scholarly journals Seasonal Predictability and Spatial Coherence of Rainfall Characteristics in the Tropical Setting of Senegal

2006 ◽  
Vol 134 (11) ◽  
pp. 3248-3262 ◽  
Author(s):  
Vincent Moron ◽  
Andrew W. Robertson ◽  
M. Neil Ward
Keyword(s):  
Interannual Variability ◽  
General Circulation ◽  
Degrees Of Freedom ◽  
Regional Scale ◽  
Spatial Coherence ◽  
Daily Rainfall ◽  
Occurrence Frequency ◽  
Seasonal Rainfall ◽  
Mean Intensity ◽  
Rainfall Occurrence

Abstract This study examines space–time characteristics of seasonal rainfall predictability in a tropical region by analyzing observed data and model simulations over Senegal. Predictability is analyzed in terms of the spatial coherence of observed interannual variability at the station scale, and within-ensemble coherence of general circulation model (GCM) simulations with observed sea surface temperatures (SSTs) prescribed. Seasonal mean rainfall anomalies are decomposed in terms of daily rainfall frequency and daily mean intensity. The observed spatial coherence is computed from a 13-station network of daily rainfall during the July–September season 1961–98 in terms of (i) interannual variability of a standardized anomaly index (i.e., the average of the normalized anomalies of each station), (ii) the external variance (i.e., the fraction of common variance among stations), and (iii) the number of spatiotemporal degrees of freedom. Spatial coherence of interannual anomalies across stations is found to be much stronger for seasonal rainfall amount and daily occurrence frequency, compared with daily mean intensity of rainfall. Combinatorial analysis of the station observations suggests that, for occurrence and seasonal amount, the empirical number of spatial degrees of freedom is largely insensitive to the number of stations considered, and is between 3 and 4 for Senegal. For daily mean intensity, by contrast, each station is found to convey almost independent information, and the number of degrees of freedom would be expected to increase for a denser network of stations. The GCM estimates of potential predictability and skill associated with the SST forcing are found to be remarkably consistent with those inferred from the observed spatial coherence: there is a moderate-to-strong skill at reproducing the interannual variations of seasonal amounts and rainfall occurrence, whereas the skill is weak for the mean intensity of rainfall. Over Senegal during July–September, it is concluded that (i) regional-scale seasonal amount and rainfall occurrence frequency are predictable from SSTs, (ii) daily mean intensity of rainfall is spatially incoherent and largely unpredictable at the regional scale, and (iii) point-score estimates of seasonal rainfall predictability and skill are subject to large sampling variability.

scholarly journals Spatial Coherence of Tropical Rainfall at the Regional Scale

2007 ◽  
Vol 20 (21) ◽  
pp. 5244-5263 ◽  
Author(s):  
Vincent Moron ◽  
Andrew W. Robertson ◽  
M. Neil Ward ◽  
Pierre Camberlin
Keyword(s):  
Regional Scale ◽  
Spatial Coherence ◽  
Daily Rainfall ◽  
Frequency Of Occurrence ◽  
Tropical Regions ◽  
Mean Intensity ◽  
Northwestern India ◽  
Rainfall Occurrence ◽  
Rainfall Frequency ◽  
Daily Time

Abstract This study examines the spatial coherence characteristics of daily station observations of rainfall in five tropical regions during the principal rainfall season(s): the Brazilian Nordeste, Senegal, Kenya, northwestern India, and northern Queensland. The rainfall networks include between 9 and 81 stations, and 29–70 seasons of observations. Seasonal-mean rainfall totals are decomposed in terms of daily rainfall frequency (i.e., the number of wet days) and mean intensity (i.e., the mean rainfall amount on wet days). Despite the diverse spatiotemporal sampling, orography, and land cover between regions, three general results emerge. 1) Interannual anomalies of rainfall frequency are usually the most spatially coherent variable, generally followed closely by the seasonal amount, with the daily mean intensity in a distant third place. In some cases, such as northwestern India, which is characterized by large daily rainfall amounts, the frequency of occurrence is much more coherent than the seasonal amount. 2) On daily time scales, the interstation correlations between amounts on wet days always fall to insignificant values beyond a distance of about 100 km. The spatial scale of daily rainfall occurrence is larger and more variable among the networks. 3) The regional-scale signal of the seasonal amount is primarily related to a systematic spatially coherent modulation of the frequency of occurrence.

scholarly journals Probabilistic Projections of Climate Change over China under the SRES A1B Scenario Using 28 AOGCMs

2011 ◽  
Vol 24 (17) ◽  
pp. 4741-4756 ◽  
Author(s):  
Weilin Chen ◽  
Zhihong Jiang ◽  
Laurent Li
Keyword(s):  
Climate Change ◽  
Interannual Variability ◽  
General Circulation ◽  
Regional Scale ◽  
First Century ◽  
Climate System Model ◽  
Huai River ◽  
Twenty First Century ◽  
The Impact ◽  
A1b Scenario

Probabilistic projection of climate change consists of formulating the climate change information in a probabilistic manner at either global or regional scale. This can produce useful results for studies of the impact of climate change impact and change mitigation. In the present study, a simple yet effective approach is proposed with the purpose of producing probabilistic results of climate change over China for the middle and end of the twenty-first century under the Special Report on Emissions Scenarios A1B (SRES A1B) emission scenario. Data from 28 coupled atmosphere–ocean general circulation models (AOGCMs) are used. The methodology consists of ranking the 28 models, based on their ability to simulate climate over China in terms of two model evaluation metrics. Different weights were then given to the models according to their performances in present-day climate. Results of the evaluation for the current climate show that five models that have relatively higher resolutions—namely, the Istituto Nazionale di Geofisica e Vulcanologia ECHAM4 (INGV ECHAM4), the third climate configuration of the Met Office Unified Model (UKMO HadCM3), the CSIRO Mark version 3.5 (Mk3.5), the NCAR Community Climate System Model, version 3 (CCSM3), and the Model for Interdisciplinary Research on Climate 3.2, high-resolution version [MIROC3.2 (hires)]—perform better than others over China. Their corresponding weights (normalized to 1) are 0.289, 0.096, 0.058, 0.048, and 0.044, respectively. Under the A1B scenario, surface air temperature is projected to increase significantly for both the middle and end of the twenty-first century, with larger magnitude over the north and in winter. There are also significant increases in rainfall in the twenty-first century under the A1B scenario, especially for the period 2070–99. As far as the interannual variability is concerned, the most striking feature is that there are high probabilities for the future intensification of interannual variability of precipitation over most of China in both winter and summer. For instance, over the Yangtze–Huai River basin (28°–35°N, 105°–120°E), there is a 60% probability of increased interannual standard deviation of precipitation by 20% in summer, which is much higher than that of the mean precipitation. In general there are small differences between weighted and unweighted projections, but the uncertainties in the projected changes are reduced to some extent after weighting.

scholarly journals Spatial Coherence of Monsoon Onset over Western and Central Sahel (1950–2000)

2009 ◽  
Vol 22 (5) ◽  
pp. 1313-1324 ◽  
Author(s):  
Romain Marteau ◽  
Vincent Moron ◽  
Nathalie Philippon
Keyword(s):  
Burkina Faso ◽  
Interannual Variability ◽  
Large Scale ◽  
Spatial Coherence ◽  
Daily Rainfall ◽  
Local Scale ◽  
Monsoon Onset ◽  
Onset Date ◽  
Dry Spell

Abstract The spatial coherence of boreal monsoon onset over the western and central Sahel (Senegal, Mali, Burkina Faso) is studied through the analysis of daily rainfall data for 103 stations from 1950 to 2000. Onset date is defined using a local agronomic definition, that is, the first wet day (>1 mm) of 1 or 2 consecutive days receiving at least 20 mm without a 7-day dry spell receiving less than 5 mm in the following 20 days. Changing either the length or the amplitude of the initial wet spell, or both, or the length of the following dry spell modifies the long-term mean of local-scale onset date but has only a weak impact either on its interannual variability or its spatial coherence. Onset date exhibits a seasonal progression from southern Burkina Faso (mid-May) to northwestern Senegal and Saharian edges (early August). Interannual variability of the local-scale onset date does not seem to be strongly spatially coherent. The amount of common or covariant signal across the stations is far weaker than the interstation noise at the interannual time scale. In particular, a systematic spatially consistent advance or delay of the onset is hardly observed across the whole western and central Sahel. In consequence, the seasonal predictability of local-scale onset over the western and central Sahel associated, for example, with large-scale sea surface temperatures, is, at best, weak.

scholarly journals Downscaling of Seasonal Rainfall over the Philippines: Dynamical versus Statistical Approaches

2012 ◽  
Vol 140 (4) ◽  
pp. 1204-1218 ◽  
Author(s):  
Andrew W. Robertson ◽  
Jian-Hua Qian ◽  
Michael K. Tippett ◽  
Vincent Moron ◽  
Anthony Lucero
Keyword(s):  
General Circulation ◽  
Spatial Interpolation ◽  
Climate Model ◽  
Daily Rainfall ◽  
Circulation Model ◽  
The Philippines ◽  
Seasonal Rainfall ◽  
Strong Impact ◽  
Wind Fields ◽  
Skill Scores

The additional value derived from a regional climate model (RCM) nested within general circulation model (GCM) seasonal simulations, over and above statistical methods of downscaling, is compared over the Philippines for the April–June monsoon transition season. Spatial interpolation of RCM and GCM gridbox values to station locations is compared with model output statistics (MOS) correction. The anomaly correlation coefficient (ACC) skill at the station scale of seasonal total rainfall is somewhat higher in the RCM compared to the GCM when using spatial interpolation. However, the ACC skills obtained using MOS of the GCM or RCM wind fields are shown to be generally—and rather equally—superior. The ranked probability skill scores (RPSS) are also generally much higher when using MOS, with slightly higher scores in the GCM case. Very high skills were found for MOS correction of daily rainfall frequency as a function of GCM and RCM seasonal-average low-level wind fields, but with no apparent advantage from the RCM. MOS-corrected monsoon onset dates often showed skill values similar to those of seasonal rainfall total, with good skill over the central Philippines. Finally, it is shown that the MOS skills decrease markedly and become inferior to those of spatial interpolation when the length of the 28-yr training set is halved. The results may be region dependent, and the excellent station data coverage and strong impact of ENSO on the Philippines may be factors contributing to the good MOS performance when using the full-length dataset over the Philippines.

scholarly journals Weather Types and Rainfall over Senegal. Part I: Observational Analysis

2008 ◽  
Vol 21 (2) ◽  
pp. 266-287 ◽  
Author(s):  
Vincent Moron ◽  
Andrew W. Robertson ◽  
M. Neil Ward ◽  
Ousmane Ndiaye
Keyword(s):  
Rainfall Variability ◽  
Regional Scale ◽  
Daily Rainfall ◽  
Weather Type ◽  
Seasonal Rainfall ◽  
Boreal Summer ◽  
Frequency Of Occurrence ◽  
Weather Types ◽  
Western Sahel ◽  
Type Frequency

Abstract A k-means cluster analysis is used to summarize unfiltered daily atmospheric variability at regional scale over the western Sahel and eastern tropical North Atlantic during the boreal summer season [July–September (JAS)] 1961–98. The analysis employs zonal and meridional regional wind fields at 925, 700, and 200 hPa from the European Centre for Medium-Range Weather Forecasts reanalyses. An eight-cluster solution is shown to yield an integrated view of the complex regional circulation variability, without the need for explicit time filtering. Five of the weather types identified characterize mostly typical phases of westward-moving wave disturbances, such as African easterly waves (AEWs), and persistent monsoon surges, while the three others describe mostly different stages of the seasonal cycle. Their temporal sequencing describes a systematic monsoonal evolution, together with considerable variability at subseasonal and interannual time scales. Daily rainfall occurrence at 13 gauge stations in Senegal is found to be moderately well conditioned by the eight weather types, with positive rainfall anomalies usually associated with southerly wind anomalies at 925 hPa. Interannual variability of daily rainfall frequency is shown to depend substantially on the frequency of occurrence of weather types specific to the beginning and end of the JAS season, together with the number of persistent monsoon surges over the western Sahel. In contrast, year-to-year changes in the frequency of the weather types mostly associated with westward-moving waves such as AEWs are not found to influence seasonal frequency of occurrence of daily rainfall substantially. The fraction of seasonal rainfall variability related to weather-type frequency is found to have a strong relationship with tropical Pacific sea surface temperatures (SSTs): an El Niño (La Niña) event tends to be associated with a higher (lower) frequency of dry weather types during early and late JAS season with enhanced trade winds over the western Sahel, together with lower (higher) prevalence of persistent monsoon surges. The component of seasonal rainfall variability not related to weather-type frequency is characterized by changes in rainfall probability within each weather type, especially those occurring in the core of the JAS season; it exhibits a larger decadal component that is associated with an SST pattern previously identified with recent observed trends in Sahel rainfall.

scholarly journals Downscaling of Daily Rainfall Occurrence over Northeast Brazil Using a Hidden Markov Model

2004 ◽  
Vol 17 (22) ◽  
pp. 4407-4424 ◽  
Author(s):  
Andrew W. Robertson ◽  
Sergey Kirshner ◽  
Padhraic Smyth
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
General Circulation ◽  
Large Scale ◽  
Hidden Markov ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Northeast Brazil ◽  
Rainfall Occurrence ◽  
Dry Spell

Abstract A hidden Markov model (HMM) is used to describe daily rainfall occurrence at 10 gauge stations in the state of Ceará in northeast Brazil during the February–April wet season 1975–2002. The model assumes that rainfall occurrence is governed by a few discrete states, with Markovian daily transitions between them. Four “hidden” rainfall states are identified. One pair of the states represents wet-versus-dry conditions at all stations, while a second pair of states represents north–south gradients in rainfall occurrence. The estimated daily state-sequence is characterized by a systematic seasonal evolution, together with considerable variability on intraseasonal, interannual, and longer time scales. The first pair of states are shown to be associated with large-scale displacements of the tropical convergence zones, and with teleconnections typical of the El Niño–Southern Oscillation and the North Atlantic Oscillation. A nonhomogeneous HMM (NHMM) is then used to downscale daily precipitation occurrence at the 10 stations, using general circulation model (GCM) simulations of seasonal-mean large-scale precipitation, obtained with historical sea surface temperatures prescribed globally. Interannual variability of the GCM's large-scale precipitation simulation is well correlated with seasonal- and spatial-averaged station rainfall-occurrence data. Simulations from the NHMM are found to be able to reproduce this relationship. The GCM-NHMM simulations are also able to capture quite well interannual changes in daily rainfall occurrence and 10-day dry spell frequencies at some individual stations. It is suggested that the NHMM provides a useful tool (a) to understand the statistics of daily rainfall occurrence at the station level in terms of large-scale atmospheric patterns, and (b) to produce station-scale daily rainfall sequence scenarios for input into crop models, etc.

scholarly journals Variations of oxygen-18 in West Siberian precipitation during the last 50 years

2014 ◽  
Vol 14 (11) ◽  
pp. 5853-5869 ◽  
Author(s):  
M. Butzin ◽  
M. Werner ◽  
V. Masson-Delmotte ◽  
C. Risi ◽  
C. Frankenberg ◽  
...  
Keyword(s):  
Interannual Variability ◽  
Seasonal Cycle ◽  
General Circulation ◽  
Western Siberia ◽  
Regional Scale ◽  
Circulation Model ◽  
Global Network ◽  
Convective Precipitation ◽  
Precipitation Δ18o

Abstract. Global warming is associated with large increases in surface air temperature in Siberia. Here, we apply the isotope-enabled atmospheric general circulation model ECHAM5-wiso to explore the potential of water isotope measurements at a recently opened monitoring station in Kourovka (57.04° N, 59.55° E) in order to successfully trace climate change in western Siberia. Our model is constrained to atmospheric reanalysis fields for the period 1957–2013 to facilitate the comparison with observations of δD in total column water vapour from the GOSAT satellite, and with precipitation δ18O measurements from 15 Russian stations of the Global Network of Isotopes in Precipitation. The model captures the observed Russian climate within reasonable error margins, and displays the observed isotopic gradients associated with increasing continentality and decreasing meridional temperatures. The model also reproduces the observed seasonal cycle of δ18O, which parallels the seasonal cycle of temperature and ranges from −25 ‰ in winter to −5 ‰ in summer. Investigating West Siberian climate and precipitation δ18O variability during the last 50 years, we find long-term increasing trends in temperature and δ18O, while precipitation trends are uncertain. During the last 50 years, winter temperatures have increased by 1.7 °C. The simulated long-term increase of precipitation δ18O is at the detection limit (<1 ‰ per 50 years) but significant. West Siberian climate is characterized by strong interannual variability, which in winter is strongly related to the North Atlantic Oscillation. In winter, regional temperature is the predominant factor controlling δ18O variations on interannual to decadal timescales with a slope of about 0.5 ‰ °C−1. In summer, the interannual variability of δ18O can be attributed to short-term, regional-scale processes such as evaporation and convective precipitation. This finding suggests that precipitation δ18O has the potential to reveal hydrometeorological regime shifts in western Siberia which are otherwise difficult to identify. Focusing on Kourovka, the simulated evolution of temperature, δ18O and, to a smaller extent, precipitation during the last 50 years is synchronous with model results averaged over all of western Siberia, suggesting that this site will be representative to monitor future isotopic changes in the entire region.

scholarly journals Extracting Subseasonal Scenarios: An Alternative Method to Analyze Seasonal Predictability of Regional-Scale Tropical Rainfall

2013 ◽  
Vol 26 (8) ◽  
pp. 2580-2600 ◽  
Author(s):  
Vincent Moron ◽  
Pierre Camberlin ◽  
Andrew W. Robertson
Keyword(s):  
Rainy Season ◽  
Early Stage ◽  
Regional Scale ◽  
Spatial Coherence ◽  
Daily Rainfall ◽  
Seasonal Prediction ◽  
East African ◽  
Seasonal Predictability ◽  
Potential Predictability ◽  
Noise Component

Abstract Current seasonal prediction of rainfall typically focuses on 3-month rainfall totals at regional scale. This temporal summation reduces the noise related to smaller-scale weather variability but also implicitly emphasizes the peak of the climatological seasonal cycle of rainfall. This approach may hide potentially predictable signals when rainfall is lower: for example, near the onset or cessation of the rainy season. The authors illustrate such a case for the East African long rains (March–May) on a network of 36 stations in Kenya and north Tanzania from 1961 to 2001. Spatial coherence and potential predictability of seasonal rainfall anomalies associated with tropical sea surface temperature (SST) anomalies clearly peak during the early stage of the rainy season (in March), while the largest rainfall (in April and May) is far less spatially coherent; the latter is shown to contain a large noise component at the station scale that characterizes interannual variability of the March–May seasonal total amounts. Combining the empirical orthogonal function of both interannual and subseasonal variations with a fuzzy k-means clustering is shown to capture the most spatially coherent subseasonal “scenarios” that tend to filter out the noisier variations of the rainfall field and emphasize the most consistent signals in both time and space. This approach is shown to provide insight into the seasonal predictability of long dry spells and heavy daily rainfall events at local scale and their subseasonal modulation.

scholarly journals Potential Predictability of Interannual Variability in the Kuroshio Extension Jet Speed in an Eddy-Resolving OGCM

2012 ◽  
Vol 25 (10) ◽  
pp. 3645-3652 ◽  
Author(s):  
Masami Nonaka ◽  
Hideharu Sasaki ◽  
Bunmei Taguchi ◽  
Hisashi Nakamura
Keyword(s):  
Interannual Variability ◽  
General Circulation ◽  
Degrees Of Freedom ◽  
North Pacific Ocean ◽  
Kuroshio Extension ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Propagation Time ◽  
Potential Predictability ◽  
The Kuroshio

AbstractVariability in the Kuroshio Extension (KE) jet speed has been considered to impact the upper-ocean ecosystem. This study investigates potential predictability of interannual variability in the KE jet speed that could arise from the propagation time of wind-driven Rossby waves as suggested by previous studies, through prediction experiments with an eddy-resolving ocean general circulation model (OGCM) under the perfect-model assumption. Despite the small number of experiments available because of limited computational resources, the prediction experiments with no anomalous atmospheric forcing suggest some predictability for not only broad-scale sea surface height anomalies (SSHAs) but also the frontal-scale KE jet speed. The predictability is confirmed in a 60-yr hindcast OGCM integration as a significantly high correlation (r = 0.68) of 13-month running mean time series of the anomalous KE jet speed with SSHAs that appear in the central North Pacific Ocean 3 yr earlier. Although with fewer degrees of freedom, the same lag relationship can be found between satellite-measured SSHAs and the geostrophically derived KE jet speed.

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