scholarly journals Hydrodynamics of Regional and Seasonal Variations in Congo Basin Precipitation

2021 ◽  
Author(s):  
Kerry H Cook ◽  
Edward K. Vizy
Keyword(s):  
Seasonal Variations ◽  
Meridional Wind ◽  
Winter Precipitation ◽  
Moisture Distribution ◽  
Congo Basin ◽  
Moisture Budget ◽  
Boreal Spring ◽  
Atmospheric Column ◽  
Precipitation Seasonality ◽  
Moist Environment

Abstract The processes that determine the seasonality of precipitation in the Congo Basin are examined using the atmospheric column moisture budget. Studying the fundamental determinants of Congo Basin precipitation seasonality supports process-based studies of variations on all time scales, including those associated with greenhouse gas-induced global warming. Precipitation distributions produced by the ERA5 reanalysis provide sufficient accuracy for this analysis, which requires a consistent dataset to relate the atmospheric dynamics and moisture distribution to the precipitation field. The Northern and Southern Hemisphere regions of the Congo Basin are examined separately to avoid the misconception that Congo Basin rainfall is primarily bimodal. While evapotranspiration is indispensable for providing moisture to the atmospheric column to support precipitation in the Congo Basin, its seasonal variations are small and it does not drive precipitation seasonality. During the equinoctial seasons, precipitation is primarily supported by meridional wind convergence in the moist environment in the 800 hPa to 500 hPa layer where moist air flows into the equatorial trough. Boreal fall rains are stronger than boreal spring rains in both hemispheres because low-level moisture divergence develops in boreal spring in association with the developing Saharan thermal low. The moisture convergence term also dominates the moisture budget during the summer season in both hemispheres, with meridional convergence in the 850-600 hPa layer as cross-equatorial flow interacts with the cyclonic flow about the Angola and Sahara thermal lows. Winter precipitation is low because of dry air advection from the winter hemisphere subtropical highs over the continent.

A MOISTURE STRESS INDEX FOR WHEAT BY MEANS OF A MODULATED SOIL MOISTURE BUDGET

1968 ◽  
Vol 48 (5) ◽  
pp. 535-544 ◽  
Author(s):  
A. R. Mack ◽  
W. S. Ferguson
Keyword(s):  
Soil Moisture ◽  
Stress Function ◽  
Potential Evapotranspiration ◽  
Moisture Stress ◽  
Moisture Distribution ◽  
Stress Index ◽  
Wheat Crop ◽  
Moisture Budget ◽  
Dough Stage ◽  
Soil Moisture Budget

Actual evapotranspiration (AE), soil moisture distribution, and moisture stress for a wheat crop (PE-AE) were estimated by the modulated soil moisture budget of Holmes and Robertson. The estimated soil moisture was reasonably well correlated with soil moisture measured weekly by means of gypsum blocks. Wheat yields from experimental plots in the corresponding area were related more closely to the moisture stress function (PE-AE: r = − 0.83), than to the seasonal precipitation (r = 0.62), the potential evapotranspiration (PE) or the evapotranspiration ratio (AE/PE). Regression analyses showed that the grain yields were reduced by an average of 156 (±sb = 40) kg/ha per cm of moisture stress from emergence to harvest, or by 311 and 69 kg/ha per cm of stress, from the fifth-leaf to the soft-dough stage and from the soft-dough stage to maturity, respectively. The moisture stress function may be used to characterize the soil–plant–atmosphere environment for the growing season of a crop. Precipitation and evapotranspiration data are presented annually for three standardized growing periods at Brandon from 1921 to 1963.

Role of the Atmospheric Moisture Budget in Defining the Precipitation Seasonality of the Great Lakes Region

2021 ◽  
Vol 34 (2) ◽  
pp. 643-657
Author(s):  
Samar Minallah ◽  
Allison L. Steiner
Keyword(s):  
Great Lakes ◽  
Spatial Patterns ◽  
Moisture Transport ◽  
Transport Processes ◽  
Great Lakes Region ◽  
Moisture Budget ◽  
Atmospheric Moisture ◽  
Precipitation Seasonality ◽  
Atmospheric Moisture Budget

AbstractPrecipitation in the Great Lakes region has a distinct seasonal cycle that peaks in early summer, followed by a decline in August and a secondary peak in September. This seasonality is often not captured by models, which necessitates understanding of the driving mechanisms to ascertain the model biases. This study analyzes the atmospheric moisture budget using reanalysis datasets to assess the role of regional evapotranspiration and moisture influx from remote origins in defining the precipitation seasonality, and to understand how the Great Lakes modulate spatial patterns and magnitudes of these components. Specifically, the land–water thermal contrast yields large seasonal variations in the evaporative fluxes and creates distinctive localized spatial patterns of moisture flux divergence. We find considerable month-to-month variations in both evapotranspiration and the net moisture transport through the boundaries, where they play a cooperative (contrasting) role in amplifying (dampening) the moisture content available for precipitation and total precipitable water. Our seasonal analysis suggests that the misrepresentation of the budget quantities in models, for example, in simulation of moisture transport processes and parameterization schemes, can result in an anomalous precipitation behavior and, in some cases, violation of the atmospheric moisture mass balance, resulting in large residual magnitudes. We also identify conspicuous differences in the representation of moisture budget components in the various reanalyses, which can alter their representation of the regional hydroclimates.

The role of winter sublimation in the Arctic moisture budget

2004 ◽  
Vol 35 (4-5) ◽  
pp. 325-334 ◽  
Author(s):  
G.E. Liston ◽  
M. Sturm
Keyword(s):  
Snow Water Equivalent ◽  
Winter Precipitation ◽  
Physical Models ◽  
The Arctic ◽  
Moisture Budget ◽  
Surface Moisture ◽  
Solid Precipitation ◽  
Horizontal Transport ◽  
Snow Water

In the Arctic, the simplest way to describe the winter surface moisture budget (in the absence of any net horizontal transport) is: snow-water-equivalent depth on the ground (D) equals precipitation (P) minus sublimation (S). D, P and S are the most fundamental components of the winter arctic hydrologic cycle and understanding them is essential to understanding arctic moisture-related processes. Unfortunately, accurate solid-precipitation (P) measurements have proven nearly impossible to achieve in the Arctic, because precipitation generally falls when it is windy. Gauge undercatch can range from 55–75% depending on the gauge type and wind conditions. The state of knowledge for winter sublimation (S) is even more limited. There are few actual measurements and most studies have used physical models to estimate this quantity. Moreover, fundamental questions concerning the boundary-layer physics of arctic winter sublimation remain unanswered. Resolving these is essential to closing local, regional, and pan-Arctic moisture budgets because some studies indicate sublimation may be as much as 50% of the total winter precipitation and 35% of the annual precipitation. This paper summarizes and analyzes the existing literature describing arctic sublimation.

scholarly journals Relationship between Winter Precipitation in Barents–Kara Seas and September–October Eastern Siberian Sea Ice Anomalies

2019 ◽  
Vol 9 (6) ◽  
pp. 1091 ◽  
Author(s):  
Jiajun Feng ◽  
Yuanzhi Zhang ◽  
Changqing Ke
Keyword(s):  
Sea Ice ◽  
Meridional Wind ◽  
Winter Precipitation ◽  
Department Of Energy ◽  
The Arctic ◽  
Height Anomaly ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Monthly Average

In this study, we applied the 1988–2017 monthly average sea ice concentration data from the Met Office Hadley Centre and the 1988–2017 monthly average reanalysis data from the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Reanalysis II to analyze the relationship between the winter precipitation in the Barents and Kara Seas (BKS) and the previous autumn eastern Siberian Sea ice anomalies. Through the correlation analysis, we found that the correlation between eastern Siberian Sea ice and the BKS winter precipitation was strongest in September and weakest in November. The results indicated that, when the eastern Siberian Sea ice extent decreased in September–October, a significant positive geopotential height anomaly would occur in the coming winter (December–February) in the Norwegian–Barents region. This result in turn caused anomalies in the northward meridional wind. Consequently, the anomalous water vapor from the mid-latitude Atlantic to the Arctic passed through the Greenland Sea before finally reaching the BKS. The meridional wind also caused the temperature in said seas to increase and the BKS ice to melt, leading to an increase of winter precipitation. We also found that the increase of the Siberian high (SH) in winter was related to the decrease of autumn East Siberian Sea ice extent and the increase of the winter BKS precipitation anomaly. Further research still needs to be refined for this issue in future studies.

Quantitative assessment of precipitation seasonality and summer surface wetness using ombrotrophic sediments from an Arctic Norwegian peatland

2009 ◽  
Vol 72 (3) ◽  
pp. 443-451 ◽  
Author(s):  
Jonathan E. Nichols ◽  
Marie Walcott ◽  
Raymond Bradley ◽  
Jon Pilcher ◽  
Yongsong Huang
Keyword(s):  
North Atlantic ◽  
Vascular Plant ◽  
Winter Precipitation ◽  
Norwegian Sea ◽  
Surface Wetness ◽  
The North ◽  
Ombrotrophic Peatland ◽  
Precipitation Seasonality ◽  
Strong Gradient ◽  
Climate Parameter

AbstractSeasonality of precipitation is an important yet elusive climate parameter in paleoclimatological reconstructions. This parameter can be inferred qualitatively from pollen and other paleoecological methods, but is difficult to assess quantitatively. Here, we have assessed seasonality of precipitation and summer surface wetness using compound specific hydrogen and carbon isotope ratios of vascular plant leaf waxes and Sphagnum biomarkers extracted from the sediments of an ombrotrophic peatland, Bøstad Bog, Nordland, Norway. Our reconstructed precipitation seasonality and surface wetness are consistent with regional vegetation reconstructions. During the early Holocene, 11.5–7.5 ka, Fennoscandia experienced a cool, moist climate. The middle Holocene, 7.5–5.5 ka, was warm and dry, transitioning towards cooler and wetter conditions from the mid-Holocene to the present. Changes in seasonality of precipitation during the Holocene show significant coherence with changes in sea surface temperature in the Norwegian Sea, with higher SST corresponding to greater percentage of winter precipitation. Both high SST in the Norwegian Sea and increased moisture delivery to northern Europe during winter are correlated with a strong gradient between the subpolar low and subtropical high over the North Atlantic (positive North Atlantic Oscillation).

scholarly journals Influence of the Tian Shan on Arid Extratropical Asia

2016 ◽  
Vol 29 (16) ◽  
pp. 5741-5762 ◽  
Author(s):  
Jane Baldwin ◽  
Gabriel Vecchi
Keyword(s):  
Climate Model ◽  
Asian Summer Monsoon ◽  
Summer Precipitation ◽  
Winter Precipitation ◽  
Taklimakan Desert ◽  
The Tibetan Plateau ◽  
Spring Precipitation ◽  
Precipitation Seasonality ◽  
Asian Summer ◽  
Tian Shan

Abstract Arid extratropical Asia (AEA) is bisected at the wetter Tian Shan (a northern offshoot of the Tibetan Plateau) into east and west deserts, each with unique climatological characteristics. The east deserts (~35°–55°N, ~75°–115°E) have a summer precipitation maximum, and the west deserts (~35°–55°N, ~45°–75°E) have a winter–spring precipitation maximum. A new high-resolution (50 km atmosphere–land) global coupled climate model is run with the Tian Shan removed to determine whether these mountains are responsible for the climatological east–west differentiation of AEA. Multicentennial simulations for the Control and NoTianshan runs highlight statistically significant effects of the Tian Shan. Overall, the Tian Shan are found to enhance the precipitation seasonality gradient across AEA, mostly through altering the east deserts. The Tian Shan dramatically change the precipitation seasonality of the Taklimakan Desert directly to its east (the driest part of AEA) by blocking west winter precipitation, enhancing subsidence over this region, and increasing east summer precipitation. The Tian Shan increase east summer precipitation through two mechanisms: 1) orographic precipitation, which is greatest on the eastern edge of the Tian Shan in summer, and 2) remote enhancement of the East Asian summer monsoon through alteration of the larger-scale seasonal mean atmospheric circulation. The decrease in east winter precipitation also generates remote warming of the Altai and Kunlun Shan, mountains northeast and southeast of the Tian Shan, respectively, due to reduction of snow cover and corresponding albedo decrease.

scholarly journals The Predictability of Rainfall over the Greater Horn of Africa. Part II: Prediction of Monthly Rainfall during the Long Rains

2015 ◽  
Vol 16 (5) ◽  
pp. 2001-2012 ◽  
Author(s):  
Sharon E. Nicholson
Keyword(s):  
Summer Rainfall ◽  
Seasonal Prediction ◽  
Meridional Wind ◽  
Lead Times ◽  
Horn Of Africa ◽  
Sea Level Pressure ◽  
Boreal Spring ◽  
Predictive Skill ◽  
Wind Sea ◽  
Greater Horn Of Africa

Abstract Seasonal prediction of the boreal spring rains in the Greater Horn of Africa has been notoriously challenging. Predictability is markedly lower than during the autumnal rainy season. Part I of this article explored predictability at the seasonal scale, using multiple linear regression. However, the three months of the boreal spring season are clearly different climatologically and with respect to the prevailing atmospheric circulation and controls on interannual variability. For that reason, the current study follows up on Part I by examining the predictability of the three months individually. The current study utilizes 1- and 2-month lead times and the results are evaluated via cross validation. This approach provided improved skill for April and May in the equatorial rainfall region, but not for March in this region and not for the region with predominantly summer rainfall. Overall, the best predictors are shown to be atmospheric variables, most often zonal and meridional wind. Sea surface temperatures and sea level pressure provided little predictive skill.

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