Sea Level Pressure Data Research Articles

Analysis of the intra-day solar resource variability in the Iberian Peninsula

The intra-day modes of variability of the solar resources in the Iberian Peninsula, their associated weather patterns and their impact on the solar power output are assessed in this work. The analysis is performed for yearly and seasonal variability. Firstly, the modes of variability are identified by means of hierarchical cluster analysis. It is computed with two years of measured global horizontal irradiance (GHI) and direct normal irradiance (DNI) data gathered at four stations. Notably, three-hour statistics describing mean and variability of solar radiation are used as input to the cluster analysis. Secondly, synoptic weather patterns associated with each group resulting from the cluster analysis are assessed using sea level pressure and cloudiness data. Finally, the solar PV power yield associated with each mode is evaluated. The yearly analysis reveals the existence of four modes of variability of the solar resource in the study area. The four modes are shown to have a distinctive weather pattern and also specific impacts on solar power generation in the study area. Seasonal analyses show results similar to the annual analysis, but with marked seasonal differences.

Non-seasonal sea level variations in the Korea Strait and regional atmospheric conditions

Kim, J.-K.; Choi, B.-J., and Lee S.-H., 2018. Non-seasonal sea level variations in the Korea Strait and regional atmospheric conditions. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 581–585. Coconut Creek (Florida), ISSN 0749-0208.To investigate dominant sea level variations in the Korea Strait (KS) and to find their responsible factors, Empirical Orthogonal Function (EOF) analysis was performed on the non-seasonal components of sea level, wind, and sea level pressure data from 1993 to 2014. The non-seasonal sea level variation accounts for 20% of the total sea level variations in the KS. The first EOF mode of the sea level explains 63.8% of the total variance and represents strait-wide simultaneous sea level oscillations. EOF analysis on the surface wind data over the KS and the East China Sea was performed and the first EOF of winds, explaining 49.5% of the total variance, represents the strength of the northeasterly wind. The amplitude time series of the first EOFs from wind and sea level data has correlation coefficients (r) of 0.48. The sea level and wind data have common spectral peaks at periods of 5.1 and 10.2 months. Variance and the first EOF of the steric height in the KS are relatively high around Jeju Island while they are small near the south coast of Korea. Correlation between the amplitude time series of sea level and steric height first modes is low (r = 0.20). The basin-wide simultaneous sea level variations with intra-seasonal and interannual periods in the KS are found to be modulated by variations in the regional surface winds and sea level pressure patterns over the East China Sea and the KS rather than by changes in local steric height.

Influence of the Scandinavian climate pattern on the UK asthma mortality: a time series and geospatial study

ObjectiveIt is well known that climate variability and trends have an impact on human morbidity and mortality, especially during the winter. However, there are only a handful of studies that...

Climatology of the autumn Red Sea trough

In this study, the Sudan low and the associated Red Sea trough (RST) are objectively identified using the mean sea level pressure (SLP) data from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis dataset covering the period 1955–2015. The Sudan low was detected in approximately 60.6% of the autumn periods, and approximately 83% of the detected low-pressure systems extended into RSTs, with most generated at night and during cold months. The distribution of the RSTs demonstrated that Sudan, South Sudan and Red Sea are the primary development areas of the RSTs, generating 97% of the RSTs in the study period. In addition, the outermost areas affected by RSTs, which include the southern, central and northern Red Sea areas, received approximately 91% of the RSTs originating from the primary generation areas. The synoptic features indicated that a Sudan low developed into an RST when the Sudan low deepened in the atmosphere, while the low pressures over the southern Arabian Peninsula are shallow and the anticyclonic systems are weakened over the northern Red Sea. Moreover, stabile areas over Africa and Arabian Peninsula form a high stability gradient around the Red Sea and the upper maximum winds weaken. The results of the case studies indicate that RSTs extend northward when the upper cyclonic and anticyclonic systems form a high geopotential gradient over Arabian Peninsula. Furthermore, the RST is oriented from the west to the east when the Azores high extends eastward and the Siberian high shrinks eastward or shifts northward.

Atmospheric Dynamics is the Largest Source of Uncertainty in Future Winter European Rainfall

Abstract The IPCC Fifth Assessment Report highlighted large uncertainty in European precipitation changes in the coming century. This paper investigates the sources of intermodel differences using CMIP5 model European precipitation data. The contribution of atmospheric circulation to differences in precipitation trends is investigated by applying cluster analysis to daily mean sea level pressure (MSLP) data. The resulting classification is used to reconstruct monthly precipitation time series, thereby isolating the component of precipitation variability directly related to atmospheric circulation. Reconstructed observed precipitation and reconstructions of simulated historical and projection data are well correlated with the original precipitation series, showing that circulation variability accounts for a substantial fraction of European precipitation variability. Removing the reconstructed precipitation from the original precipitation leaves a residual component related to noncirculation effects (and any small remaining circulation effects). Intermodel spread in residual future European precipitation trends is substantially reduced compared to the spread of the original precipitation trends. Uncertainty in future atmospheric circulation accounts for more than half of the intermodel variance in twenty-first-century precipitation trends for winter months for both northern and southern Europe. Furthermore, a substantial part of this variance is related to different forced dynamical responses in different models and is therefore potentially reducible. These results highlight the importance of understanding future changes in atmospheric dynamics in achieving more robust projections of regional climate change. Finally, the possible dynamical mechanisms that may drive the future differences in regional circulation and precipitation are illustrated by examining simulated teleconnections with tropical precipitation.

Evaluation of Cool-Season Extratropical Cyclones in a Multimodel Ensemble for Eastern North America and the Western Atlantic Ocean

Abstract This paper evaluates the extratropical cyclones within three operational global ensembles [the 20-member Canadian Meteorological Centre (CMC), 20-member National Centers for Environmental Prediction (NCEP), and 50-member European Centre for Medium-Range Weather Forecasts (ECMWF)]. The day-0–6 forecasts were evaluated over the eastern United States and western Atlantic for the 2007–15 cool seasons (October–March) using the ECMWF’s ERA-Interim dataset as the verifying analysis. The Hodges cyclone-tracking scheme was used to track cyclones using 6-h mean sea level pressure (MSLP) data. For lead times less than 72 h, the NCEP and ECMWF ensembles have comparable mean absolute errors in cyclone intensity and track, while the CMC errors are larger. For days 4–6 ECMWF has 12–18 and 24–30 h more accuracy for cyclone intensity than NCEP and CMC, respectively. All ensembles underpredict relatively deep cyclones in the medium range, with one area near the Gulf Stream. CMC, NCEP, and ECMWF all have a slow along-track bias that is significant from 24 to 90 h, and they have a left-of-track bias from 120 to 144 h. ECMWF has greater probabilistic skill for intensity and track than CMC and NCEP, while the 90-member multimodel ensemble (NCEP + CMC + ECMWF) has more probabilistic skill than any single ensemble. During the medium range, the ECMWF + NCEP + CMC multimodel ensemble has the fewest cases (1.9%, 1.8%, and 1.0%) outside the envelope compared to ECMWF (5.6%, 5.2%, and 4.1%) and NCEP (13.7%, 10.6%, and 11.0%) for cyclone intensity and along- and cross-track positions.

A novel scaling indicator of early warning signals helps anticipate tropical cyclones

Tipping events in dynamical systems have been studied in many contexts, often modelled by the decay of critical modes, system states which are tending towards bifurcation, characterised by increased return times to stable equilibria. Temporal scaling properties of time series data can be used to detect the presence of a critical mode by estimating the decay rate, and indicators of changes in these properties may therefore be used to provide an early warning signal (EWS) for an impending tipping event. The lag-1 autocorrelation function (ACF(1)) indicator and the detrended fluctuation analysis (DFA) indicator have previously been used in such a way; in this paper we introduce a novel scaling indicator based on the decay rate of the power spectrum (PS). We compare the ACF(1), DFA- and PS-indicators using artificial data; data from a model which includes a bifurcation point; and sea-level pressure data along the paths of 14 tropical cyclones. By using the PS-indicator with such data, we show that the new indicator may be used to provide an EWS in a context where the ACF(1)- and DFA-indicators fail.

Assimilating Weather Data into a Digital Event Gazetteer of Airborne Parachute Operations during the French Indochina War

Abstract Modern datasets cataloging historical events, known as digital event gazetteers, feature spatiotemporal data regarding events that enable analysis through parameters including location and other descriptive information of those events. Weather and climate data represent two dimensions of spatiotemporal information, which can enhance understanding of historical events. A recently published digital event gazetteer of airborne parachute operations [opérations aéroportées (OAPs)] during and prior to the French Indochina War, spanning from 1945 to 1954, represents an opportunity to associate discrete historical events with weather information. This study outlines a methodology for assimilating weather data into the construct of a digital event gazetteer and then demonstrates example analyses of how the weather and climate conditions in Indochina may relate to OAPs during the war. A synoptic classification, utilizing the self-organizing maps procedure, is performed using daily mean sea level pressure data from 1945 to 2010, from a twentieth-century reanalysis dataset, to characterize weather patterns over the Indochina Peninsula. Since observations are sparse during the years of the conflict, the resulting weather patterns are associated with modern precipitation observations in the area, as a representation of wet and dry patterns during the war. The appropriate daily weather pattern is then assigned to each OAP in order to investigate its relationship with the weather and climate patterns of Indochina, including the influence of monsoon seasons, and how the resulting precipitation patterns affected combat operations across the theater. Additionally, specific OAPs of various missions are analyzed to investigate how weather patterns may have affected operation planning during the French Indochina War.

The KLIWAS North Sea Climatology. Part I: Processing of the Atmospheric Data

AbstractClimatological reference data serve as validation of regional climate models, as the boundary condition for the model runs, and as input for assimilation systems used by reanalyses. Within the framework of the interdisciplinary research program Climate Water Navigation (KLIWAS): Impacts of Climate Change on Waterways and Navigation of the German Federal Ministry of Transport and Digital Infrastructure, a new climatology of the North Sea and adjacent regions was developed in an joint effort by the Federal Maritime and Hydrographic Agency, the German Weather Service [Deutscher Wetterdienst (DWD)], and the Integrated Climate Data Center (ICDC) of the University of Hamburg. Long-term records of monthly and annual mean 2-m air temperature, dewpoint temperature, and sea level pressure data from 1950 to 2010 were calculated on a horizontal 1° × 1° grid. All products were based on quality-controlled data from DWD’s Marine Data Centre. Correction methods were implemented for each parameter to reduce the sampling error resulting from the sparse coverage of observations in certain regions. Comparisons between sampling error estimates based on ERA-40 and the climatology products show that the sampling error was reduced effectively. The climatologies are available for download on the ICDC’s website and will be updated regularly regarding new observations and additional parameters. An extension to the Baltic Sea is in progress.

Trends in the frequency of synoptic types in central-southern Chile in the period 1961–2012 using the Jenkinson and Collison synoptic classification

Atmospheric circulation patterns in southern Chile (42° 30′ S) were studied in order to determine and analyse the most characteristic synoptic types and their recent trends, as well as to gain an understanding of how they are associated with low-frequency variability patterns. According to the Jenkinson and Collison (J&C) classification method, a 16-point grid of sea-level pressure data was employed. The findings reveal that some synoptic types show statistically significant trends with a 95% confidence level, positively for anticyclonic westerly hybrids (AW) and advective types for third and fourth quadrant wind flows (W, NW, and N) and negatively for SW and cyclonic hybrids (CS and CSW). A model has been constructed of the linear regression of some weather types with teleconnections that most affect Chile: the undetermined types (U), AW were associated with El Nino or the warm phase of the Pacific Decadal Oscillation (PDO), whereas the cyclonic northerly and cyclonic northeasterly types (CN and CNE) were associated with La Nina or cool phase of the PDO. The weather types associated with Antarctic Oscillation (AAO) in its positive phase are anticyclonic northerly and northeasterly and northerly advection types, while in its negative phase are cyclonic southwesterly and advection types.

Changing contribution rate of heavy rainfall to the rainy season precipitation in Northeast China and its possible causes

Based on the daily precipitation data from 208 meteorological stations in Northeast China, NCEP/NCAR reanalysis monthly mean wind, sea level pressure data and NOAA reconstructed monthly mean sea surface temperature (SST) data from 1961 to 2013, the contribution rate of heavy rainfall to the total rainfall (hereafter referred to as “heavy rainfall contribution rate” or HRCR) during the rainy season in Northeast China was investigated. The changing characteristics of HRCR in the context of global warming are analyzed. The relationship between the HRCR and the contemporaneous atmospheric general circulation and early SST anomaly was analyzed to understand the possible physical mechanism responsible for the changing HRCR before and after the warming. Results show that during the whole study period (1961–2013), no evident trend in the HRCR has been detected. However, during cold period (1961–1979), the HRCR showed a significantly declining trend, while during warm period (1981–2013), the HRCR does not exhibit any trend. During cold period, the anomalous North Pacific summer monsoon and March North Atlantic tripole SSTA are the main factors affecting the HRCR, while the West Pacific summer monsoon, the East Asian subtropical westerly jet and March North Pacific dipole SSTA are responsible for the HRCR in the warm period. In the cold period, due to the air-sea interaction, March Atlantic tripole SSTA can influence the North Pacific summer monsoon in the late July–August, in turn affecting the HRCR. In the warm period, March North Pacific dipole SSTA tends to cause anomalies in the West Pacific summer monsoon and the position of the East Asian subtropical westerly jet axis in the July–August through air-sea interaction, thereby affecting the HRCR. During 1961–1979, the weakening of the North Pacific summer monsoon might have been the primary cause of the significant decline in the trend of the HRCR in the cold period. In 1981–2013, the absence of significant trends of the West Pacific summer monsoon and subtropical westerly jet position might be the main reason for the lack of an obvious linear trend of HRCR in the warm period.

Synoptic conditions leading to extremely warm periods in Western Iran

ABSTRACTThe purpose of this study is the characterization of the synoptic circulation patterns leading to extremely warm periods (EWPs) over the Kurdistan province of Iran. Daily maximum temperature data from 27 meteorological stations were used for the period 1962–2012. Data were interpolated to a 6 × 6 km grid using the geospatial Kriging method. EWPs were defined as a period of at least 6 consecutive days with maximum temperature above the local daily‐based 95th percentile of the 1971–2000 period, with additional criteria for spatial extension. The identified EWPs are distributed through the whole year, with maximum frequencies in winter and summer. Interestingly, the decade 1988–1997 did not record any extreme episode and summer extremes were recorded only before 1983. The associated synoptic patterns were classified into four clusters by applying the Ward's hierarchical clustering method to sea level pressure and Z500 data. Four clusters are identified: two represent warm periods in winter, one those occurring spring, especially in March, and another one mainly reflecting summer heat waves. In general, it is found that EWPs are associated with statistically significant anomalies in regional circulation. A deep trough of geopotential height in 500 hPa over North Africa and a ridge directly over the study area were the more important causes for the occurrence of EWPs. Other synoptic configurations that favour extreme periods in winter and summer include, respectively, the northward expansion of the Sudan low pressure with an associated southerly wind advection and the persistence of anticyclonic circulation at all tropospheric levels enhancing subsidence and adiabatic heating. The spring pattern is the most frequent one (nearly 48% of all cases) and reflects a southeastward extension of the subpolar low pressure that leads to the displacement of the Siberian high to south and southwestern Asia and the accompanied northward expansion of the Sudan low.

Sensitivity of typhoon modeling to surface waves and rainfall

AbstractImproving intensity simulation and forecast of tropical cyclones has always been a challenge, although in recent years the track forecasts have been remarkably improved. In this study, we explore the sensitivity of typhoon simulation to three physical processes using a fully coupled atmosphere‐ocean‐wave model. Two storms, a strong and a weak one, have been chosen. The effects of wave breaking induced sea spray, ocean vertical mixing associated with nonbreaking surface waves, and sea surface cooling due to intense rainfall are assessed by means of a set of numerical experiments. The results show and confirm that sea spray leads to an increase of typhoon intensity by enhancing the air‐sea heat flux, while nonbreaking wave‐induced vertical mixing and rainfall lead to a decrease. Each process can be relevant, depending on wind and wave conditions. These can vary dramatically when typhoons interact with not sufficiently well‐defined coastal areas, typically an archipelago. Compared with the control runs, when all the three physical processes are considered, the (absolute) difference between the modeled sea level pressure and best track data is reduced from 26.05 to 0.70 hPa for typhoon Haiyan, and from −9.42 to −8.67 hPa for typhoon Jebi. We have found a steady overestimate of the dimensions of the typhoons. We have verified an extreme sensitivity to the initial conditions, especially when small differences in the typhoon track may imply different relevance of the physical processes, like the ones we have considered, governing the evolution of the storm.

Prediction of summer rainfall in Pakistan from global sea‐surface temperature and sea‐level pressure

Agriculture contributes around a quarter to Pakistan's economy and is closely linked with the variability of monsoon rainfall. The prediction of monsoon rains with sufficient lead time has immense importance for the planning and management of water resources and agriculture. In this study, Multiple Linear Regression (MLR) and Principal Component Regression (PCR) methods are employed to predict monsoon rainfall, and their performances are compared for June–September (JJAS) for the period 1961–2014 over the monsoon region of Pakistan. Rainfall data of Meteorological stations are used as the predictand. In the MLR method, predictors are carried out from sea level pressure (SLP) and sea surface temperature (SST) of the National Centers for Environmental Prediction (NCEP) reanalysis datasets. The PCR method first calculates principal components (PCs) from SLP and SST data, and these PCs are then combined with the regression technique and used as predictors. The performance of both models is tested using statistical measures such as root mean square error (RMSE), mean absolute error (MAE), bias and the correlation coefficient to evaluate the skill of the forecast. The agreement between actual and predicted rainfall data provides evidence for reasonably accurate predictions from both methods. The MLR and PCR models explained 84.6 and 92.2% of the variation of data, and the multiple correlation coefficients are 0.92 and 0.96 respectively. The correlation coefficient for the verification period (2005–2014) is 0.73 for MLR and 0.89 for PCR. The values of mean bias, MAE and RMSE are −5.5, 20.0 and 25.1mm for MLR, and −0.42, 16.2 and 16.6mm for PCR, respectively. The results indicate that the PCR model forecast is slightly better than that of the MLR model.

Combining dispersion modelling with synoptic patterns to understand the wind-borne transport into the UK of the bluetongue disease vector.

Bluetongue, an economically important animal disease, can be spread over long distances by carriage of insect vectors (Culicoides biting midges) on the wind. The weather conditions which influence the midge's flight are controlled by synoptic scale atmospheric circulations. A method is proposed that links wind-borne dispersion of the insects to synoptic circulation through the use of a dispersion model in combination with principal component analysis (PCA) and cluster analysis. We illustrate how to identify the main synoptic situations present during times of midge incursions into the UK from the European continent. A PCA was conducted on high-pass-filtered mean sea-level pressure data for a domain centred over north-west Europe from 2005 to 2007. A clustering algorithm applied to the PCA scores indicated the data should be divided into five classes for which averages were calculated, providing a classification of the main synoptic types present. Midge incursion events were found to mainly occur in two synoptic categories; 64.8% were associated with a pattern displaying a pressure gradient over the North Atlantic leading to moderate south-westerly flow over the UK and 17.9% of the events occurred when high pressure dominated the region leading to south-easterly or easterly winds. The winds indicated by the pressure maps generally compared well against observations from a surface station and analysis charts. This technique could be used to assess frequency and timings of incursions of virus into new areas on seasonal and decadal timescales, currently not possible with other dispersion or biological modelling methods.

Impact of the North Atlantic Oscillation on winter climate of Germany

Correlations of the North Atlantic Oscillation (NAO) with climate variables in Europe are strong in the north and the south of Europe but there is a zone of statistically not significant correlations in between. This paper presents evidence that the impact of NAO can be ascertained more clearly by decomposing it into its constituent centres of action (COA), the Azores High (AZH) and the Icelandic Low (IL), each of which varies continuously in intensity as well as position. Monthly values of intensity and latitude and longitude positions for each COA are estimated from sea level pressure data. These are used to analyse their impact on interannual variations of winter precipitation and climate of Germany. Correlation of NAO with winter precipitation is weak but statistically significant in north Germany and not significant in south Germany. By separately calculating correlations with the AZH and the IL, it is found that the primary influence on the interannual variations of winter precipitation and surface temperature over both north and south Germany is the zonal migration of the IL. When the IL is situated east of its mean location, there is above normal precipitation and warmer temperatures over Germany and in winters when the IL is situated west of its mean longitudinal position, drier and colder conditions occur over Germany. The impact of this teleconnection is greater in north Germany than in the south, presumably because of the mountainous topography in the south which hinders wind flow.

Sea level pressure variation and major cyclone events in coastal regions of Bangladesh

The study was conducted in 16 coastal district viz. Satkhira, Khulna, Mongla, Khepupara, Barishal, Bhola, Patuakhali, Hatiya, Chandpur, Feni, Swandip, Sitakunda, Chittagong, Kutubdia, Cox’s Bazar and Teknaf of Bangladesh during the period of 1975-2014 to investigate the variation of sea level pressure (SLP) and rainfall. SLP data of 40 years (1975-2014) have been analyzed in this study. The SLP was increasing slightly to the month of September and then a rapid increasing was observed from the month of October to December. Of all the stations, in the month of June and July the lowest SLP was observed in the Khulna region and highest in Teknaf. In the month of December and January the highest and lowest SLP was found in the Month of January. As the fluctuation of SLP was high in the summer season the possibility of cyclone occurrence also rise in this season. The average rainfall is lowest in the station Sitakunda and highest in Swandip. The average rainfall of all district divided the stations into two parts. The rainfall of Barishal, Bhola, Chandpur, Mongla, Sitakunda and Khulna is lower the average of all district. This shows a great instability of weather in these regions. Rest of the stations has the higher average rainfall than the average rainfall of all stations.Progressive Agriculture 27 (3): 285-295, 2016

Genetic climatic regionalization of the Balkan Peninsula using cluster analysis

Sea Level Pressure (SLP) data for the period 1950–2012 at 61 stations located in or around the Balkan Peninsula was used. The main concept is that intra-annual course of SLP represents the best different air masses that are situated over the Balkan Peninsula during the year. The method for differentiation of climatic zones is cluster analysis. A hierarchical clustering technique–average linkage between groups with Pearson correlation for measurement of intervals was employed in the research. The climate of the Balkan Peninsula is transitional between oceanic and continental and also between subtropical and temperate climates. Several major changes in atmospheric circulation over the Balkan Peninsula have happened over the period 1950–2012. There is a serious increase of the influence of the Azores High in the period January–March, which leads to an increase of SLP and enhances oceanic influence. There is an increase of the influence of the north-west extension of the monsoonal low in the period June–September. This leads to more continental climate, but also to more tropical air masses over the Balkan Peninsula. Accordingly, the extent of subtropical climate widens in northern direction. There is an increase of the influence of the Siberian High in the period October–December. This influence covers central and eastern part of the peninsula in October and November, and it reaches western parts in December. Thus, the climate becomes more continental.

Climate Change in the Kola Peninsula, Arctic Russia, during the Last 50 Years from Meteorological Observations

AbstractThe authors provide a detailed climatology and evaluation of recent climate change in the Kola Peninsula, Arctic Russia, a region influenced by both the North Atlantic and Arctic Oceans. The analysis is based on 50 years of monthly surface air temperature (SAT), precipitation (PPN), and sea level pressure (SLP) data from 10 meteorological stations for 1966–2015. Regional mean annual SAT is ~0°C: the moderating effect of the ocean is such that coastal (inland) stations have a positive (negative) value. Examined mean annual PPN totals rise from ~430 mm in the northeast of the region to ~600 mm in the west. Annual SAT in the Kola Peninsula has increased by 2.3° ± 1.0°C over the past 50 years. Seasonally, statistically significant warming has taken place in spring and fall, although the largest trend has occurred in winter. Although there has been no significant change in annual PPN, spring has become significantly wetter and fall drier. The former is associated with the only significant seasonal SLP trend (decrease). A positive winter North Atlantic Oscillation (NAO) index is generally associated with a warmer and wetter Kola Peninsula whereas a positive Siberian high (SH) index has the opposite impact. The relationship between both the NAO and SH and the SAT is broadly coherent across the region whereas their relationship with PPN varies markedly, although none of the relationships is temporally invariant. Reduced sea ice in the Barents and White Seas and associated circulation changes are likely to be the principal drivers behind the observed changes.

Future projections of synoptic weather types over the Arabian Peninsula during the twenty-first century using an ensemble of CMIP5 models

An assessment of future change in synoptic conditions over the Arabian Peninsula throughout the twenty-first century was performed using 20 climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database. We employed the mean sea level pressure (SLP) data from model output together with NCEP/NCAR reanalysis data and compared the relevant circulation types produced by the Lamb classification scheme for the base period 1975–2000. Overall, model results illustrated good agreement with the reanalysis, albeit with a tendency to underestimate cyclonic (C) and southeasterly (SE) patterns and to overestimate anticyclones and directional flows. We also investigated future projections for each circulation-type during the rainy season (December–May) using three Representative Concentration Pathways (RCPs), comprising RCP2.6, RCP4.5, and RCP8.5. Overall, two scenarios (RCP4.5 and RCP 8.5) revealed a statistically significant increase in weather types favoring above normal rainfall in the region (e.g., C and E-types). In contrast, weather types associated with lower amounts of rainfall (e.g., anticyclones) are projected to decrease in winter but increase in spring. For all scenarios, there was consistent agreement on the sign of change (i.e., positive/negative) for the most frequent patterns (e.g., C, SE, E and A-types), whereas the sign was uncertain for less recurrent types (e.g., N, NW, SE, and W). The projected changes in weather type frequencies in the region can be viewed not only as indicators of change in rainfall response but may also be used to inform impact studies pertinent to water resource planning and management, extreme weather analysis, and agricultural production.