Winter Season Precipitation Research Articles

Western disturbances alter the trend of winter precipitation and its extremes over Northwest Himalayas: Kashmir Himalaya.

The perception of spatio-temporal variability of winter precipitation covering dominant water resources and environment over Kashmir Himalaya is a pre-eminent perspective under current social interests. The main objectives of the study are, firstly, to unriddle the whole (1980-2020) and post-2000 trend in the winter season precipitation (WP), frequency of low-pressure systems (Lp-S), and western disturbances (WDs) to understand the footprints of climate change over the region; secondly, to evaluate the spatio-temporal variability of winter precipitation under the dominance of global teleconnection indices, using both station and reanalysis datasets. Modified Mann-Kendell test and a linear regression model were utilized to extract the trend and significance in WP, Lp-S, and WDs. The results reveal that WP shows an insignificant decreasing trend (1980-2020), with an increasing trend post-2000, while a decreasing trend is seen during post-2000 in the frequency of Lp-S and WDs. The contribution of WP to annual precipitation shows decadal variability with a decreasing trend post-2000. The anatomy of winter extreme precipitation days has shown a decreasing trend (1980-2020), with a robust coupled increasing trend with WP post-2000. The dynamics of ten severe WD-based extreme precipitation events show that a cyclonic circulation and trough connects the Arabian Sea and the Himalayas, bringing additional moisture to the study region and resulting in extreme precipitation. The cogency of global teleconnections on the variability of WP shows that a pattern with positive phase of NAO, ENSO, and a negative phase in Siberian high intensifies the WD-based precipitation. The study discovers its applicability for the regional forecast system in predicting the winters.

Using the snow-day fraction to measure climatic change in southern Ontario (Canada): historical trends in winter season precipitation phase

Global temperatures are increasing, and regional precipitation patterns are changing. Snow is an excellent indicator of regional climate change; 50 years of temperature and precipitation data were analysed from weather stations located within the five most populated cities of Ontario (Canada). Recorded measurements for temperature and precipitation were converted into binary values to indicate the frequency of rain days, snow days, wet days (when total precipitation is greater than 0.2 mm) and freezing days (when the average temperature is less than 0 °C); then, these values were summed over each winter season from 1970/71 to 2019/20. The snow-day fraction was calculated from the seasonal totals by dividing the total number of snow days by the total number of wet days. Historical trends were detected using Pearson’s R, Kendall’s Tau and Spearman’s Rho. Differences in mean values between the first decade (1971–1980) and the last decade (2011–2020) within the time series for the snow-day fraction and total freezing days were determined using Student’s t-tests. During the winter season in southern Ontario (December 1 to March 31), total snow days, total wet days, the snow-day fraction and freezing days were all decreasing at statistically significant rates (90 to 99% confidence levels) across four of the five cities studied (Toronto, Ottawa, Hamilton and London). Mississauga was the exception, being the only city where rain days were increasing, but no trends were detected for snow days or wet days. The snow-day fraction was decreasing in Mississauga but not at a statistically significant rate, despite freezing days decreasing at the greatest rate compared to the other four cities. Total freezing days were highly correlated with the snow-day fraction during the winter season, being able to explain 61 to 76 percent of the observed variability, where Mississauga recorded the weakest correlation and London recorded the strongest correlation.

Numerical Simulation of Winter Precipitation over the Western Himalayas Using a Weather Research and Forecasting Model during 2001–2016

In the present study, dynamically downscaled Weather Research and Forecasting (WRF) model simulations of winter (DJF) seasonal precipitation were evaluated over the Western Himalayas (WH) at grey zone configurations (at horizontal resolutions of 15 km (D01) and 5 km (D02)) and further validated using satellite-based (IMERG; 0.1°), observational (IMD; 0.25°), and reanalysis (ERA5; 0.25° and IMDAA; 0.108°) gridded datasets during 2001–2016. The findings demonstrate that both model resolutions (D01 and D02) are effective at representing precipitation characteristics over the Himalayan foothills. Precipitation features over the region, on the other hand, are much clearer and more detailed, with a significant improvement in D02, emphasizing the advantages of higher model grid resolution. Strong correlations and the lowest biases and root mean square errors indicate a closer agreement between model simulations and reanalyses IMDAA and ERA5. Vertical structures of various dynamical and thermodynamical features further confirm the improved and more realistic in WRF simulations with D02. Moreover, the seasonal patterns of upper tropospheric circulation, vertically integrated moisture transport, surface temperature and cloud cover show more realistic simulation in D02 compared to coarser domain D01. The categorical statistics reveal the efficiency of both D01 and D02 in simulating moderate and heavy precipitation events. Overall, our study emphasizes the significance of high-resolution data for simulating precipitation features specifically over complex terrains like WH.

Persistent warm and dry extremes over the eastern Mediterranean during winter: The role of North Atlantic blocking and central Mediterranean cyclones

AbstractPersistent dry winter events over the eastern Mediterranean (EM) disrupt the rainy (winter) season precipitation patterns and dramatically reduce water availability in the region. Here we objectively identify persistent dry, warm winter events over Israel, and apply a Lagrangian approach to three case‐studies, aiming to understand the relation between the synoptic setting, precursor Rossby waves, and how the dry, warm conditions emerge. Self‐organizing map classification of atmospheric profile data over Israel shows that the most persistent (at least 5 days) dry and warm winter events are induced by a stagnant upper tropospheric ridge over the EM, a pronounced trough or cut‐off low over the western/central Mediterranean and blocking over the North Atlantic. The leading mechanisms of the warm and dry conditions are: adiabatic heating during slantwise subsidence, heating by sensible heat fluxes from the surface, and advection of warm and dry continental air. The relative contributions of the mechanisms and geographical locations of the back trajectories vary greatly both within and among the events. In addition, the Atlantic blocking and EM ridge are supported by upstream diabatic heating in warm conveyor belts of North Atlantic cyclones and Mediterranean cyclones, respectively. A quantitative classification of the back trajectories above the Atlantic and the Mediterranean has shown transitions from adiabatic to diabatic contributions and vice versa along these paths. The sequential relation between Atlantic ridge (or block), trough over Europe and ridge over EM and/or west Russia places local persistent warm and dry extremes into a large‐scale context and thus provides new opportunities for understanding their predictability at weather and climate scales.

Simulation of ENSO Teleconnections to Precipitation Extremes over the United States in the High-Resolution Version of E3SM

Abstract We evaluate the simulated teleconnection of El Niño–Southern Oscillation (ENSO) to winter season precipitation extremes over the United States in a long (98 years) 1950 control high-resolution version (HR; 25-km nominal atmosphere model horizontal resolution) of the U.S. Department of Energy’s (DOE) Energy Exascale Earth System Model version 1 (E3SMv1). The model bias and spatial pattern of ENSO teleconnections to mean and extreme precipitation in HR overall are similar to the low-resolution model’s (LR; 110 km) historical simulation (four-member ensemble, 1925–59). However, over the southeastern United States (SE-U.S.), HR produces stronger El Niño–associated extremes, reducing LR’s model bias. Both LR and HR produce weaker than observed increase in storm track activity during El Niño events there, but HR improves the ENSO-associated variability of moisture transport over SE-U.S. During El Niño, stronger vertical velocities in HR produce stronger large-scale precipitation, causing larger latent heating of the troposphere that pulls in more moisture from the Gulf of Mexico into the SE-U.S. This positive feedback also contributes to the stronger mean and extreme precipitation response in HR. Over the Pacific Northwest, LR’s bias of stronger than observed La Niña associated extremes is amplified in HR. Both models simulate stronger than observed moisture transport from the Pacific Ocean into the region during La Niña years. The amplified HR bias there is due to stronger orographically driven vertical updrafts that create stronger large-scale precipitation, despite weaker La Niña–induced storm track activity. Significance Statement New high-resolution Earth system models (ESMs) solve mathematical equations of fluid flow at much smaller spatial scales than prevalent ESMs, and thus are prohibitively expensive to compute. However, they can be useful for simulating accurate details of regional climate extremes that are driven by naturally occurring climate oscillations like El Niño–Southern Oscillation (ENSO). Here, we evaluate the simulation of ENSO-driven precipitation extremes over the United States in the high-resolution version of the U.S. Department of Energy’s new Energy Exascale Earth System Model version 1. We find that the high-resolution model improves upon its low-resolution counterpart over the southeastern United States by producing a better transport of moisture into the region from the Gulf of Mexico during El Niño. Over the U.S. Pacific Northwest, the high-resolution model simulates the atmospheric flow in more detail over the complex mountainous terrain. However, it also brings in more moisture from the Pacific Ocean just like the low-resolution model. This causes it to produce precipitation extremes during La Niña years there that are stronger than that observed in the real world.

Effect of increased greenhouse gas concentration on mean, extreme, and timing of precipitation over Arizona (USA)

AbstractWe use a combination of a regional climate model (RCM) and a global climate model (GCM) to explore potential changes to the complex precipitation regime in the semi‐arid and arid state of Arizona in the American Southwest. The RCM output for the contemporary period (2000–2009) compares well with a gridded precipitation dataset with respect to seasonality, amount, intensity, and diurnal patterns. Output from the GCM forced by the continued buildup of greenhouse gases was dynamically downscaled by the RCM for the period 2090–2099. Results indicate an increase in winter precipitation of 1–2 mm day−1 in the mountainous areas of the state with somewhat smaller increases for the summer and fall seasons; negligible changes were projected for spring precipitation. Extreme precipitation is projected to increase across much of the state in winter (10–30 mm day−1) and to a lesser extent in spring. Our results indicate an increase in the 99th percentile of winter season precipitation. However, we note there is substantial seasonal dependency: statewide‐averaged winter season precipitation is projected to undergo greater frequency of wet than dry extreme events, whereas the statewide‐averaged summer, fall, and spring seasons, broadly demonstrate an equal likelihood of increased and decreased extreme precipitation. While the RCM captured the unusual observed night‐time maximum in summer rainfall in the centre of the state, our results do not indicate any change in the diurnal character or sub‐diurnal duration (6–18 hr) of precipitation over the next 100 years.

Climate change detection and attribution in the Ganga-Brahmaputra-Meghna river basins

Ganga-Brahmaputra-Meghna (GBM) river basin is the third-largest and one of the most populated river basins in the world. As climate change is affecting most of the hydrometeorological variables across the globe, this study investigated the existence of climate change signal in all four climatological seasons in the GBM river basin and assessed the contribution of anthropogenic activities, i.e., Greenhouse Gases (GHGs) emission in the change. Significant decreasing trends in the monsoon and a small increase in pre-monsoon precipitation were observed. Negligible change was detected in post-monsoon and winter season precipitation. CMIP5 GCMs were used for climate change detection, change point estimation, and attribution studies. Support Vector Machine (SVM) regression method was adopted to downscale GCM variables at the local scale. Monte-Carlo simulation approach was used to detect changes in different seasons. The climate change ‘signals’ were detectable after the year 1980 using Signal to Noise ratio (SNR) method in the majority of central and north-western regions. The change point was detectable only in annual monsoon precipitation at the basin level. Attribution analysis indicated >50% contribution of anthropogenic activities (GHGs) to annual monsoon precipitation changes. So, there is high confidence that monsoon precipitation in GBM has significantly changed due to anthropogenic activities. Different mitigation and adaption measures are also suggested, which may be adopted to manage the growing demand and water availability in the basin.

Impact of Urbanization on the Nature of Precipitation at Toronto, Ontario, Canada

AbstractA newly developed precipitation phase metric is used to detect the impact of urbanization on the nature of precipitation at Toronto, Ontario, Canada, by contrasting the relative amounts of rain and snow. A total of 162 years of observed precipitation data were analyzed to classify the nature of winter-season precipitation for the city of Toronto. In addition, shorter records were examined for nearby climate stations in less-urbanized areas in and near Toronto. For Toronto, all winters from 1849 to 2010 as well as three climate normal periods (1961–90, 1971–2000, and 1981–2010) were thus categorized for the Toronto climate record. The results show that Toronto winters have become increasingly “rainy” across these time periods in a statistically significant fashion, consistent with a warming climate. Toronto was compared with the other less urban sites to tease out the impacts of the urban heat island from larger-scale warming. This yielded an estimate of 19%–27% of the Toronto shift in precipitation type (from snow to rain) that can be attributed to urbanization for coincident time periods. Other regions characterized by similar climates and urbanization with temperatures near the freezing point are likely to experience similar climatic changes expressed as a change in the phase of winter-season precipitation.

The Shifting Scales of Western U.S. Landfalling Atmospheric Rivers Under Climate Change

AbstractAtmospheric rivers (ARs) can be a boon and bane to water resource managers as they have the ability to replenish water reserves, but they can also generate million‐to‐billion‐dollar flood damages. To investigate how anthropogenic climate change may influence AR characteristics in the coastal western United States by end century, we employ a suite of novel tools such as variable resolution in the Community Earth System Model (VR‐CESM), the TempestExtremes AR detection algorithm, and the Ralph, Rutz, et al. (2019, https://doi.org/10.1175/BAMS‐D‐18‐0023.1) AR category scale. We show that end‐century ARs primarily shift from being “mostly or primarily beneficial” to “mostly or primarily hazardous” with a concomitant sharpening and intensification of winter season precipitation totals. Changes in precipitation totals are due to a significant increase in AR (+260%) rather than non‐AR (+7%) precipitation, largely through increases in the most intense category of AR events and a decrease in the interval between landfalling ARs.

Quantifying the Changing Nature of the Winter Season Precipitation Phase from 1849 to 2017 in Downtown Toronto (Canada)

One hundred and sixty–nine years of weather station data were analyzed to quantify the changing nature of the winter season precipitation phase in the downtown area of Toronto (Canada). The precipitation variables examined were rainfall, snowfall water equivalent, total precipitation, rain days, snow days, and precipitation days. From these precipitation variables, three precipitation phase metrics were constructed for further analysis: the fraction of total precipitation that fell as snow, the fraction of precipitation days that recorded snow, and finally, the precipitation phase index (PPI) derived from comparing the rainfall to the snowfall water equivalent. Snowfall and snow days were decreasing at the most significant rate over this time period, and although rain days were increasing, total precipitation and precipitation days were also decreasing at a statistically significant rate. All three precipitation phase metrics suggest that winters are becoming less snowy in Toronto’s urban center. We also looked at trends and changes in average winter season temperatures to explore correlations between warming temperatures and changes in the winter season precipitation phase. Of the three precipitation phase metrics considered, the ratio of snow days to precipitation days recorded the strongest time series trend and the strongest correlation with warming temperatures.

Seasonal estimates of the impacts of aerosol and dust pollution on orographic precipitation in the Colorado River Basin

ABSTRACT The cumulative effect of anthropogenic aerosol pollution acting primarily as cloud condensation nuclei (CCN) and dust acting as CCN, giant CCN, and ice nuclei (IN) is examined in this study, which covers the entire Colorado Rocky Mountains from the months of October to April in the year 2004–2005. The ~6.5-month analysis provides a range of snowfall totals and variability in concentrations of anthropogenic aerosol pollution and dust. This research quantifies the impacts of both dust and pollution aerosols on wintertime precipitation in the Colorado River Basin (CRB) using the Regional Atmospheric Modeling System (RAMS). In general, anthropogenic aerosol pollution reduces water resources in the CRB via the “spill-over” effect by enhancing cloud droplet concentrations and reducing riming, while dust enhances precipitation primarily by acting as IN. Dust is more episodic and anthropogenic aerosol is more pervasive throughout the winter season. Combined response to dust and anthropogenic aerosol is a net reduction of water resources in the CRB. The question is by how much are those water resources affected? We estimate that total winter-season precipitation loss for CRB in 2004–2005 due to the combined influence of anthropogenic aerosol and dust was 5,380,00 acre-feet of water (66.36 x 106 m3).

Predictable and Unpredictable Aspects of U.S. West Coast Rainfall and El Niño: Understanding the 2015/16 Event

AbstractCalifornia experienced record-setting drought from 2012 to 2017. Based on both seasonal forecast models and historical associations, there was widespread expectation that the major El Niño event of 2015/16 would result in increased winter-season precipitation and break the drought. However, the 2015/16 winter rainy season ultimately resulted in slightly below-average precipitation and the drought continued. In this work we analyze data from both observations and seasonal forecasts made as part of the North American Multi-Model Ensemble (NMME) to better understand the general relationship between El Niño and U.S. West Coast rainfall, focusing on Southern California (SOCAL) rainfall, Pacific Northwest (PNW) rainfall, and the 2015/16 event. We find that while there is a statistically significant positive correlation between El Niño events and the SOCAL and PNW rainfall anomalies, this relationship explains at most one-third of the observed variance. Examination of hindcasts from the NMME demonstrates that the models are capable of accurately reproducing this observed correlation between tropical Pacific sea surface temperatures and California rainfall when information from the individual ensemble members is retained. However, focusing on the multimodel ensemble mean, which deliberately reduces the influence of unpredicted variability, drastically overestimates the strength of this relationship. Our analysis demonstrates that much of the winter rainfall variability along the U.S. West Coast is dominated by unpredicted variations in the 200-hPa height field and that this same unpredicted variability was largely responsible for the unexpectedly dry conditions in 2015/16.

High-elevation mountain hemlock growth as a surrogate for cool-season precipitation in Crater Lake National Park, USA

Snow dominates the hydrology and climate of the United States’ central Pacific Coast, but because local measurements of snowpack and winter precipitation often extend back only a few decades, observations by themselves are not adequate to describe potential amplitude of wintertime conditions. Here we present a set of updated and extended mountain hemlock (Tsuga mertensiana [Bong.] Carr.) tree-ring width records from Crater Lake National Park, Oregon, and use these data to make inferences about snowpack prior to the start of instrumental monitoring. In July and August 2013, we collected cores from 228 trees at seven high-elevation hemlock stands that surround the crater’s rim. The oldest tree had an inner ring date of CE 1474, and the longest ring-width chronology maintained a satisfactory common signal back to the middle of the 16th century. The growth of high-elevation mountain hemlock is strongly and inversely related to cool-season precipitation, making these records some of the most southerly examples of a robust inverse cool-season moisture signal in North American tree rings. The growth of these snow-limited forests does not appear to have been affected by the substantial decline in spring snowpack observed in the past two decades across the broader Cascade Range, and we did not find any indication of changing relationships between tree growth and either monthly or seasonal winter precipitation since the early 1990s. The exceptional three-year sequence in Crater Lake tree rings between CE 1809 and 1811, which includes the narrowest ring since CE 1540 and anatomical abnormalities produced by cold weather, leads us to conclude that 1809–1810 was the most snowy and severe winter to affect south-central Oregon during the past four and a half centuries.

A 305-year continuous monthly rainfall series for the island of Ireland (1711–2016)

Abstract. A continuous 305-year (1711–2016) monthly rainfall series (IoI_1711) is created for the Island of Ireland. The post 1850 series draws on an existing quality assured rainfall network for Ireland, while pre-1850 values come from instrumental and documentary series compiled, but not published by the UK Met Office. The series is evaluated by comparison with independent long-term observations and reconstructions of precipitation, temperature and circulation indices from across the British–Irish Isles. Strong decadal consistency of IoI_1711 with other long-term observations is evident throughout the annual, boreal spring and autumn series. Annually, the most recent decade (2006–2015) is found to be the wettest in over 300 years. The winter series is probably too dry between the 1740s and 1780s, but strong consistency with other long-term observations strengthens confidence from 1790 onwards. The IoI_1711 series has remarkably wet winters during the 1730s, concurrent with a period of strong westerly airflow, glacial advance throughout Scandinavia and near unprecedented warmth in the Central England Temperature record – all consistent with a strongly positive phase of the North Atlantic Oscillation. Unusually wet summers occurred in the 1750s, consistent with proxy (tree-ring) reconstructions of summer precipitation in the region. Our analysis shows that inter-decadal variability of precipitation is much larger than previously thought, while relationships with key modes of climate variability are time-variant. The IoI_1711 series reveals statistically significant multi-centennial trends in winter (increasing) and summer (decreasing) seasonal precipitation. However, given uncertainties in the early winter record, the former finding should be regarded as tentative. The derived record, one of the longest continuous series in Europe, offers valuable insights for understanding multi-decadal and centennial rainfall variability in Ireland, and provides a firm basis for benchmarking other long-term records and reconstructions of past climate. Correlation of Irish rainfall with other parts of Europe increases the utility of the series for understanding historical climate in further regions.

Miocene vegetation shift and climate change: Evidence from the Siwalik of Nepal

Abstract We reconstruct climate and vegetation applying the Coexistence Approach (CA) methodology on two palaeofloras recovered from the Lower (middle Miocene; ~ 13–11 Ma) and Middle Siwalik (late Miocene; 9.5–6.8 Ma) sediments of Surai Khola section, Nepal. The reconstructed mean annual temperature (MAT) and cold month mean temperature (CMT) show an increasing trend, while warm month mean temperature (WMT) remains nearly the same during the period. The reconstructed precipitation data indicates that the summer monsoon precipitation was nearly the same during the middle and late Miocene, while the winter season precipitation significantly decreased in the late Miocene. The overall precipitation infers increased rainfall seasonality during the late Miocene. The vegetation during the middle Miocene was dominated by wet evergreen taxa, whereas deciduous ones increased significantly during the late Miocene. The reconstructed climate data indicates that high temperature and significantly low precipitation during the winter season (dry season) in the late Miocene might have enhanced forest fire which favoured the expansion of C4 plants over C3 plants during the period. This idea gets further support not only from a recent forest fire in northern India that was caused by the weakening of winter precipitation, but also from the burnt wood recovered from the late Miocene Siwalik sediments of northern India.

Extreme reversals in successive winter season precipitation anomalies across the Western United States, 1895–2015

ABSTRACTExtreme precipitation studies commonly link anomalies to specific episodes of drought or flooding, while studies of precipitation volatility typically employ statistical measures of variability. This study addresses the concept of an extreme precipitation reversal (EPR), defined as an unusually wet or dry period that precedes a large precipitation anomaly of opposite sign, or more specifically the difference in the percentiles of precipitation for consecutive periods. The focus of this study is EPRs over the past 121 years for the hydrologically critical winter season across the Western United States. Results show that EPR values are of a greater magnitude during the second half of the historical record than during the first half, indicating increased volatility in extreme winter‐to‐winter precipitation. Historically, the direction of reversals (wet‐to‐dry or dry‐to‐wet) corresponds to a similar reversal in the nature of the El Niño‐Southern Oscillation (ENSO) phenomenon, although EPRs are not systematically aligned with extreme ENSO reversals. While a greater frequency and intensity of winter season EPRs in recent years is potentially problematic for water managers, the relationship with ENSO may afford proactive management and mitigation of the impacts.

Downscaled climate change projections for the Hindu Kush Himalayan region using CORDEX South Asia regional climate models

This study assessed the regional climate models (RCMs) employed in the Coordinated Regional climate Downscaling Experiment (CORDEX) South Asia framework to investigate the qualitative aspects of future change in seasonal mean near surface air temperature and precipitation over the Hindu Kush Himalayan (HKH) region. These RCMs downscaled a subset of atmosphere ocean coupled global climate models (AOGCMs) in the Coupled Model Intercomparison Project phase 5 (CMIP5) to higher 50 km spatial resolution over a large domain covering South Asia for two representation concentration pathways (RCP4.5 and RCP8.5) future scenarios. The analysis specifically examined and evaluated multi-model and multi-scenario climate change projections over the hilly sub-regions within HKH for the near-future (2036–2065) and far-future (2066–2095) periods. The downscaled multi-RCMs provide relatively better confidence than their driving AOGCMs in projecting the magnitude of seasonal warming for the hilly sub-region within the Karakoram and northwestern Himalaya, with higher projected change of 5.4 °C during winter than of 4.9 °C during summer monsoon season by the end of 21st century under the high-end emissions (RCP8.5) scenario. There is less agreement among these RCMs on the magnitude of the projected warming over the other sub-regions within HKH for both seasons, particularly associated with higher RCM uncertainty for the hilly sub-region within the central Himalaya. The downscaled multi-RCMs show good consensus and low RCM uncertainty in projecting that the summer monsoon precipitation will intensify by about 22% in the hilly sub-region within the southeastern Himalaya and Tibetan Plateau for the far-future period under the RCP8.5 scenario. There is low confidence in the projected changes in the summer monsoon and winter season precipitation over the central Himalaya and in the Karakoram and northwestern Himalaya due to poor consensus and moderate to high RCM uncertainty among the downscaled multi-RCMs. Finally, the RCM related uncertainty is found to be large for the projected changes in seasonal temperature and precipitation over the hilly sub-regions within HKH by the end of this century, suggesting that improving the regional processes and feedbacks in RCMs are essential for narrowing the uncertainty, and for providing more reliable regional climate change projections suitable for impact assessments in HKH region.

High-resolution long-term WRF climate simulations over Volta Basin. Part 1: validation analysis for temperature and precipitation

A 26-year simulation (1980–2005) was performed with the Weather Research and Forecast (WRF) model over the Volta Basin in West Africa. This was to investigate the ability of a climate version of WRF to reproduce present day temperature and precipitation over the Volta Basin. The ERA-Interim reanalysis and one realization of the ECHAM6 global circulation model (GCM) data were dynamically downscaled using two nested domains within the WRF model. The outer domain had a horizontal resolution of 50 km and covered the whole of West Africa while the inner domain had a horizontal resolution of 10 km. It was observed that biases in the respective forcing data were carried over to the RCM, but also the RCM itself contributed to the mean bias of the model. Also, the biases in the 50-km domain were transferred unchanged, especially in the case of temperature, to the 10-km domain, but, for precipitation, the higher-resolution simulations increased the mean bias in some cases. While in general, WRF underestimated temperature in both the outer (mean biases of −1.6 and −2.3 K for ERA-Interim and ECHAM6, respectively) and the inner (mean biases of −0.9 K for the reanalysis and −1.8 K for the GCM) domains, WRF slightly underestimated precipitation in the coarser domain but overestimated precipitation in the finer domain over the Volta Basin. The performance of the GCM, in general, is good, particularly for temperature with mean bias of −0.7 K over the outer domain. However, for precipitation, the added value of the RCM cannot be overlooked, especially over the whole West African region on the annual time scale (mean biases of −3% for WRF and −8% for ECHAM6). Over the whole Volta Basin and the Soudano-Sahel for the month of April and spring (MAM) rainfall, respectively, mean bias close to 0% was simulated. Biases in the interannual variability in both temperature and precipitation over the basin were smaller in the WRF than the ECHAM6. High spatial pattern correlations between 0.7 and 0.8 were achieved for the autumn precipitation and low spatial correlation in the range of 0.0 and 0.2 for the winter season precipitation over the whole basin and all the three belts over the basin.

Multidecadal Changes in the Relationship of Storm Frequency over Euro-Mediterranean Region and ENSO During Boreal Winter

BackgroundThe El Niño–Southern Oscillation (ENSO) influence over Euro-Mediterranean boreal winter season (December to February) precipitation anomalies has changed along the twentieth century.PurposeThe precipitation anomalies are closely associated with storm track frequency. The changing relationship between the interannual variability of storm track frequency and ENSO is investigated at interannual and multidecadal timescales.MethodsThe Melbourne University cyclone tracking scheme (MS) is applied to reanalyse 200-hPa geopotential height (Z200) datasets to track the winter storms over the Euro-Mediterranean region for the period 1950–2016.ResultsThe maximum of climatological storm track frequency is found to prevail in the study domain with large interannual variability. The multidecadal changes show a phase shift in the relationship between storm track frequency and ENSO from the 1950s to the recent decades. The storm track frequency is negatively correlated (−0.24) to ENSO for the early period (1950–1979), whereas it is positively correlated with a maximum correlation value of 0.44 for the recent period (1987–2016). The correlation departure (0.68) between the two periods shows a significant change in the multidecadal relationship between storm track frequency and ENSO.ConclusionThe ENSO-associated significant changes are noted in the upper and lower tropospheric height anomalies over the North Atlantic and Euro-Mediterranean sector in the recent period with respect to the earlier period, which may largely influencing the storm activity (Frequency/Intensity) and precipitation anomalies over the Euro-Mediterranean region. The findings of this study can have important implications in Euro-Mediterranean seasonal predictability.

Probability of occurrence of monthly and seasonal winter precipitation over Northwest India based on antecedent-monthly precipitation

Winter (December, January, and February (DJF)) precipitation over northwest India (NWI) is mainly associated with the eastward moving mid-latitude synoptic systems, western disturbances (WDs), embedded within the subtropical westerly jet (SWJ), and is crucial for Rabi (DJF) crops. In this study, the role of winter precipitation at seasonal and monthly scale over NWI and its nine meteorological subdivisions has been analyzed. High-resolution (0.25° × 0.25°) gridded precipitation data set of India Meteorological Department (IMD) for the period of 1901–2013 is used. Results indicated that the seasonal precipitation over NWI is below (above) the long-term mean in most of the years, when precipitation in any of the month (December/January/February) is in deficit (excess). The contribution of December precipitation (15–20%) to the seasonal (DJF) precipitation is lesser than January (35–40%) and February (35–50%) over all the subdivisions. December (0.60), January (0.57), and February (0.69) precipitation is in-phase (correlation) with the corresponding winter season precipitation. However, January precipitation is not in-phase with the corresponding December (0.083) and February (−0.03) precipitation, while December is in-phase with the February (0.21). When monthly precipitation (December or January or December–January or February) at subdivision level over NWI is excess (deficit); then, the probability of occurrence of seasonal excess (deficit) precipitation is high (almost nil). When antecedent-monthly precipitation is a deficit or excess, the probability of monthly (January or February or January + February) precipitation to be a normal category is >60% over all the subdivisions. This study concludes that the December precipitation is a good indicator to estimate the performance of January, February, January–February, and the seasonal (DJF) precipitation.