Weather Regimes Research Articles

North American Weather Regimes Are Becoming More Persistent: Is Arctic Amplification a Factor?

AbstractRapid Arctic warming is hypothesized to favor an increased persistence of regional weather patterns in the Northern Hemisphere (Francis & Vavrus, 2012). Persistent conditions can lead to drought, heat waves, prolonged cold spells, and storminess that can cost millions of dollars in damage and disrupt societal and ecosystem norms. This study defines a new metric called long‐duration events (LDEs)—conditions that endure at least four consecutive days—and takes two independent approaches to assessing seasonal changes in weather‐pattern persistence over North America. One applies precipitation measurements at weather stations across the United States; the other is based on a cluster analysis of large‐scale, upper‐level atmospheric patterns. Both methods indicate an overall increase in LDEs. We also find that large‐scale patterns consistent with a warm Arctic exhibit an increased frequency of LDEs, suggesting that further Arctic warming may favor persistent weather patterns that can lead to weather extremes.

Breeze effects at a large artificial lake: summer case study

Abstract. Natural lakes and big artificial reservoirs can affect the weather regime of surrounding areas but, usually, consideration of all aspects of this impact and their quantification is a difficult task. The Alqueva reservoir, the largest artificial lake in western Europe, located on the south-east of Portugal, was filled in 2004. It is a large natural laboratory that allows the study of changes in surface and in landscape and how they affect the weather in the region. This paper is focused on a 3-day case study, 22–24 July 2014, during which an intensive observation campaign was carried out. In order to quantify the breeze effects induced by the Alqueva reservoir, two simulations with the mesoscale atmospheric model Meso-NH coupled to the FLake freshwater lake model has been performed. The difference between the two simulations lies in the presence or absence of the reservoir on the model surface. Comparing the two simulation datasets, with and without the reservoir, net results of the lake impact were obtained. Magnitude of the impact on air temperature, relative humidity, and other atmospheric variables are shown. The clear effect of a lake breeze (5–7 m s−1) can be observed during daytime on distances up to 6 km away from the shores and up to 300 m above the surface. The lake breeze system starts to form at 09:00 UTC and dissipates at 18:00–19:00 UTC with the arrival of a larger-scale Atlantic breeze. The descending branch of the lake breeze circulation brings dry air from higher atmospheric layers (2–2.5 km) and redistributes it over the lake. It is also shown that despite its significant intensity the effect is limited to a couple of kilometres away from the lake borders.

Long-term memory and multifractality of downwelling longwave radiation flux at the Earth’s surface

We study for the first time the correlation properties and the multifractality of downward longwave radiation (DLR) temporal fluctuations, using the multi-fractal detrended fluctuation analysis. Our DLR data are taken from 40 stations of the Baseline Surface Radiation Network and span more than 20 years on temporal resolutions of a few minutes. In almost all stations, two different scaling regions are defined through the emergence of a crossover at around 10 days, similarly to earlier studies of other meteorological quantities. The crossover marks the transition from a persistent, non-stationary signal on weather scales to a more weakly persistent (and sometimes anti-persistent) behavior extending in the macroweather region between 2 months and at least several years. We observe typical multifractal behavior for DLR through the investigation of the generalized Hurst exponents and singularity spectra. Generally, the multifractality strength is larger for the weather regime compared to the macroweather. We find evidence that the shape of the probability density function of the DLR anomalies contributes to the multifractality strength for macroweather. The three Western Equatorial Pacific stations exhibit markedly different characteristics, with fluctuation functions closely following 1/f noise between the scales of a few hours up to 2 months. These stations appear to be strongly affected by the ENSO. Also, their timeseries are barely long enough so that the ENSO shows as a semi-periodic signal on the large scales of their long-term memory. Their multifractality is not clearly defined and its strength is much smaller than that of the other stations.

Evaluation of Satellite Surface Winds in Relation to Weather Regimes Over the Indian Ocean Using DYNAMO Observations

AbstractSatellites provide the most extensive surface wind data over the tropical oceans, but sampling limitations, rain contamination, and algorithm uncertainties remain. In this study, scatterometer and passive microwave swath data, and multisatellite products are evaluated using Dynamics of the Madden‐Julian Oscillation (DYNAMO) ship and moored buoy wind speed data over the Indian Ocean from September 2011 to January 2012. Each of the satellite products has <2 m/s error, consistent with previous studies. Blended products have error and bias characteristics comparable with swath data and provide better coverage. The errors and biases depend on the prevailing weather regime. The equatorial region is characterized by weeks of winds <5 m/s with little rain punctuated by episodes of winds >10 m/s and heavy rainfall associated with MJO events. The blended products resolve the episodic enhanced equatorial winds associated with the active phase of the MJO. However, due to limited sampling and rain contamination, the swath data did not fully resolve the peak MJO winds. In the Southern Hemisphere trade wind regime, rain contamination is less of a factor and winds are steadier. Satellite wind estimates of equatorial (trade wind) regime tended to be biased low (high). The surface wind products underestimate higher winds during the active MJO due to limited sampling and have high wind bias in the trade wind regime. These weather regime‐dependent biases should be taken into account for applications using these wind products for ocean circulation studies and estimating air‐sea fluxes over the Indian Ocean.

Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1

Abstract. Sea ice is an important factor affecting weather regimes, especially in polar regions. A lack of its representation in numerical weather prediction (NWP) systems leads to large errors. For example, in the HARMONIE–AROME model configuration of the ALADIN–HIRLAM NWP system, the mean absolute error in 2 m temperature reaches 1.5 ∘C after 15 forecast hours for Svalbard. A possible reason for this is that the sea ice properties are not reproduced correctly (there is no prognostic sea ice temperature in the model). Here, we develop a new simple sea ice scheme (SICE) and implement it in the ALADIN–HIRLAM NWP system in order to improve the forecast quality in areas influenced by sea ice. The new parameterization is evaluated using HARMONIE–AROME experiments covering the Svalbard and Gulf of Bothnia areas for a selected period in March–April 2013. It is found that using the SICE scheme improves the forecast, decreasing the value of the 2 m temperature mean absolute error on average by 0.5 ∘C in areas that are influenced by sea ice. The new scheme is sensitive to the representation of the form drag. The 10 m wind speed bias increases on average by 0.4 m s−1 when the form drag is not taken into account. Also, the performance of SICE in March–April 2013 and December 2015–December 2016 was studied by comparing modelling results with the sea ice surface temperature products from MODIS and VIIRS. The warm bias (of approximately 5 ∘C) of the new scheme is indicated for areas of thick ice in the Arctic. Impacts of the SICE scheme on the modelling results and possibilities for future improvement of sea ice representation in the ALADIN–HIRLAM NWP system are discussed.

Predictability of Recurrent Weather Regimes over North America during Winter from Submonthly Reforecasts

Abstract Four recurrent weather regimes are identified over North America from October to March through a k-means clustering applied to MERRA daily 500-hPa geopotential heights over the 1982–2014 period. Three regimes resemble Rossby wave train patterns with some baroclinicity, while one is related to an NAO-like meridional pressure gradient between eastern North America and western regions of the North Atlantic. All regimes are associated with distinct rainfall and surface temperature anomalies over North America. The four-cluster partition is well reproduced by ECMWF week-1 reforecasts over the 1995–2014 period in terms of spatial structures, daily regime occurrences, and seasonal regime counts. The skill in forecasting daily regime sequences and weekly regime counts is largely limited to 2 weeks. However, skill relationships with the MJO, ENSO, and SST variability in the Atlantic and Indian Oceans suggest further potential for subseasonal predictability based on wintertime large-scale weather regimes.

39 years of directional wave recorded data and relative problems, climatological implications and use

The dataset consists of 39 years of directional wave time series recorded since 1979 at the CNR-ISMAR “Acqua Alta” oceanographic research tower, located in the Northern Adriatic Sea. The extent of the time series allows us to describe the wave climate in the North Adriatic region and to identify trends and links with large scale climate patterns obtained from a single and permanent observational source. The northern part of the Adriatic Sea is characterized by two main wind and correspondingly wave regimes, strongly forced by the regional orography. The high sensitivity of this particular area to even small variations of large scale meteorological patterns allows to explore possible links of the local wave, hence wind, activity with large-scale north hemisphere circulation or weather regimes. Different wave gauges have been used since the start of the measurements, progressively upgraded and repositioned during maintenance operations. The recorded wave data have been thoroughly verified and corrected where necessary.

A 15‐year hail streak climatology for the Alpine region

In this study, we present a unique 15‐year hail streak climatology for Switzerland based on volumetric radar reflectivity. Two radar‐based hail detection products and an automatic thunderstorm‐tracking algorithm were reprocessed for the extended convective season (April–September) between 2002 and 2016. More than 1.1 million convective cells were automatically tracked over the full radar domain, and over 191,000 storms and 31,000 hail streaks in the considered subdomain were selected for analysis following consistency and robustness tests.The year‐to‐year variability in the number of hailstorms reveals two types of convective seasons: (a) a few seasons with hail frequency far above the average, and (b) all other years with an average number of hailstorms. A high number of hailstorms in a particular year is not correlated with a higher number of convective storms in general, but is related to a greater fraction of severe storms.Convection initiation, hail initiation, and hail frequency maxima are located along the southern and northern foothills over the pre‐Alpine area and over the Jura mountains. Few hail streaks are present over the Alpine main ridge. Hail streak frequency and location is found to be strongly dependent on the synoptic‐scale weather regimes. This is important for monthly and seasonal outlooks, as well as for climate modelling.Analysis of storm life cycles shows that: (a) the majority of hail swaths contain only a single hail streak, (b) severe storms follow a more rapid evolution during their initial stages than do less severe storms, and (c) severe storms produce more spatially extended hail streaks.Finally, significant seasonal and diurnal cycles are present in most of the considered storm characteristics.

Investigation of Lightning Flash Locations in Isolated Convection Using LMA Observations

AbstractUsing Lightning Mapping Arrays (LMAs), lightning flash locations in three dimensions have been investigated using multiple methods. Approximately 500,000 flashes were analyzed to reveal the variability of lightning initiation and propagation within convective storms. These flashes were produced by over 4,000 isolated convective storms during one warm season across diverse weather regimes in northern Alabama, Washington, D.C., central Oklahoma, and northeastern Colorado. Lightning locations are analyzed within the context of radar reflectivities and examined for vertical variability. Results show that storms in Colorado preferentially produced flashes at lower altitudes and in regions of higher reflectivity compared to the other regions. The regional differences in flash altitudes are largely attributed to the prevalence of anomalous polarity storms (middle‐ or low‐level dominant positive charge) in Colorado, as anomalous storms produced a majority of flashes at lower altitudes compared to storms with normal polarity charge structures (middle‐level negative charge). Conversely, anomalous storms are exceedingly rare in the other regions of study. The differences in flash altitudes are coincident with discrepancies between annual average densities estimated by satellite observations and LMA. Specifically, large differences in annual average flash density estimates exist in northeastern Colorado, which are not present in the other regions, suggesting that the lower altitude flashes in Colorado may be more difficult to detect by satellites.

The influence of volcanic eruptions on weather regimes over the North Atlantic simulated by ECHAM5/MPI-OM ensemble runs from 800 to 2000 CE

The volcanic fingerprint on the winter North Atlantic atmospheric circulation and climate is analyzed in six ensemble runs of ECHAM5/MPI-OM covering 800–2000 CE, both for equatorial and Northern Hemisphere (NH) eruptions. Large volcanic eruptions influence climate on both annual and decadal time scales due to dynamic interactions of different climate components in the Earth's system. It is well known that the North Atlantic Oscillation (NAO) tends to shift towards its positive phase during winter in the first 1–2 years after large tropical volcanic eruptions, causing warming over Europe, but other North Atlantic weather regimes have received less attention. Here we investigate the four dominant weather regimes in the North Atlantic: The negative and positive phase of NAO as well as the Atlantic Ridge, Scandinavian blocking. The volcanic fingerprint is detected as a change in the frequency of occurrence and anomalies in the wind and temperature fields as well as in the sea ice cover. We observe a strong significant increase in the frequency of Atlantic Ridge in the second year after equatorial eruptions that precede the NAO+ detected in year 3–5 as a result of a strong zonal wind anomalies in year 1–2. Evidence for a stronger polar vortex is detected in years 12–14 where NAO+ is detected both as a frequency increase and in the wind and temperature fields. A short-term response is also detected 2–4 years after NH eruptions. The longterm signal after NH eruptions indicate a weak polar vortex around a decade after an eruption. Although the signal after NH eruptions is weaker our results stress the need for further studies. The simulated atmospheric response recorded in ECHAM5 after volcanic eruptions suggest a more dynamic response than previously thought. The methodology used can also be applied to other forcing scenario, for example for future climate projections where the aim is to search for a long-term climate signal.

Precipitation Sensitivity to the Uncertainty of Terrestrial Water Flow in WRF-Hydro: An Ensemble Analysis for Central Europe

Abstract Precipitation is affected by soil moisture spatial variability. However, this variability is not well represented in atmospheric models that do not consider soil moisture transport as a three-dimensional process. This study investigates the sensitivity of precipitation to the uncertainty in the representation of terrestrial water flow. The tools used for this investigation are the Weather Research and Forecasting (WRF) Model and its hydrologically enhanced version, WRF-Hydro, applied over central Europe during April–October 2008. The model grid is convection permitting, with a horizontal spacing of 2.8 km. The WRF-Hydro subgrid employs a 280-m resolution to resolve lateral terrestrial water flow. A WRF/WRF-Hydro ensemble is constructed by modifying the parameter controlling the partitioning between surface runoff and infiltration and by varying the planetary boundary layer (PBL) scheme. This ensemble represents terrestrial water flow uncertainty originating from the consideration of resolved lateral flow, terrestrial water flow uncertainty in the vertical direction, and turbulence parameterization uncertainty. The uncertainty of terrestrial water flow noticeably increases the normalized ensemble spread of daily precipitation where topography is moderate, surface flux spatial variability is high, and the weather regime is dominated by local processes. The adjusted continuous ranked probability score shows that the PBL uncertainty improves the skill of an ensemble subset in reproducing daily precipitation from the E-OBS observational product by 16%–20%. In comparison to WRF, WRF-Hydro improves this skill by 0.4%–0.7%. The reproduction of observed daily discharge with Nash–Sutcliffe model efficiency coefficients generally above 0.3 demonstrates the potential of WRF-Hydro in hydrological science.

Effect of temperature, light on germination and morphological characteristics of Bipolaris sorokiniana

Wheat is one of the most nutritious, proteinaceous and basic stable food crop is attacked by several factors. Among the biotic factors, the most notorious disease of wheat is spot blotch caused by Bipolaris sorokiniana. The behaviour of the pathogen with different combinations of weather regimes were studied in-vitro, in the dark at different temperatures. Maximum conidial germination (92.04%) was observed at 30 °C followed by 91.69% at 25 °C temperature after 4 h of incubation. Lowest germination was recorded at 10 °C. Maximum length of unipolar germ tube (26.28 µm) was recorded at 15 °C which was statistically superior than other treatments. Whereas, maximum width of unipolar germinated germ tube (4.49 µm) was observed at 25 °C followed by 15 °C. The length and width of germ tubes of the two poles of bipolar germinated B. sorokiniana spores were 19.74, 22.61 and 3.92, 4.11 µm, respectively at 15 °C followed by 20 °C temperature. Significantly lowest time required for secondary conidiophore formation (25 h) and conidia formation (39 h) was recorded at 25 °C. Highest time required for formation of secondary conidiophore (35 h) at 20 °C, conidia at 30 °C (80 h) but at 10 and 35 °C temperature secondary conidiophore and conidia formation was not observed within 80 h of incubation. Higher mycelial growth and no sporulation were observed under continuous light condition.

Equatorially/globally conditioned meteorological analysis of heaviest monsoon rains over India during 23–28 July 2005

The heaviest monsoon rainstorm of the period 1951–2007 over India occurred during 23–28 July 2005, mostly the peninsula received rainfall, and each day the rainwater over the country was 40.0 bcm (billion cubic meter) or more, highest 98.4 bcm fell on 25 July 2005. Present premise of monsoon genesis is that it evolves in association with spreading and intensification of equatorial atmospheric condition over Afro-Eurasian landmass and adjoining Indian and Pacific Oceans during boreal summer. Robust natural criteria have been applied to demarcate monsoon and other global weather regimes (GWRs) at standard levels (1000‒100 hPa). Global atmospheric (1000‒100 hPa) thermal condition and monsoon and general circulations during 23–28 July 2005 have been compared with normal features of respective parameters. Over tropics-subtropics (45°S–45°N), troposphere (1000‒250 hPa) was warmer-thicker and pressure lower than normal and mixed conditions of positive/negative departures in temperature, height/thickness and pressure over northern and southern mid-high latitudes. Noticeable changes in 3D monsoon structure were: horizontally spread and eastward-southward shifted over western North Pacific and stretched further southeastward across equatorial Pacific; intense warm-low lower tropospheric confluence-convergence across Asia–Pacific with vertical depth extending beyond 400 hPa; and intense warm-high upper tropospheric anticyclonic circulation zonally stretched and divided into three interconnected cells. Outflows from anticyclonic cells over Tibetan plateau and western North Pacific were mostly directed westward/southwestward/southward. Troposphere was warmer-thicker and pressure higher over eastern part of both subpolars-polars and cooler-thinner and pressure lower over western part. During the period, a deep cyclonic circulation moved from Bay of Bengal through central India while near-stationary atmospheric condition prevailed across the globe.

Rainfall extremes, weather and climate drivers in complex terrain: A data-driven approach based on signal enhancement methods and EV modeling

Weather and climatic characterization of rainfall extremes is both of scientific and societal value for hydrometeorological risk management, yet discrimination of local and large-scale forcing remains challenging in data-scarce environments. Here, we present an analysis framework that separates weather regime and climate controls using data-driven process identification. The approach is based on signal-to-noise separation methods and explanatory extreme value (EV) modeling of multisite rainfall extremes. The EV models integrate the temporal component of the weather/climate driver using semi-automatic parameter identification. At weather scale, the EV models are combined with a state-based Markov model to represent the spatiotemporal structure of rainfall as weather states. At climate scale, the EV models are used to search for drivers leading to the shift of weather patterns. The drivers are brought out in a climate-to-weather signal subspace, built via dimension reduction of climate model reconstructions.We apply the framework to a complex terrain region: the Western Andean Ridge in Ecuador and Peru (0–6°S) using ground data from the second half of the 20th century. Overall, we show that the framework, which does not make any prior assumption on the explanatory power of the weather and climate drivers, allows identification of well-known and new features of the regional climate in a purely data-driven fashion. Thus, the approach shows potential to identify weather controls on precipitation extremes in data-scarce and orographically complex regions in which model reconstructions are the only climate proxies.

A weather regime characterisation of Irish wind generation and electricity demand in winters 2009–11

Prolonged cold spells were experienced in Ireland in the winters of 2009–10 and 2010–11, and electricity demand was relatively high at these times, whilst wind generation capacity factors were low. Such situations can cause difficulties for an electricity system with a high dependence on wind energy. Studying the atmospheric conditions associated with these two winters offers insights into the large-scale drivers for cold, calm spells, and helps to evaluate if they are rare events over the long-term. The influence of particular atmospheric patterns on coincidental winter wind generation and weather-related electricity demand is investigated here, with a focus on blocking in the North Atlantic/European sector. The occurrences of such patterns in the 2009–10 and 2010–11 winters are examined, and 2010–11 in particular was found to be unusual in a long-term context. The results are discussed in terms of the relevance to long-term planning and investment in the electricity system.

Evaluation and Postprocessing of Ensemble Fire Weather Predictions over the Northeast United States

AbstractThe Short-Range Ensemble Forecast (SREF) system is verified and bias corrected for fire weather days (FWDs) defined as having an elevated probability of wildfire occurrence using a statistical Fire Weather Index (FWI) over a subdomain of the northeastern United States (NEUS) between 2007 and 2014. The SREF is compared to the Rapid Update Cycle and Rapid Refresh analyses for temperature, relative humidity, specific humidity, and the FWI. An additive bias correction is employed using the most recent previous 14 days [sequential bias correction (SBC)] and the most recent previous 14 FWDs [conditional bias correction (CBC)]. Synoptic weather regimes on FWDs are established using cluster analysis (CA) on North American Regional Reanalysis sea level pressure, 850-hPa temperature, 500-hPa temperature, and 500-hPa geopotential height. SREF severely underpredicts FWI (by two indices at FWI = 3) on FWDs, which is partially corrected using SBC and largely corrected with CBC. FWI underprediction is associated with a cool (ensemble mean error of −1.8 K) and wet near-surface model bias (ensemble mean error of 0.46 g kg−1) that decreases to near zero above 800 hPa. Although CBC improves reliability and Brier skill scores on FWDs, ensemble FWI values exhibit underdispersion. CA reveals three synoptic weather regimes on FWDs, with the largest cool and wet biases associated with a departing surface low pressure system. These results suggest the potential benefit of an operational analog bias correction on FWDs. Furthermore, CA may help elucidate model error during certain synoptic weather regimes.

Evaluation of the HadGEM3-A simulations in view of detection and attribution of human influence on extreme events in Europe

A detailed analysis is carried out to assess the HadGEM3-A global atmospheric model skill in simulating extreme temperatures, precipitation and storm surges in Europe in the view of their attribution to human influence. The analysis is performed based on an ensemble of 15 atmospheric simulations forced with observed sea surface temperature of the 54 year period 1960–2013. These simulations, together with dual simulations without human influence in the forcing, are intended to be used in weather and climate event attribution. The analysis investigates the main processes leading to extreme events, including atmospheric circulation patterns, their links with temperature extremes, land–atmosphere and troposphere-stratosphere interactions. It also compares observed and simulated variability, trends and generalized extreme value theory parameters for temperature and precipitation. One of the most striking findings is the ability of the model to capture North-Atlantic atmospheric weather regimes as obtained from a cluster analysis of sea level pressure fields. The model also reproduces the main observed weather patterns responsible for temperature and precipitation extreme events. However, biases are found in many physical processes. Slightly excessive drying may be the cause of an overestimated summer interannual variability and too intense heat waves, especially in central/northern Europe. However, this does not seem to hinder proper simulation of summer temperature trends. Cold extremes appear well simulated, as well as the underlying blocking frequency and stratosphere-troposphere interactions. Extreme precipitation amounts are overestimated and too variable. The atmospheric conditions leading to storm surges were also examined in the Baltics region. There, simulated weather conditions appear not to be leading to strong enough storm surges, but winds were found in very good agreement with reanalyses. The performance in reproducing atmospheric weather patterns indicates that biases mainly originate from local and regional physical processes. This makes local bias adjustment meaningful for climate change attribution.

Estimates of flow‐dependent predictability of wintertime Euro‐Atlantic weather regimes in medium‐range forecasts

This study assesses the medium‐range flow‐dependent forecast skill of Euro‐Atlantic weather regimes: the positive and negative phases of the North Atlantic Oscillation (NAO+ and NAO−), Atlantic ridge (ATLR), and Euro‐Atlantic blocking (EABL), for extended winters (November–March) in the periods 2006/2007–2013/2014 and 1985/1986–2013/2014 using The Interactive Grand Global Ensemble (TIGGE) and the National Oceanic and Atmospheric Administration (NOAA)'s Global Ensemble Forecasting System (GEFS) reforecast datasets, respectively. The models show greater‐than‐observed (smaller‐than‐observed) frequencies of NAO− and ATLR (NAO+) with forecast lead time. The increased frequency of NAO− is not due to its excess persistence but due to more frequent transitions mainly from ATLR, but also from NAO+. In turn, NAO+ is under‐persistent. The models show the highest probabilistic skill for forecasts initialised on NAO− and the NAO− forecasts during the TIGGE period. However, the GEFS reforecast during the period 1985/1986–2013/2014 revealed that these recent high skills reflect the occurrence of four long‐lasting (>30 days) NAO− events in 2009/2010–2013/2014 and that the skill for forecasts initialised on NAO− before 2009/2010 (the longest duration was 22 days and the second‐longest 16 days) was the lowest. The longer the NAO− events persist, the higher the skill of forecasts initialised on NAO−. The skill dependency on regime duration is less clearly observed for the other regimes. In addition, the GEFS reforecast also revealed that the highest skill of the NAO− forecasts during the period 1985/1986–2013/2014 is attributed to the higher skill of the NAO− forecasts during the active NAO− periods. The EABL forecasts initialised on ATLR show the lowest skill, followed by the NAO− (EABL) forecasts initialised on NAO+ or ATLR (NAO+). These results suggest that the recent models still have difficulties in predicting the onset of blocking.

The influence of the stratospheric state on North Atlantic weather regimes

Stratosphere–troposphere coupling is often viewed from the perspective of the annular modes and their dynamics. Despite the obvious benefits of this approach, recent work has emphasised the greater tropospheric sensitivity to stratospheric variability in the Atlantic basin than in the Pacific basin. In this study, a new approach to understanding stratosphere–troposphere coupling is proposed, with a focus on the influence of the stratospheric state on North Atlantic weather regimes (during extended winter, November to March). The influence of the strength of the lower‐stratospheric vortex on four commonly used tropospheric weather regimes is quantified. The negative phase of the North Atlantic Oscillation is most sensitive to the stratospheric state, occurring on 33% of days following weak vortex conditions but on only 5% of days following strong vortex conditions. An opposite and slightly weaker sensitivity is found for the positive phase of the North Atlantic Oscillation and the Atlantic Ridge regime. For the North Atlantic Oscillation regimes, stratospheric conditions change both the probability of remaining in each regime and the probability of transitioning to that regime from others. A logistic regression model is developed to further quantify the sensitivity of tropospheric weather regimes to the lower stratospheric state. The logistic regression model predicts an increase of 40–60% in the probability of transition to the negative phase of the North Atlantic Oscillation for a one standard deviation reduction in the strength of the stratospheric vortex. Similarly it predicts a 10–30% increase in the probability of transition to the positive phase of the North Atlantic Oscillation for a one standard deviation increase in the strength of the stratospheric vortex. The stratosphere–troposphere coupling in the European Centre for Medium‐range Weather Forecasts Integrated Forecasting System model is found to be consistent with the re‐analysis data by fitting the same logistic regression model.

The precipitation response to variable terrain forcing over low mountain ranges in different weather regimes

The impact of terrain forcing on cloud formation and precipitation over low mountain ranges is investigated by numerical experiments with the COnsortium for Small‐scale MOdeling (COSMO) model. The investigation comprises six case studies divided into strong and weak large‐scale synoptic forcing. To understand how the terrain affects the occurrence and intensity of precipitation, sensitivity runs with flattened mountains and incrementally smoothed terrain were performed at 500‐m horizontal grid spacing. On days with weak forcing (i.e. air‐mass convection), results show that low‐level wind convergence is crucial for the initiation of deep convection. Its strength varies between the simulations, depending e.g. on the formation of boundary‐layer rolls, and determines whether there is an increase or decrease of total precipitation in the simulations with flattened individual mountains compared with the reference run. For cases with strong synoptic forcing (i.e. passage of frontal zones), the large‐scale advection of precipitation interacts with local effects, as advected cells are not intensified by orographic uplift in the absence of mountains. As a consequence, the runs with flattened mountains show higher moisture contents over flat terrain, which can lead to more precipitation downstream. The model runs with smoothed external parameters (i.e. terrain height, land use, roughness length) show small changes on days with strong forcing and slightly larger effects under weak forcing. However, changing the resolution of the external parameters has only a relatively small effect on precipitation in high‐resolution simulations. The results from this study demonstrate the complexity of multiple processes, like lifting or flow deviation by mountains and wind changes due to thermal instabilities, on different spatial scales for the initiation of deep convection over complex terrain.