–1%/decade) versus more pronounced impacts in Northern and Eastern Europe (2%/decade–6%/decade). Cloud effects exhibit spatial variability, inducing a negative effect on surface solar radiation (−3%/decade–−2%/decade) at most investigated locations in the same period. In the period 2001–2020, aerosol effects are much smaller, while cloud effects dominate the observed brightening (2%/decade–5%/decade). This study therefore finds a substantial decrease in the cloud radiative effect over Europe in the first two decades of the 21st century. Water vapor exerts negligible influence in both sub‐periods.

Influence of Marine Heatwaves on Upwelling Parameter in Indonesian Fisheries Management Area (IFMA) 713 Waters

Abstract An upwelling process linked to water productivity is likely to be affected by marine heatwaves (MHWs). The events of MHWs are distinguished by warmer sea surface temperature (SST), leading to increased stratification, causing vertical mixing limits and suppressing the upwelling process. In the Indonesian Fisheries Management Area (IFMA) 713 waters, which have enormous potential for fishery resources, five upwelling areas were identified during the southeast monsoon months (May–October) based on the Ekman pumping velocity (EPV) assessment, namely locations A (western part of West Sulawesi), B (southern Makassar), C (eastern part of Bone Bay), D (southern Banjarmasin), and E (southern Makassar Strait). Thus, this study aimed to investigate the influence of MHWs on upwelling parameter (chlorophyll-a concentration) in IFMA 713, particularly in 1998 and 2016, where prominent MHWs occurred and coincided with upwelling sessions. To accomplish this, EPV anomalies and chlorophyll-a concentrations for each MHWs event during May–October 1998 and 2016 were calculated. The analysis revealed that MHWs events co-occuring with negative (upwelling velocity is weaker than the climatological mean) and positive (upwelling velocity is stronger than the climatological average) EPV anomalies were associated with decreased chlorophyll-a concentrations.

Characterizing oceanographic conditions near Coiba Island and Pacific Panama using 20 years of satellite-based wind stress, SST and chlorophyll-a measurements

Coiba Island and the associated Special Zone of Marine Protection represent an important, yet poorly studied marine reserve along the Pacific coast of Panama. While efforts have recently began to establish monitoring programs in the region, a range of historical, marine-related environmental measurements already exist, derived from satellite-based observations. The goal of this paper was to use long-term datasets for key variables to provide qualitative insights (i.e. descriptive oceanography) of climatological conditions and interannual variability in the Pacific Panama region. These are underpinned with numerical assessments, providing an important baseline for ongoing and future studies, particularly in the Coiba Island/Gulf of Chiriqui region. In particular, we examined 20 years (January 2003-December 2022) of wind stress, sea surface temperature (SST), and chlorophyll-a (Chl-a), spanning the neritic and pelagic regions of the Pacific Panama coast. During the dry season (northern winter), the well-known, seasonal, regional Panama wind jet appeared across the Gulf of Panama, leading to surface mixing and SST cooling that eventually extended across most of the Panama Bight. West of the Azuero Peninsula, SST increased and surface warming extended further offshore from January through April. The SST in the Gulf of Chiriqui during this period was about 1 °C warmer on average than east of Coiba Island. By July and August, offshore SST gradients became largely longitudinal, cooling occured across the season, and the SST on either side of Coiba Island was nearly the same. The influence of the Panama jet in the Gulf of Panama was clear in the Chl-a data as well, with upwelling-driven values peaking in February/March (up to 11 mg m-3, with a monthly climatological value of around 2 mg m-3 during this period). During the rest of the year, the Chl-a concentration in this region averaged around 0.5-1.0 mg m-3. In the Gulf of Chiriqui and the region east of Coiba Island, the climatological monthly averages were roughly 0.3-0.5 mg m-3 and 0.4-0.6 mg m-3, respectively. Somewhat surprisingly, very high Chl-a values were present in the satellite data for the Gulf of Chiriqui during May 2007 and June 2008, peaking at 16 mg m-3 and 32 mg m-3 at a location just west of Coiba Island, respectively. It remains unclear as to the cause of these apparent blooms. Even when the high Chl-a values were excluded in the calculation of climatological averages in the Gulf of Chiriqui, however, there is a suggestion of modest seasonality in Chl-a values, with slightly elevated values (~ 0.4 mg m-3) peaking around May and October. During the extreme El Niño event of 2015-2016, the monthly-averaged SST along the Panama Pacific coast was warmer than average, with elevated levels of up to + 2 °C and lasting 12 months in the Gulf of Chiriqui. In the Gulf of Panama, the monthly-averaged SST anomalies were up to + 1.7 °C, although the temperatures returned to near-seasonal averages after roughly 5 months.

Difference spectrum fitting of the ion–neutral collision frequency from dual-frequency EISCAT measurements

Abstract. The plasma–neutral coupling in the mesosphere–lower thermosphere strongly depends on the ion–neutral collision frequency across that region. Most commonly, the collision frequency profile is calculated from the climatologies of atmospheric models. However, previous measurements indicated that the collision frequency can deviate notably from the climatological average. Direct measurement of the ion–neutral collision frequency with multifrequency incoherent scatter radar (ISR) measurements has been discussed before, though actual measurements have been rare. The previously applied multifrequency analysis method requires a special simultaneous fit of the two incoherent scatter spectra, which is not possible with standard ISR analysis software. The difference spectrum method allows us to infer ion–neutral collision frequency profiles from multifrequency ISR measurements based on standard incoherent scatter analysis software, such as the Grand Unified Incoherent Scatter Design and Analysis Package (GUISDAP) software. In this work, we present the first results by applying the difference spectrum method. Ion–neutral collision frequency profiles obtained from several multifrequency EISCAT ISR campaigns are presented. The profiles obtained with the difference spectrum method are compared to previous collision frequency measurements, both from multifrequency ISR and other measurements, as well as results from empirical and comprehensive atmosphere models. Ion–neutral collision frequency measurements can be applied to improve first-principle ionospheric models.

Observed Warming Trends at U.S. Army Basic Combat Training Installations andImplications forFuture Recruit Training.

Army recruits conducting BCT are among the most susceptible population of military personnel to experience exertional heat illness, a concern expected to become increasingly urgent due to steadily rising temperatures. In this study, we provide an empirical analysis of wet bulb globe temperature (WBGT) index trends at U.S. Army BCT installations and quantify the magnitude of these trends. Assuming these warming trends continue, the anticipated effects of increasing temperature trends are discussed in relation to potential impacts on recruit heat illness incidence and training disruption. We obtained weather data beginning in the early 1960s, including WBGT index measurements derived by the U.S. Air Force 14th Weather Squadron. We apply these datasets to two classifications for high WBGT index days, including one classification accounting for heat illness susceptibility based on prior day heat exposure, to determine when recruits are most at risk of heat illness. The daily likelihood of extreme WBGT index values is described at each installation using a 30-year climatological average. Trends in the WBGT index are evaluated quantitatively during the warm season (May 1-September 30) and full year and compared between decades and by individual BCT classes. Trends in the WBGT index have increased at all four BCT installations. Between January 1960 and October 2022, the mean WBGT index value increased most quickly at Ft Jackson, SC (0.272°C decade-1, CI: 0.255-0.289) and least at Ft Moore, GA (0.190°C decade-1, CI: 0.170-0.210). Ft Moore experiences the greatest heat burden, with the daily likelihood of experiencing a "black flag" event (≥90°F WBGT index) peaking at nearly 50% in late July, while Ft Leonard Wood, MO, experiences the least heat burden. This heat burden is spread unevenly across installations and dependent on BCT class start date. Recruits beginning in mid-June will experience approximately 200 hours of hazardous heat during BCT at Ft Moore, GA; 100 hours at Ft Jackson, SC; 80 hours at Ft Sill, OK; and 61 hours at Ft Leonard Wood, MO. Temperatures measured on the WBGT index have steadily increased at US Army basic training installations since at least 1960. In the future, adaptation to the BCT program will be required to maintain rigorous standards without incurring unacceptable risk of recruit heat illness. The analysis provided by this study can help inform medical, training, and policy implementations needed to ensure continued BCT in a warming world.

Oceanic and ecological response to native Typhoons Cempaka and Lupit (2021) along the northern South China Sea continental shelf: comparison and evaluation of global and regional Operational Oceanography Forecasting Systems

The Global Operational Oceanography Forecasting System from the Mercator Ocean (MO) and the regional South China Sea Operational Oceanography Forecasting System (SCSOFSv2) were compared and evaluated using in situ and satellite observations, with a focus on the oceanic and ecological response to two consecutive native typhoons, Cempaka and Lupit, that occurred in July–August 2021. Results revealed a better simulation of the chlorophyll a (Chla) structure by SCSOFSv2 and a better simulation of the temperature profile by MO in the Pearl River Estuary. In addition, SCSOFSv2 sea surface temperature (SST) and MO Chla variations corresponded well with observations along the northern SCS shelf. Simulated maximum SST cooling was larger and 2–3 days earlier than those observations. Maximum Chla was stronger and led the climatological average by 2 days after the typhoon passage. Typhoon-induced vertical variations of Chla and NO3 indicated that different Chla bloom processes from coastal waters to the continental shelf. Discharge brought extra nutrients to stimulate Chla bloom in coastal waters, and model results revealed that its impact could extend to the continental shelf 50–150 km from the coastline. However, bottom nutrients were uplifted to contribute to Chla enhancement in the upper and middle layers of the shelf. Nutrients transported from the open sea along the continental slope with the bottom cold water could trigger Chla enhancement in the Taiwan Bank. This study suggests considering strong tides and waves as well as regional dynamics to improve model skills in the future.

Coupling habitat-specific temperature scenarios with tolerance landscape to predict the impacts of climate change on farmed bivalves

Due to climate change, heatwaves are likely to become more frequent, prolonged and characterized by higher peak values, compared with climatological averages. However, the thermal tolerance of organisms depends on the actual exposure, which can be modulated by environmental context and microhabitat characteristics. This study investigated the frequency of occurrence of mass mortality events in the next decades for two species of farmed bivalves, the mussel Mytilus galloprovincialis and the clam Ruditapes philippinarum, in a shallow coastal lagoon, characterised by marked diurnal oscillations of water temperature. The effect of heatwaves was estimated by means of tolerance landscape models, which predict the occurrence of 50% mortality based on the exposure intensity and duration. Scenarios of water temperature up to the year 2100 were modelled by combining two mechanistic components, namely: 1) monthly mean water temperatures, simulated using a hydrodynamic model including the heat budget; 2) daily oscillations, estimated from the harmonic analysis of a twenty year-long site-specific time series of water temperature. Scenarios of mean daily sediment temperature were estimated by means of a cross-correlation model, using as input the water temperature one: the model parameters were estimated based on a comprehensive set of site-specific water and sediment temperature observations. The results indicate that for both species the risk of mass mortality rapidly increases starting from the 2060s. Furthermore, the daily patterns of water temperature seemed to be relevant, as overnight it falls below the predicted mortality thresholds for a few hours. These findings suggest that further studies should address: 1) the improvement of tolerance landscape models, in order to take into account the integrated effect of repeated non-lethal stress events on mortality rate; 2) the prediction of environmental temperature in specific habitat, by means of both process-based and data driven models.

Evaluation of the Rio de Janeiro State Flood Warning System: A Case Study for the Hydrographic Region of the Médio Paraíba do Sul (RJ), Brazil

This work is a case study for the Hydrographic Region of the Médio Paraíba do Sul located in the State of Rio de Janeiro (Brazil), which has natural disasters, such as floods, as the most recurrent. Due to the social, economic and environmental impacts that these disasters cause, this research aim to analyze the history of alerts issued by the Flood Warning System (FWS) and to assess its efficiency. Through the retro-analysis of Alert Trigger Events (ATE) that occurred in the monitored rivers, investigations were also carried out in the hydrological and meteorological scope. The results identified that the Attention stage belonging to the operational protocol significantly reduces the efficiency of the FWS about sending alerts, causing high false alarm rate. Regarding the influence that variation in the type of data transmission has on the Operational Protocol in RH-III, it is considered that the time interval between data transmission and their availability on the State Environmental Institute (SEI) erver generates a false alarm, however the FWS is still efficient. Barra Mansa River is the only one that has flood events, which raises questions about the representativeness of the other stations spread across RH-III. In addition, 60% of alerts issued for RH-III were related to episodes of Cold Fronts and South Atlantic Convergence Zones. These information helps improve the description of the flood events in the region. Rainfall Anomaly Index (RAI) identified periods that the rainfall rate was below the climatological average, indicating a drier environment, but with alerts issued.

PENINGKATAN JARINGAN IRIGASI DAERAH IRIGASI KRIPIK KOTA SEMARANG

Water is a very basic need for All living things on this earth without exception. The Kripik River is currently not being utilized by community optimally. For this reason, it is necessary to increase Irrigation Area which aims to increase agricultural production. Currently, the Bendung Kripik area serves an irrigation area of 95,94 Ha with the height of the exsisting weir lighthouse is 1 m.The improvement of the irrigation network is carried out based on secondary data with other supporting data. The analysis carried out includes: hydrological analysis which includes : watershed, maximum rainfall, flood discharge, average rainfall, average climatology, Eto – Penman, F.J Mock mainstay discharge, planting patterns then carried out channel design, weir design and calculation of weir stability.In this study, it was proposed to increase the rice field area by 130.17 Ha From the results of hydrological analysis, the flood discharge value of the Nakayasu Synthetic Unit Hydrograph (HSS) method was obtained when repeating 100 th by 40.10 m3 / s. The water requirement for irrigation of D.I Kripik after the addition of the area is 0.086 m3 / s. As for the bed, a weir width of 30 m with a weir height of 1.05 m is required, a weir lighthouse width of 28 m, a rinse width of 1 m and a pillar width of 1 m (2 Pillars of 1 m each). The radius of the spherical threshold type weir is 0.5 m, the olak pond type is USBR Type IV with a length of 3.50m. Keywords: Irrigation, Weir, Flood Discharge

Causes for an extreme cold condition over Northeast Asia during April 2020

Although 2020 was the fourth warmest year on record in northern Asia, the cold condition in April 2020 caused severe damage to the agricultural and marine ecosystems in northeastern Asia. Previous studies have indicated that the dipole atmospheric circulation over Siberia and the East Sea (Japan Sea) produced this cold environment with strong northwesterly wind. However, the potential causes of the dipole circulation over northeastern Asia remain unclear. In this study, we found that the East Atlantic/Western Russia (EAWR) pattern and blocking combined to produce the atmospheric structure. The wave train originated from the vorticity forcing of northwestern/central Russia and propagated from Western Europe to the East Sea via the background westerly and northerly winds. In addition, the Siberian blocking days increased eleven times in April 2020 relative to the climatological average, and an easterly (westerly) anomaly was observed over Mongolia–northeastern China (northern Russia). The strong blocking and EAWR pattern led to the robust atmospheric dipole structure with a prevailing northerly flow in April 2020, thereby causing the extreme cold condition over northeastern Asia. Our results provide novel insights into the cause of extreme cold condition in April over northeastern Asia.

Assessment of the Record-Breaking 2020 Rainfall in Guinea-Bissau and Impacts of Associated Floods

The impacts of Climate Change are quite visible in Guinea-Bissau. Greater irregularity at the beginning and end of the rainy season, as well as in relation to the interannual variability of precipitation, are evidence that shows these phenomena in West African countries and particularly in Guinea-Bissau, where the agriculture is rain-fed. The year 2020 was characterized as very rainy in comparison to the climatological average of 1981–2020, with positive anomalies throughout the country, despite the late arrival of the wet season, which usually occurs in May. July, August, and September 2020 were the rainiest months, registering above a normal frequency of days with precipitation greater than 50 mm. Bissau, the capital, registered a record-breaking annual rainfall and monthly amounts higher than the 90th and 95th percentiles in July and August, respectively. This heavy rain accompanied by strong winds caused flooding in several urban areas and agricultural fields, and the destruction of roads, houses, and infrastructures in different cities across the country. As a way of mitigating these impacts, the government, through the Ministry of Solidarity, made available 100 million CFA francs (6.5 million euros) to help families that were victims of the floods.

Identifying climate models based on their daily output using machine learning

AbstractClimate models are primary tools for investigating processes in the climate system, projecting future changes, and informing decision makers. The latest generation of models provides increasingly complex and realistic representations of the real climate system, while there is also growing awareness that not all models produce equally plausible or independent simulations. Therefore, many recent studies have investigated how models differ from observed climate and how model dependence affects model output similarity, typically drawing on climatological averages over several decades. Here, we show that temperature maps of individual days drawn from datasets never used in training can be robustly identified as “model” or “observation” using the CMIP6 model archive and four observational products. An important exception is a prototype storm-resolving simulation from ICON-Sapphire which cannot be unambiguously assigned to either category. These results highlight that persistent differences between simulated and observed climate emerge at short timescales already, but very high-resolution modeling efforts may be able to overcome some of these shortcomings. Moreover, temporally out-of-sample test days can be assigned their dataset name with up to 83% accuracy. Misclassifications occur mostly between models developed at the same institution, suggesting that effects of shared code, previously documented only for climatological timescales, already emerge at the level of individual days. Our results thus demonstrate that the use of machine learning classifiers, once trained, can overcome the need for several decades of data to evaluate a given model. This opens up new avenues to test model performance and independence on much shorter timescales.

Validation and Comparison of Climate Reanalysis Data in the East Asian Monsoon Region

Understanding East Asian monsoon (EAM) has been a crucial issue due to its socio-economic effects on one-fifth of the world’s population and its interactions with the global climate system. However, the reliabilities of climate reanalysis data are still uncertain at varying temporal and spatial scales. In this study, we examined the correlations and differences for climate reanalyses with weather observations and suggested the best climate reanalysis for the EAM region. The three reanalyses of ERA5, JRA55, and NCEP2 along with a gridded observation (CRU) were evaluated using the correlation coefficients (Pearson, Spearman, and Kendall), difference statistics (RMSE and bias), and Taylor diagrams, comparing their annual and seasonal temperatures and precipitations with those from the total of 537 weather stations across China, North Korea, South Korea, and Japan. We found that ERA5 showed the best performance in reproducing temporal variations in temperature with the highest correlations in annual, summer, and autumn, and the smallest RMSEs and biases for all seasons and annually. For precipitation, among the three reanalysis datasets, ERA5 had the highest correlations, annually and in four seasons, with the smallest RMSEs, annually and in spring, summer and autumn, and the smallest biases, annually and in summer and autumn. Regarding spatial variations, ERA5 was also the most suitable reanalysis data in representing the annual and seasonal climatological averages.

What causes a heavy precipitation period to become extreme? The exceptional October of 2018 in the Western Mediterranean

The Mediterranean region is particularly exposed to heavy precipitation and flash flooding. Every autumn the region is affected by these weather-related hazards, frequently with immense costly and deadly consequences. What makes an already potentially damaging period in terms of heavy precipitation, even more intense? This is the underlying question in this study, in which the atmosphere and the ocean conditions in October 2018 are examined to identify anomalies favoring this intensification. Furthermore, the model representativity of the over-averaged precipitation period and underlying anomalies is analyzed across scales using climatological, seasonal, and event-based COSMO high-resolution model simulations. Our investigation shows that October-2018, in the context of the climatological series from 1982 to 2018, could be marked as an unprecedented period because of the presence of intense and numerous low-pressure systems. Additionally, atmospheric moisture values placed this time above the climatological average, mainly for the high percentiles of the TCWV hourly anomalies. Specific humidity showed similar behaviour as TCWV except for pressure levels lower than 700 hPa, probably in relation to the evolution of the former Hurricane Leslie. The atmosphere-ocean interaction presented combined strong sea surface temperature (SST) and evaporation anomalies. April to October SST clearly exceeds climatological values while October-2018 presents both strong monthly anomaly and intense evaporation peaks preceding the most intense precipitation events. These large-scale features’ anomalies were in general well captured by the high-resolution regional climate model simulations at climatic and seasonal scales leading to an accurate representation of accumulated precipitation for the October period. However, the numerical weather prediction simulations on an event scale revealed low predictability, in agreement with former investigations, due to differences at the location and intensity of the cut-off lows and particularly at the atmospheric moisture field. The conclusions of this study show that it is not the most extreme period in terms of single anomalies which lead to extreme wet seasons, but the synergy of atmospheric and oceanic anomaly conditions with a constant interplay which made Autumn/October 2018 an extreme season/month.

Using Principal Component Analysis of Satellite and Ground Magnetic Data to Model the Equatorial Electrojet and Derive Its Tidal Composition

AbstractThe intensity of the equatorial electrojet (EEJ) shows temporal and spatial variability that is not yet fully understood nor accurately modeled. Atmospheric solar tides are among the main drivers of this variability but determining different tidal components and their respective time series is challenging. It requires good temporal and spatial coverage with observations, which, previously could only be achieved by accumulating data over many years. Here, we propose a new technique for modeling the EEJ based on principal component analysis (PCA) of a hybrid ground‐satellite geomagnetic data set. The proposed PCA‐based model (PCEEJ) represents the observed EEJ better than the climatological EEJM‐2 model, especially when there is good local time separation among the satellites involved. The amplitudes of various solar tidal modes are determined from PCEEJ based tidal equation fitting. This allows to evaluate interannual and intraannual changes of solar tidal signatures in the EEJ. On average, the obtained time series of migrating and nonmigrating tides agree with the average climatology available from earlier work. A comparison of tidal signatures in the EEJ with tides derived from neutral atmosphere temperature observations show a remarkable correlation for nonmigrating tides such as DE3, DE2, DE4, and SW4. The results indicate that it is possible to obtain a meaningful EEJ spectrum related to solar tides for a relatively short time interval of 70 days.

Evaluating the impact of a time-evolving constellation on multi-platform satellite based daily precipitation estimates

Satellite based precipitation climate data records (CDRs) have recently emerged and provide new observational sources to characterize of the changing nature of global precipitation. These products rely on the use of passive microwave instruments. At the daily scale, these CDRs are prone to performance sensitivity resulting from the availability of microwave observations. As the configuration of the microwave sounders and imagers fleet evolves over time, adding new satellites and instruments or losing old platforms, the climate-oriented performances of the CDRs are likely impacted. In this study, this effect is quantified using a prototype constellation-based quasi-global precipitation product algorithm and data-denial experiments. The constellation change has a small impact of the long-term average climatology both in terms of mean and distribution recalling the resilience of the climatology of such a multi-platform product to the fluctuations of the amount of available input data. The interannual variability on the other hand is more impacted. More large rainfall amounts are relatively more perturbed than the lower rain daily accumulation with anomalies up to 30% for some configurations. The method to correct for the artefact is detailed and while some aspects of the computations are product-specific, the major outcome of this study should apply to various similar products as well. • The impact of the changing passive microwave constellation configuration on the daily precipitation estimate is explored through a time-dependent error model based on a series of data denial experiments. • The multiplatform approach shows strong resilience to the constellation configuration for the climatology • The impact of the constellation change is significant in terms of interannual variability especially at the higher end of the precipitation intensity range