Large-scale Atmospheric Patterns Research Articles

Improving seasonal predictions of German Bight storm activity

Abstract. Extratropical storms are one of the major coastal hazards along the coastline of the German Bight, the southeastern part of the North Sea, and a major driver of coastal protection efforts. However, the predictability of these regional extreme events on a seasonal scale is still limited. We therefore improve the seasonal prediction skill of the Max Planck Institute Earth System Model (MPI-ESM) large-ensemble decadal hindcast system for German Bight storm activity (GBSA) in winter. We define GBSA as the 95th percentiles of three-hourly geostrophic wind speeds in winter, which we derive from mean sea-level pressure (MSLP) data. The hindcast system consists of an ensemble of 64 members, which are initialized annually in November and cover the winters of 1960/61–2017/18. We consider both deterministic and probabilistic predictions of GBSA, for both of which the full ensemble produces poor predictions in the first winter. To improve the skill, we observe the state of two physical predictors of GBSA, namely 70 hPa temperature anomalies in September, as well as 500 hPa geopotential height anomalies in November, in areas where these two predictors are correlated with winter GBSA. We translate the state of these predictors into a first guess of GBSA and remove ensemble members with a GBSA prediction too far away from this first guess. The resulting subselected ensemble exhibits a significantly improved skill in both deterministic and probabilistic predictions of winter GBSA. We also show how this skill increase is associated with better predictability of large-scale atmospheric patterns.

Large-scale Atmospheric Patterns associated with the 2018 Heatwave Prediction in the Korea-Japan Region using GloSea6

Large-scale Atmospheric Patterns associated with the 2018 Heatwave Prediction in the Korea-Japan Region using GloSea6

Arctic/North Atlantic atmospheric variability causes Severe PM10 events in South Korea

Severe PM10 (particulate matter with a diameter of <10 μm) events in South Korea are known to be caused by stable atmospheric circulation conditions related to high-pressure anomalies in the upper troposphere. However, research on why these atmospheric circulation patterns occur is unknown. In this study, we propose new large-scale teleconnection pathways that cause severe PM10 events during the midwinter in South Korea. This study investigated instances of extremely high (EH)-PM10 in South Korea during mid-winter and examined the corresponding atmospheric teleconnection patterns to identify the factors contributing to EH-PM10 events. K-means clustering analysis revealed that EH-PM10 instances were associated with two large-scale teleconnection patterns. Cluster 1 exhibited a wave train pattern originating in the North Atlantic that developed from Eurasia to the Korean Peninsula. Cluster 2 was associated with a wave-like teleconnection pattern from the Barents-Kara Sea to the Korean Peninsula. The Rossby waves, triggered by the North Atlantic and the Arctic, propagated and weakened the surface pressure system. This led to a high-pressure anomaly over the Korean Peninsula, reducing atmospheric ventilation and causing a rapid increase in PM10 concentration within a few days. Furthermore, an experiment involving a linear baroclinic model established that atmospheric forcing in upstream regions has the potential to induce large-scale atmospheric teleconnection patterns, resulting in EH-PM10 cases in South Korea. These findings emphasize the ventilation effect and transport of PM10 concentrations modulated by two large-scale teleconnection patterns originating from the Arctic and North Atlantic, leading to EH-PM10 events in South Korea. Understanding this combined phenomenon may assist in the implementation of emission reduction measures based on the results of short-term forecasts of severe PM10 events.

Karadeniz'de 2000-2022 Yılları Arasında Yüzey Sıcaklığının Mekansal ve Zamansal Değişimi

This study presents a comprehensive assessment of the spatio-temporal variability of Sea Surface Temperature (SST) in the Black Sea at monthly to interannual scales, with a focus on understanding its connection to major large-scale atmospheric forcing during the period 2000-2022. Monthly variations of SST in the Black Sea reveal distinct seasonal patterns. The study evaluates the potential impacts of large-scale atmospheric patterns on interannual SST variations using climate indices such as the North Atlantic Oscillation (NAO), East Atlantic/West Russia (EA-WR) and El Niño-Southern Oscillation (ENSO) during the winter months. The results indicated that these large-scale atmospheric oscillations played a substantial role in influencing SST anomalies, with the NAO and EA-WR indices particularly affecting the Black Sea's SST anomalies. The NAO index exhibited negative values during warm winters and positive values during cold winters, with extreme cold and warm winters corresponding to specific years, as observed in 2003, 2006, 2012, 2017 (cold) and 2018, 2020, 2021 (warm). Notably, the relationship between NAO and SST anomalies was not as dominant during 2000-2022. This difference might be explained by the combined influence of NAO and ENSO, which is beyond the scope of this study. The EA-WR pattern was identified as another significant large-scale atmospheric dynamic affecting the Black Sea's SST. Although it explains the cold SST anomalies in certain years, it cannot account for extreme warm SST years. While the influence of ENSO remains somewhat inconclusive for the extreme warm period, the SST pattern between 2016-2022 aligns closely with El Niño events, particularly in 2018 and 2021 when positive SOI index values coincide with warm SST years in the Black Sea.

The extraordinary atmospheric rivers analysis over the Middle East: Large-scale drivers, structure, effective sources, and precipitation characterization

The study investigates the characteristics of Extraordinary Atmospheric Rivers (EARs), including large-scale atmospheric patterns, structure, effective sources, and precipitation in the Middle East. For this purpose, ARs with maximum Vertically Integrated Water Vapor Transport (IVT) ≥ 1000 kg m−1 s−1 were extracted from 1981 to 2020. ERA5 and PERSIANN-CCS-CDR data were used to analyze the characteristics of the EARs. The latter was applied to show the precipitation risk level. Atmospheric patterns indicated the state of the merging cyclones. Sudan's low pressure was the more recurrent system in all the patterns and alternately integrated with one or two cyclonic tongues over the Mediterranean, the Black Sea, the Caspian Sea, and then the polar vortex. Atmospheric blocking was present in all events, affecting the lifetime of the EARs and the maximum IVT anomaly, which averaged 4.5 days and 622 kg m−1 s−1, respectively. The EARs are the result of the simultaneous feeding of several moisture pathways from different sources. Both regional (mainly below 850 hPa) and trans-regional (above 700 hPa, except for the western Mediterranean) water sources played a crucial role in their formation. Dynamically, most events were characterized by merging the subtropical and polar jets, with maximum central speeds between 70 and 85 m s−1. EARs were also accompanied by strong near surface wind gusts up to 28 m s−1. In the IVT core wind structure, the low-level jet with a speed of 30 m s−1 deepened to a maximum of 925 hPa. There were also intense upward velocities between − 3 and − 5 Pa s−1 near the precipitation maxima areas in the EARs. The spatial character of the precipitation was of a continuous or intermittent nature, and a considerable part of it fell in short periods (up to 286 mm in 3 h). The daily maximum was 390 mm. Accordingly, the importance of using high-resolution data was represented for such events with devastating hydrological effects.

Recent European drying and its link to prevailing large-scale atmospheric patterns

The extreme 2018 and 2022 droughts pose as recent examples of a series of drought events that have hit Europe over the last decades with wide ranging social, environmental and economic impacts. Although the link between atmospheric circulation and meteorological drought is clear and often highlighted during major drought events, there is a lack of in-depth studies linking historical changes in meteorological drought indices and prevailing large-scale atmospheric patterns in Europe. To meet this shortfall, we investigated the relation between changes in large-scale atmospheric patterns and meteorological drought, as indicated by the geopotential height at 500mb (Z500) and the Standardised Precipitation-Evapotranspiration Index (SPEI), respectively. Calculations were done separately for four climate regions (North, West, Central-East and Mediterranean) over the growing season (March–September). Coherent patterns of significant changes towards higher pressure (increasing Z500) and drier conditions (decreasing SPEI) over 1979–2021 are found over West in spring and Central-East in summer. Z500 and SPEI are strongly linked, reflected by both significant (1979–2021) correlations and high co-occurrences (69-96%) between meteorological drought and high-pressure anomaly occurrences since 1900. North shows the most heterogeneous trend patterns and weakest links, but constitutes a hotspot of significantly increasing Z500 in September. Finally, we performed an ensemble-based, European wide analysis of future Z500, based on CMIP6 low-end (SSP126) and high-end (SSP585) 21st century emission scenarios. According to the projected changes, anomalously high-pressure systems will be the new normal regardless of scenario, and well exceeding the 2018 and 2022 levels in the case of the high-end emission scenario. However, due to the limitations of the model ensemble to represent the spatial heterogeneity in historical Z500 variability and trends (1979–2014), projected changes in large-scale circulation, and associated meteorological droughts, are highly uncertain. This paper provides new insight into significant trends in atmospheric circulation over Europe, their strong links to the observed drying trends, and the inability of a CMIP6 ensemble to reproduce the spatial heterogeneity of the circulation changes.

Identifying atmospheric processes favouring the formation of bubble-free layers in the Law Dome ice core, East Antarctica

Abstract. Physical features preserved in ice cores may provide unique records about past atmospheric variability. Linking the formation and preservation of these features and the atmospheric processes causing them is key to their interpretation as palaeoclimate proxies. We imaged ice cores from Law Dome, East Antarctica, using an intermediate layer core scanner (ILCS) and found that thin bubble-free layers (BFLs) occur multiple times per year at this site. The origin of these features is unknown. We used a previously developed age–depth scale in conjunction with regional accumulation estimated from atmospheric reanalysis data (ERA5) to estimate the year and month that the BFLs occurred, and then we performed seasonal and annual analysis to reduce the overall dating errors. We then investigated measurements of snow surface height from a co-located automatic weather station to determine snow surface features co-occurring with BFLs, as well as their estimated occurrence date. We also used ERA5 to investigate potentially relevant local/regional atmospheric processes (temperature inversions, wind scour, accumulation hiatuses and extreme precipitation) associated with BFL occurrence. Finally, we used a synoptic typing dataset of the southern Indian and southwest Pacific oceans to investigate the relationship between large-scale atmospheric patterns and BFL occurrence. Our results show that BFLs occur (1) primarily in autumn and winter, (2) in conjunction with accumulation hiatuses &gt; 4 d, and (3) during synoptic patterns characterised by meridional atmospheric flow related to the episodic blocking and channelling of maritime moisture to the ice core site. Thus, BFLs may act as a seasonal marker (autumn/winter) and may indicate episodic changes in accumulation (such as hiatuses) associated with large-scale circulation. This study provides a pathway to the development of a new proxy for past climate in the Law Dome ice cores, specifically past snowfall conditions relating to synoptic variability over the southern Indian Ocean.

Spatiotemporal variation of snow cover days and influencing factors on the Loess Plateau during 2000–2019

Identifying the large-scale teleconnection is of great practical significance to comprehend the formation mechanism of snow cover days (SCDs) anomalies and improve the disaster risk management of ecological construction on the Loess Plateau. Based on Terra Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover days datasets from 2000 to 2020, we analyzed the spatiotemporal variation of SCDs on the Loess Plateau. Meanwhile, we identified the variability in large-scale atmospheric patterns associated with SCDs on the Loess Plateau. The results show that: in the past 20 years have revealed an interdecadal fluctuations of SCDs on the Loess Plateau. Spatially, the SCDs experienced a significant decreasing trend (p < 0.05) in only 13.1% of the regions, which were distributed in the Northern Earth-Rock Mountain and Hully and Gully Loess Region. The effects of climate-related factor variations on SCD dynamics showed obvious spatial and monthly differences. Specifically, SCDs dominated by precipitation were significantly positively correlated in the north-eastern part of the Loess Plateau. Temperature plays a decisive role in SCD dynamics in the southern part of the Loess Plateau. Among the influencing factors, the East Asian Trough Intensity (EATI), the Asian Polar Vortex Area index (APVA) and the sea surface temperature (SST) anomalies in the NINO W region are three effective indictors of SCDs anomaly. In January, with the deepening of East Asian Trough and low SST in the NINO W region (0°∼10°N, 140°E∼180°E), East Asian winter monsoon is relatively strengthening, temperature is lower on the Loess Plateau, leading to more SCDs in the southern study region. The bigger APVA, corresponding to more snow accumulation in December, could effectively prolong the SCDs dominated by precipitation on the Loess Plateau. The conclusions of this study have implications for excavating early warning information of snow cover anomalies on the Loess Plateau.

Statistical and Spatial Analysis of Extreme Temperatures in Libya (1961-2020)

The present paper seeks to examine spatial and temporal variability of changes in both warm and cold extreme temperatures. Additionally, it aims to determine the most extreme geographical areas in extreme temperatures based on climate data recorded during the period from 1961 to 2020. This paper analysed the annual data of extreme temperatures for 16 meteorological stations during 1961- 2020 across Libya over two climatic regions (coastal and inland) based on Mann–Kendall test. Time trend of the extreme of both maximum and minimum temperature will be analyses and the results from geographical analysis are will also be presented based on ArcMap. The results indicated that a significant increase in the coldest extreme (TNn) temperature are found in autumn and spring and winter at majority of stations. Temperature variability can be associated with variations in large-scale atmospheric patterns represented by Eastern Atlantic and the Western Mediterranean Oscillations.

Impacts of stronger winds and less sea ice on Canadian Beaufort Sea shelf ecosystems since the late 1990s

Continuous and multi-decadal records of faunal abundance and diversity helping to identify the impacts of ongoing global warming on aquatic ecosystems are rare in the coastal Arctic. Here, we used a 50-year-long microfaunal record from a sediment core collected in the Herschel Basin (YC18-HB-GC01; 18 m water depth) to document some aspects of the environmental responses of the southern Canadian coastal Beaufort Sea to climate change. The microfaunal indicators include benthic foraminiferal assemblages, ostracods and tintinnids. The carbonate shells of two foraminiferal species were also analyzed for their stable isotope signatures (δ13C and δ18O). We compiled environmental parameters from 1970 to 2019 for the coastal region, including sea ice data (break-up date, freeze-up date, open season length and mean summer concentration), the wind regime (mean speed, direction of strong winds and the number of storms), hydrological data (freshet date, freshet discharge and mean summer discharge of the Firth and the Mackenzie rivers), and air temperature. Large-scale atmospheric patterns were also taken into consideration. Time-constrained hierarchal clustering analysis of foraminiferal assemblages and environmental parameters revealed a near-synchronous shift around the late 1990s. The microfaunal shift corresponds to an increased abundance of taxa tolerant to variable salinity, turbulent bottom water conditions, and turbid waters towards the present. The same time interval is marked by stronger easterly winds, more frequent storms, reduced sea-ice cover, and a pervasive anticyclonic circulation in the Arctic Ocean (positive Arctic Ocean Oscillation; AOO+). Deeper vertical mixing in the water column in response to intensified winds was fostered by increased open surface waters in summer leading to turbulence, increased particle loading and less saline bottom waters at the study site. Stronger easterly winds probably also resulted in enhanced resuspension events and coastal erosion in addition to a westward spreading of the Mackenzie River plume, altogether contributing to high particulate-matter transport. Increase food availability since ∼2000 was probably linked to enhanced degradation of terrestrial organic carbon, which also implies higher oxygen consumption. The sensitivity of microfaunal communities to environmental variations allowed capturing consequences of climate change on a marine Arctic shelf ecosystem over the last 50 years.

A DEEP LEARNING MODEL TO PREDICT SHORELINE CHANGE

As coastal population increases, so does the risk for social and economic losses under a changing climate. To assess future changes, much progress has been made towards developing shoreline numerical models, although producing reliable shoreline change predictions remains a challenge (Montaño 2020). Here we present a Deep Learning (DL) model to predict long-term shoreline evolution due to waves and large-scale atmospheric patterns. The model is based on two types of Artificial Neural Networks: Long-Short Term Memory (LSTM) networks and Convolutional Neural Networks (CNN).

Do heat waves worsen air quality? A 21-year observational study in Seoul, South Korea

Heat waves are generally known to deteriorate air quality. However, the impacts of heat waves on air quality can substantially vary depending on the characteristics of heat waves. In this study, we examine air quality changes in Seoul during heat waves and their associations with large-scale atmospheric patterns. For this, air quality data from 25 stations and meteorological data from 23 weather stations and reanalysis datasets during July and August of 2001–2021 are used. Under heat waves, the mean daily PM10, NO2, and CO concentrations decrease by 7.9 %, 6.1 %, and 4.6 %, respectively, whereas the mean daily PM2.5, O3, and SO2 concentrations increase by 4.1 %, 17.2 %, and 2.9 %, respectively. The atmospheric circulation under heat waves is less favorable for long-range transport of air pollutants to Seoul. The PM2.5/PM10 ratio increases under heat waves, indicating that the secondary formation of aerosols becomes more important under heat waves. 37 % of the heat wave days are accompanied by severe O3 pollution exceeding the O3 concentration standard in South Korea. There is a significant variability of air quality in Seoul within heat waves. The heat wave days with higher concentrations of PM2.5, PM10, O3, NO2, and CO than their non-heat wave means exhibit a prominent difference in large-scale atmospheric pattern from the heat wave days with lower concentrations. This difference is characterized by a zonal wave-like pattern of geopotential height, which is similar to the circumglobal teleconnection pattern known as one of the major patterns for heat waves in South Korea. This zonal wave-like pattern produces more stagnant conditions over Seoul.

Subseasonal Representation and Predictability of North American Weather Regimes Using Cluster Analysis

Abstract This study focuses on assessing the representation and predictability of North American weather regimes, which are persistent large-scale atmospheric patterns, in a set of initialized subseasonal reforecasts created using the Community Earth System Model, version 2 (CESM2). The k-means clustering was used to extract four key North American (10°–70°N, 150°–40°W) weather regimes within ERA5 reanalysis, which were used to interpret CESM2 subseasonal forecast performance. Results show that CESM2 can recreate the climatology of the four main North American weather regimes with skill but exhibits biases during later lead times with overoccurrence of the West Coast high regime and underoccurrence of the Greenland high and Alaskan ridge regimes. Overall, the West Coast high and Pacific trough regimes exhibited higher predictability within CESM2, partly related to El Niño. Despite biases, several reforecasts were skillful and exhibited high predictability during later lead times, which could be partly attributed to skillful representation of the atmosphere from the tropics to extratropics upstream of North America. The high predictability at the subseasonal time scale of these case-study examples was manifested as an “ensemble realignment,” in which most ensemble members agreed on a prediction despite ensemble trajectory dispersion during earlier lead times. Weather regimes were also shown to project distinct temperature and precipitation anomalies across North America that largely agree with observational products. This study further demonstrates that unsupervised learning methods can be used to uncover sources and limits of subseasonal predictability, along with systematic biases present in numerical prediction systems. Significance Statement North American weather regimes are large-scale atmospheric patterns that can persist for several days. Their skillful subseasonal (2 weeks or greater) prediction can provide valuable lead time to prepare for temperature and precipitation anomalies that can stress energy and water resources. The purpose of this study was to assess the climatological representation and subseasonal predictability of four key North American weather regimes using a research subseasonal prediction system and clustering analysis. We found that the Pacific trough and West Coast high regimes exhibited higher predictability than other regimes and that skillful representation of conditions across the tropics and extratropics can increase predictability during later lead times. Future work will quantify causal pathways associated with high predictability.

On the role of local and large-scale atmospheric variability in snow cover duration: a case study of Montevergine Observatory (Southern Italy)

Snow cover plays an important role in Earth’s climate, hydrological and biological systems as well as in socio-economical dynamics, especially in mountain regions. The objective of this work is to provide the first evidence about snow cover variability in the Italian Southern Apennines and investigate the forcing mechanisms controlling it. To this purpose, we present a new historical long-term (from 1931 to 2008) series of snow cover duration data observed at Montevergine Observatory, a mountainous site located at 1280 m above sea level. From the analysis of this series, it emerged a strong interannual variability, an overall reduction over time of snow cover days until mid-1990s and a recovery in the last 10-years. We model snow cover duration employing a multiple linear regression, considering both local and large-scale climate factors as explanatory variables. Our findings show that snow cover duration appears to be primarily dependent on temperature, which exhibits a positive trend in the considered time interval. However, the interannual and decadal fluctuations of the examined parameter are also strongly modulated by two large-scale patterns, the Arctic Oscillation and the Eastern Mediterranean Pattern. In the last segment of the considered time interval, the increase in temperature is not consistent with the dominant patterns of large-scale indices, which proved to be more effective in capturing the recent rebound in snow cover duration. The results demonstrate that snow cover duration is linked to the global warming by a non-trivial relationship and that its behaviour, in specific periods, can be largely independent from rising temperature tendency, according to the prevailing phase of large-scale atmospheric patterns.

Long-term trend of snow water equivalent in the Italian Alps

Snow stores a significant amount of water in mountain regions. The decrease of water storage in the snowpack can have relevant impacts on water supply for mountain and lowland areas that rely on snow melting. In this work, we modelled the Snow Water Equivalent (SWE) using daily snow depth (HS) data obtained from 19 historical HS measurement stations located in the southern European Alps (Italy). Then, we analysed the long-term (1930–2020) variability of the monthly Standardised SWE Index (SSWEI) and its links with climate change and large-scale atmospheric forcings (teleconnection indices). We found a marked variability in monthly SSWEI, with the lowermost values generally occurring in the last few decades (1991–2020), irrespective of elevation. In this recent period, highly negative values occurred at the snow season tails, mostly in spring. We found large-scale atmospheric patterns (North Atlantic Oscillation, Atlantic Multi-decadal Oscillation, and Artic Oscillation) and precipitation to be interconnected with SSWEI oscillations, although this relation changed after the 1980s, especially at low and medium elevations. This change occurred in correspondence of highly positive air temperature anomalies. In the last decades, we found increasing air temperature to be the main driver for the pronounced snow mass loss and persistent snow-drought conditions.

Meteorological and geographical factors associated with dry lightning in central and northern California

Lightning occurring with less than 2.5 mm of rainfall—typically referred to as ‘dry lightning’—is a major source of wildfire ignition in central and northern California. Despite being rare, dry lightning outbreaks have resulted in destructive fires in this region due to the intersection of dense, dry vegetation and a large population living adjacent to fire-prone lands. Since thunderstorms are much less common in this region relative to the interior West, the climatology and drivers of dry lightning have not been widely investigated in central and northern California. Using daily gridded lightning and precipitation observations (1987–2020) in combination with atmospheric reanalyses, we characterize the climatology of dry lightning and the associated meteorological conditions during the warm season (May–October) when wildfire risk is highest. Across the domain, nearly half (∼46%) of all cloud-to-ground lightning flashes occurred as dry lightning during the study period. We find that higher elevations (>2000 m) receive more dry lightning compared to lower elevations (<1000 m) with activity concentrated in July-August. Although local meteorological conditions show substantial spatial variation, we find regionwide enhancements in mid-tropospheric moisture and instability on dry lightning days relative to background climatology. Additionally, surface temperatures, lower-tropospheric dryness, and mid-tropospheric instability are increased across the region on dry versus wet lightning days. We also identify widespread dry lightning outbreaks in the historical record, quantify their seasonality and spatial extent, and analyze associated large-scale atmospheric patterns. Three of these four atmospheric patterns are characterized by different configurations of ridging over the continental interior and offshore troughing. Understanding the meteorology of dry lightning across this region can inform forecasting of possible wildfire ignitions and is relevant for assessing changes in dry lightning and wildfire risk in climate projections.

Leading modes of wind field variability over the western Tibet Plateau

Atmospheric circulations bring moisture from above the ocean to the high mountains of the western Tibet Plateau (TP), thus wind variability is of great importance to the water cycle centered on the western TP. This study therefore examines the leading modes of the wind fields over the western TP. We use the multivariate empirical orthogonal function (MV-EOF) analysis method to detect the dominant wind patterns above the western TP. This method extracts the leading modes of combined meridional and zonal wind variability at 200-hPa in the region of 22° N–50° N, 50° E–92° E. We find the first leading mode of the combined zonal and meridional wind field in the annual mean and in most seasons (spring, summer and autumn) over the western TP shows high similarity to the Western Tibetan Vortex (WTV), a large-scale atmospheric vortex-like pattern recently recognized over the western TP. In winter, the WTV, however, is closer to the second leading mode. We estimate the sensitivity of our results by changing the domain of the MV-EOF analysis region surrounding the western. We find the influence of the WTV is less sensitive to analysis location around the western TP. In short, the WTV generally represents the first leading mode of the wind field in most seasons over the western TP. This study augments our knowledge on wind variability over the western TP.

Influence of atmospheric teleconnections on interannual variability of Arctic-boreal fires

Fires across the Arctic-boreal zone (ABZ) play an important role in the boreal forest succession, permafrost thaw, and the regional and global carbon cycle and climate. These fires occur mainly in summer with large interannual variability. Previous studies primarily focused on the impacts of local surface climate and tropical El Niño-Southern Oscillation (ENSO). This study, for the first time, comprehensively investigates the influence of summer leading large-scale atmospheric teleconnection patterns in the Northern Hemisphere extra-tropics on interannual variability of ABZ fires. We use correlation and regression analysis of 1997–2019 multiple satellite-based products of burned area and observed/reanalyzed climate data. Results show that eight leading teleconnection patterns significantly affect 63 ± 2 % of burned areas across the ABZ. Western North America is affected by the East Pacific/North Pacific pattern (EP/NP) and the West Pacific pattern (WP); boreal Europe by the Scandinavia pattern (SCA); eastern North America, western and central Siberia, and southeastern Siberia by the North Atlantic Oscillation (NAO); and eastern Siberia /Russian Far East by the East Atlantic pattern (EA). NAO/EA induces lower-tropospheric drier northwesterly/northerly airflow passing through the east of boreal North America/Eurasia, which decreases surface relative humidity. Other teleconnections trigger a high-pressure anomaly, forcing downward motion that suppresses cloud formation and increases solar radiation reaching the ground to warm the surface air as well as brings drier air downward to reduce surface relative humidity. The drier and/or warmer surface air can decrease fuel wetness and thus increase burned area. Our study highlights the important role of the extra-tropical teleconnection patterns on ABZ fires, which is much stronger than ENSO that was thought to control interannual variability of global fires. It also establishes a theoretical foundation for ABZ fire prediction based on extra-tropical teleconnections, and has the potential to facilitate ABZ fire prediction and management.

The Impact of the Direct Radiative Effect of Increased CO2 on the West African Monsoon

Abstract Projections of future West African monsoon (WAM) precipitation change in response to increasing greenhouse gases are uncertain, and an improved understanding of the drivers of WAM precipitation change is needed to help aid model development and better inform adaptation policies in the region. This paper addresses one of those drivers, the direct radiative effect of increased CO2 (i.e., the impact of increased CO2 in the absence of SST warming and changes in plant physiology). An atmosphere-only model is used to examine both the equilibrium response and the evolution of the change over the days following the instantaneous CO2 increase. In response to the direct radiative effect, WAM precipitation increases due to a weakening of the shallow meridional circulation over North Africa, advecting less dry air into the convective column associated with the monsoon. Changes in the shallow circulation are associated with atmospheric and surface warming patterns over North Africa. A large-scale atmospheric warming pattern, whereby North Africa warms more than the monsoon region, leads to a northward shift in the Saharan heat low. In response to increased precipitation in the Sahel, local soil moisture feedbacks play a key role in determining the low-level circulation change and the location of the intertropical discontinuity. The large-scale warming patterns over North Africa result from differing levels of constraint applied by convective quasi-equilibrium. While this constraint acts strongly in the equatorial WAM region, preventing the region from warming in response to the direct radiative effect, North Africa is not strongly constrained and is therefore able to warm.

Seasonal predictions of energy-relevant climate variables through Euro-Atlantic Teleconnections

The goal of this analysis is the better understanding of how the large-scale atmospheric patterns affect the renewable resources over Europe and to investigate to what extent the dynamical predictions of the large-scale variability might be used to formulate empirical prediction of local climate conditions (relevant for the energy sector). The increasing integration of renewable energy into the power mix is making the electricity supply more vulnerable to climate variability, therefore increasing the need for skillful weather and climate predictions. Forecasting seasonal variations of energy relevant climate variables can help the transition to renewable energy and the entire energy industry to make better informed decision-making. At seasonal timescale climate variability can be described by recurring and persistent, large-scale patterns of atmospheric pressure and circulation anomalies that interest vast geographical areas. The main patterns of the North Atlantic region (Euro Atlantic Teleconnections, EATCs) drive variations in the surface climate over Europe. We analyze reanalysis dataset ERA5 and the multi-system seasonal forecast service provided by Copernicus Climate Change Service (C3S). We found that the observed EATC indices are strongly correlated with surface variables. However, the observed relationship between EATC patterns and surface impacts is not accurately reproduced by seasonal prediction systems. This opens the door to employ hybrid dynamical-statistical methods. The idea consists in combining the dynamical seasonal predictions of EATC indices with the observed relationship between EATCs and surface variables. We reconstructed the surface anomalies for multiple seasonal prediction systems and benchmarked these hybrid forecasts with the direct variable forecasts from the systems and also with the climatology. The analysis suggests that hybrid methodology can bring several improvements to the predictions of energy relevant Essential Climate Variables.