Roles of Remote and Local Forcings in the Variation and Prediction of Regional Maritime Continent Rainfall in Wet and Dry Seasons

2016 ◽  
Vol 29 (24) ◽  
pp. 8871-8879 ◽  
Author(s):  
Tuantuan Zhang ◽  
Song Yang ◽  
Xingwen Jiang ◽  
Bohua Huang
Keyword(s):  
Late Summer ◽  
Seasonal Prediction ◽  
Early Spring ◽  
Dry Season ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Wet Season ◽  
Maritime Continent ◽  
Rainfall Prediction ◽  
Relationship Of

Abstract Seasonal prediction of extratropical climate (e.g., the East Asian climate) is partly dependent upon the prediction skill for rainfall over the Maritime Continent (MC). A previous study by the authors found that the NCEP Climate Forecast System, version 2 (CFSv2), had difference in skill between predicting rainfall over the western MC (WMC) and the eastern MC (EMC), especially in the wet season. In this study, the potential mechanisms for this phenomenon are examined. It is shown that observationally in the wet season (from boreal winter to early spring) the EMC rainfall is closely linked to both ENSO and local sea surface temperature (SST) anomalies, whereas the WMC rainfall is only moderately correlated with ENSO. The model hindcast unrealistically predicts the relationship of the WMC rainfall with local SST and ENSO (even opposite to the observed feature), which contributes to lower prediction skill for the WMC rainfall. In the dry season (from boreal late summer to fall), the rainfall over the entire MC is significantly influenced by both ENSO and local SST in observations and this feature is well captured by the CFSv2. Therefore, the hindcasts do not show apparently different skill in rainfall prediction for EMC and WMC in the dry season. The possible roles of atmospheric internal processes are also discussed.

scholarly journals Seasonality of Mansonia titillans during dam construction, Biritiba-Mirim, São Paulo State, Brazil

2013 ◽  
Vol 13 (1) ◽  
pp. 70-73 ◽  
Author(s):  
Frederico Alves D'Avila ◽  
Almério de Castro Gomes
Keyword(s):  
Early Spring ◽  
Dry Season ◽  
Southeastern Brazil ◽  
Rank Test ◽  
Late Winter ◽  
Dam Construction ◽  
Wet Season ◽  
Paulo State ◽  
Significant Difference ◽  
Wilcoxon Rank Test

A two and a half year survey was conducted at a dam in southeastern Brazil. Shannon Traps were used for sampling. Kruskal-Wallis test showed little relation between rainfall and abundance. The data clearly show three abundance peaks, all of them in the end of the dry season, in consonance with the scarce literature existent. Although Kruskal-Wallis Test did not find a clear preference for the dry season, Pairwise Wilcoxon Rank Test revealed a significant difference between Fall and Spring samples. Ma. titillans population had a peak on late winter/early spring, close to the begin of wet season.

Seasonal variability of evapotranspiration in the central Andes of Peru using eddy covariance techniques and empirical methods

2020 ◽  
Author(s):  
Stephany Magaly Callañaupa Gutierrez ◽  
Hans Segura Cajachagua ◽  
Miguel Saavedra ◽  
Jose Flores ◽  
Joan Cuxart ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Sonic Anemometer ◽  
Strong Relationship ◽  
Mean Value ◽  
Dry Season ◽  
P Value ◽  
Wet Season ◽  
Peruvian Andes ◽  
Energy Limitation ◽  
Relationship Of

<p>In this study, the real evapotranspiration (ET) obtained using the eddy covariance (EC) technique from field crops located in the central Peruvian Andes (Huancayo Observatory, 12.04° S, 75.32°, 3350 msnm) is analyzed. Data from a sonic anemometer and a krypton hygrometer are used to estimate daily and monthly ET variability and to explore relationships with meteorological and surface variables. The results show that the mean value of daily evapotranspiration is estimated to be 3.45 mm/day during the wet season (January to March) while in the dry season (June to August) the value is 0.95 mm/day. In addition, linear regressions were used in order to evaluate the relationship of meteorological variables with evapotranspiration. As a result, solar radiation is the meteorological variable that has a strong relationship with evapotranspiration during the wet season (r2=0.76, p-value <0.005) and soil moisture during the dry season (r2=0.77, p-value <0.005). These results indicate a clear water-energy limitation depending on the season. Besides, the empirical evapotranspiration equations of FAO Penman-Monteith, Priestley-Taylor and Hargreaves were validated. Where the Priestley-Taylor equation is the empirical equation that best fits the observed data of evapotranspiration by EC (r2=0.70, p-value< 0.005).</p>

scholarly journals Pemetaan Indek Kekeringan dan Sebaran Titik Hotspot Daerah Potensi Kebakaran Hutan dan Lahan di Propinsi Riau

2018 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Yotta Autika ◽  
Aras Mulyadi ◽  
Yusni Ikhwan Siregar
Keyword(s):  
Land Use ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Dry Season ◽  
Wet Season ◽  
Index Method ◽  
Spi Index ◽  
Maximum Duration ◽  
Relationship Of ◽  
The Relationship

Riau is one of the most vulnerable provinces to forest and land fires in Indonesia. The potency for forest and land fires is inseparable from the presence of peatlands and exacerbated by drought. The purpose of this research is to know the characteristics of meteorological drought using SPI (Standardized Precipitation Index) method and its relation with forest and peatland fire as one of disaster management effort in Riau Province. The data used in this research are monthly rainfall data from meteorology station and rainfall posts of BMKG, hotspot data from NOAA satellite, map of Forest Use Agreement (TGHK), peat land map and land use map. Analysis of drought characteristics was done by calculating monthly SPI-1 then determining the maximum duration, intensity, severity and drought exposure. Determination of the severity of the drought by weighting and suspension method was based on duration and intensity while drought exposure was done by overlaying the map of the severity of the drought with the land use map. Meanwhile, to know the potential of forest and land fires began with the selection of hotspots on peatlands and forest areas every month then created a graph of the relationship of meteorological drought with the number of hotspots. Then, to see the relationship of drought distribution to the distribution of hotspots in dry season (MK) and wet season (MH) of 2015 was done by overlaying cover the drought distribution with hotspot distribution. The result shows that drought characteristic in the most of Riau province has maximum duration around 4-6 months, dry category of intensity, high category of severity with exposure area in paddy field, field, habitation, and plantation. Then, negative SPI Index (dry condition) has potential to increase the number of hotspots otherwise positive SPI index (wet condition) leads to low occurrence of hotspot. The drought distribution in the dry season (July, August, September) of 2015 triggers the number of hotspots during drought conditions, while in wet season (April, November, December) of 2015 are dominated by normal conditions, some areas are dry and wet, resulting in lower hotspots distribution compared to the dry season.

scholarly journals Representation of Boreal Winter MJO and Its Teleconnection in a Dynamical Ensemble Seasonal Prediction System

2018 ◽  
Vol 31 (21) ◽  
pp. 8803-8818 ◽  
Author(s):  
Hyerim Kim ◽  
Myong-In Lee ◽  
Daehyun Kim ◽  
Hyun-Suk Kang ◽  
Yu-Kyung Hyun
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Seasonal Prediction ◽  
La Niña ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Teleconnection Patterns ◽  
La Nina ◽  
Forecast Lead Time

This study examines the representation of the Madden–Julian oscillation (MJO) and its teleconnection in boreal winter in the Global Seasonal Forecast System, version 5 (GloSea5), using 20 years (1991–2010) of hindcast data. The sensitivity of the performance to the polarity of El Niño–Southern Oscillation (ENSO) is also investigated. The real-time multivariate MJO index of Wheeler and Hendon is used to assess MJO prediction skill while intraseasonal 200-hPa streamfunction anomalies are used to evaluate the MJO teleconnection. GloSea5 exhibits significant MJO prediction skill up to 25 days of forecast lead time. MJO prediction skill in GloSea5 also depends on initial MJO phases, with relatively enhanced (degraded) performance when the initial MJO phase is 2 or 3 (8 or 1) during the first 2 weeks of the hindcast period. GloSea5 depicts the observed MJO teleconnection patterns in the extratropics realistically up to 2 weeks albeit weaker than the observed. The ENSO-associated basic-state changes in the tropics and in the midlatitudes are reasonably represented in GloSea5. MJO prediction skill during the first 2 weeks of the hindcast is slightly higher in neutral and La Niña years than in El Niño years, especially in the upper-level zonal wind anomalies. Presumably because of the better representation of MJO-related tropical heating anomalies, the Northern Hemispheric MJO teleconnection patterns in neutral and La Niña years are considerably better than those in El Niño years.

Predictability of the Madden–Julian Oscillation in the Intraseasonal Variability Hindcast Experiment (ISVHE)*

2014 ◽  
Vol 27 (12) ◽  
pp. 4531-4543 ◽  
Author(s):  
J. M. Neena ◽  
June Yi Lee ◽  
Duane Waliser ◽  
Bin Wang ◽  
Xianan Jiang
Keyword(s):  
Dynamic Models ◽  
Intraseasonal Variability ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Future Research ◽  
Boreal Winter ◽  
Madden Julian Oscillation ◽  
Maritime Continent ◽  
Coupled Models ◽  
Ensemble Mean

Abstract The Madden–Julian oscillation (MJO) represents a primary source of predictability on the intraseasonal time scales and its influence extends from seasonal variations to weather and extreme events. While the last decade has witnessed marked improvement in dynamical MJO prediction, an updated estimate of MJO predictability from a contemporary suite of dynamic models, in conjunction with an estimate of their corresponding prediction skill, is crucial for guiding future research and development priorities. In this study, the predictability of the boreal winter MJO is revisited based on the Intraseasonal Variability Hindcast Experiment (ISVHE), a set of dedicated extended-range hindcasts from eight different coupled models. Two estimates of MJO predictability are made, based on single-member and ensemble-mean hindcasts, giving values of 20–30 days and 35–45 days, respectively. Exploring the dependence of predictability on the phase of MJO during hindcast initiation reveals a slightly higher predictability for hindcasts initiated from MJO phases 2, 3, 6, or 7 in three of the models with higher prediction skill. The estimated predictability of MJO initiated in phases 2 and 3 (i.e., convection in Indian Ocean with subsequent propagation across Maritime Continent) being equal to or higher than other MJO phases implies that the so-called Maritime Continent prediction barrier may not actually be an intrinsic predictability limitation. For most of the models, the skill for single-member (ensemble mean) hindcasts is less than the estimated predictability limit by about 5–10 days (15–25 days), implying that significantly more skillful MJO forecasts can be afforded through further improvements of dynamical models and ensemble prediction systems (EPS).

scholarly journals Weakened seasonality of the African rainforest precipitation in boreal winter and spring driven by tropical SST variabilities

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Xin-Yue Wang ◽  
Jiang Zhu ◽  
Meijiao Xin ◽  
Chentao Song ◽  
Yadi Li ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Global Climate ◽  
Dry Season ◽  
Boreal Winter ◽  
Wet Season ◽  
Surface Temperature Change ◽  
Boreal Spring ◽  
Zonal Circulation ◽  
Precipitation Changes ◽  
The Impact

AbstractPrecipitation in the equatorial African rainforest plays an important role in both the regional hydrological cycle and the global climate variability. Previous studies mostly focus on the trends of drought in recent decades or long-time scales. Using two observational datasets, we reveal a remarkable weakening of the seasonal precipitation cycle over this region from 1979 to 2015, with precipitation significantly increased in the boreal winter dry season (~ 0.13 mm/day/decade) and decreased in the boreal spring wet season (~ 0.21 mm/day/decade), which account for ~ 14% (the precipitation changes from 1979 to 2015) of their respective climatological means. We further use a state-of-the-art atmospheric model to isolate the impact of sea surface temperature change from different ocean basins on the precipitation changes in the dry and wet seasons. Results show that the strengthening precipitation in the dry season is mainly driven by the Atlantic warming, whereas the weakening precipitation in the wet season can be primarily attributed to the Indian Ocean. Warming Atlantic intensifies the zonal circulation over the African rainforest, strengthening moisture convergence and intensifying precipitation in the boreal winter dry season. Warming Indian Ocean contributes more to reducing the zonal circulation and suppressing the convection in the boreal spring wet season, leading to an opposite effect on precipitation. This result has important implication on local ecology as well as global climate system.

scholarly journals Understanding Skill of Seasonal Mean Precipitation Prediction over California during Boreal Winter and Role of Predictability Limits

2020 ◽  
Vol 33 (14) ◽  
pp. 6141-6163
Author(s):  
Arun Kumar ◽  
Mingyue Chen
Keyword(s):  
Role Model ◽  
Systematic Approach ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Southeast United States ◽  
The North ◽  
Precipitation Prediction ◽  
Prediction Systems

AbstractUsing extensive hindcasts from seasonal prediction systems participating in the North American Multi-Model Ensemble (NMME), possible causes for low skill in predicting seasonal mean precipitation over California during December–February (DJF) are investigated. The analysis focuses on investigating two possibilities for low prediction skill: role model biases or inherent predictability limits. The motivation for the analysis was the seasonal prediction during DJF 2015/16 that called for enhanced probability for above normal precipitation over southern California (which was consistent with expected conditions during an extreme El Niño) while the observed precipitation was below normal. Based on various analysis approaches and using hindcast datasets from multiple seasonal prediction systems, we build up the evidence that low skill in predicting seasonal mean precipitation over California is likely to be due to inherent predictability associated with a low signal-to-noise (SNR) regime. For the same set of seasonal prediction systems, the precipitation variability over California is contrasted with that over the southeast United States where prediction skill, as well as the SNR, is higher. The discussion also notes that building a knowledge base that goes beyond the well-known response to ENSO (based on the linear regression or composite techniques) has proven to be difficult and a systematic approach to reaching resolution to some of the overarching questions is required, and toward that end, a pathway is suggested.

The potential predictability of Singapore and Maritime Continent weather regimes in relation to the MJO and ENSO

2020 ◽  
Author(s):  
Muhammad Eeqmal Hassim ◽  
Joshua Lee
Keyword(s):  
Peak Frequency ◽  
Boreal Winter ◽  
Madden Julian Oscillation ◽  
Maritime Continent ◽  
Potential Predictability ◽  
Inter Tropical Convergence Zone ◽  
Weather Regimes ◽  
Seasonal Movement ◽  
Relationship Of ◽  
The Relationship

<p><span>The Madden-Julian Oscillation (MJO) is a well-known source of predictability on sub-seasonal-to-seasonal (S2S) time scales and a major driver of intraseasonal weather variability around the globe. For example, the MJO’s interaction with and influence on daily regional weather in the Maritime Continent-Southeast Asia (MC-SEA) region is thought to be most pronounced during boreal winter (November through February), given that the amplitude of MJO activity is often much stronger during that period compared to other times of the year.</span></p><p><span>In this study, we examine the relationship of the MJO to eight weather regimes (WR) that have been previously defined for Singapore and the MC-SEA region using </span><em><span>k</span></em><span>-means clustering of daily sounding data from reanalysis. These weather regimes cover the whole annual cycle of rainfall with well-defined peak frequency times and mean spatial structures that correspond to the seasonal movement of the Inter-tropical Convergence Zone (ITCZ) across the Equator. Following previous work, we use a statistical method to compute the lagged relationship between each MJO phase and daily WR occurrence between December 1980 - November 2014 to quantify the </span><span>change in the likelihood</span><span> that a certain regime will occur relative to climatology, given an MJO phase in advance. Bimonthly analysis indicates that strong lag relationships exist between MJO phases and certain regimes in different two-month periods, thus giving potential predictability of the type of mean weekly weather in the MC-SEA up to 3-4 weeks ahead. In addition, we consider the modulation of the MJO-WR relationships stratified by the ENSO phase to determine whether the expected WR frequency response to MJO activity varies substantially in different background states.</span></p>

Seasonal–Interannual Variation and Prediction of Wet and Dry Season Rainfall over the Maritime Continent: Roles of ENSO and Monsoon Circulation

2016 ◽  
Vol 29 (10) ◽  
pp. 3675-3695 ◽  
Author(s):  
Tuantuan Zhang ◽  
Song Yang ◽  
Xingwen Jiang ◽  
Ping Zhao
Keyword(s):  
El Niño ◽  
Large Scale ◽  
El Nino ◽  
Southern Oscillation ◽  
Dry Season ◽  
La Niña ◽  
Monsoon Circulation ◽  
Wet Season ◽  
Maritime Continent ◽  
La Nina

Abstract The authors analyze the seasonal–interannual variations of rainfall over the Maritime Continent (MC) and their relationships with El Niño–Southern Oscillation (ENSO) and large-scale monsoon circulation. They also investigate the predictability of MC rainfall using the hindcast of the U.S. National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). The seasonal evolution of MC rainfall is characterized by a wet season from December to March and a dry season from July to October. The increased (decreased) rainfall in the wet season is related to the peak-decaying phase of La Niña (El Niño), whereas the increased (decreased) rainfall in the dry season is related to the developing phase of La Niña (El Niño), with an apparent spatial incoherency of the SST–rainfall relationship in the wet season. For extremely wet cases of the wet season, local warm SST also contributes to the above-normal rainfall over the MC except for the western area of the MC due to the effect of the strong East Asian winter monsoon. The CFSv2 shows high skill in predicting the main features of MC rainfall variations and their relationships with ENSO and anomalies of the large-scale monsoon circulation, especially for strong ENSO years. It predicts the rainfall and its related circulation patterns skillfully in advance by several months, especially for the dry season. The relatively lower skill of predicting MC rainfall for the wet season is partly due to the low prediction skill of rainfall over Sumatra, Malay, and Borneo (SMB), as well as the unrealistically predicted relationship between SMB rainfall and ENSO.

scholarly journals Forecast Skill of the NAO in the Subseasonal to-Seasonal Prediction Models

2021 ◽  
pp. 1-50
Author(s):  
Pei-Ning Feng ◽  
Hai Lin ◽  
Jacques Derome ◽  
Timothy M. Merlis
Keyword(s):  
Prediction Models ◽  
Model Performance ◽  
Seasonal Prediction ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Consistent Difference ◽  
Initial Condition ◽  
Quasi Biennial Oscillation ◽  
Project Model

AbstractThe prediction skill of the North Atlantic Oscillation (NAO) in boreal winter is assessed in the operational models of the WCRP/WWRP Subseasonal-to-Seasonal (S2S) prediction project. Model performance in representing the contribution of different processes to the NAO forecast skill is evaluated. The S2S models with relatively higher stratospheric vertical resolutions (high-top models) are in general more skillful in predicting the NAO than those models with relatively lower stratospheric resolutions (low-top models). Comparison of skill is made between different groups of forecasts based on initial condition characteristics: phase and amplitude of the NAO, easterly and westerly phases of the quasi-biennial oscillation (QBO), warm and cold phases of ENSO, and phase and amplitude of the Madden-Julia Oscillation (MJO). The forecasts with a strong NAO in the initial condition are more skillful than with a weak NAO. Those with negative NAO tend to have more skillful predictions than positive NAO. Comparisons of NAO skill between forecasts during easterly and westerly QBO and between warm and cold ENSO show no consistent difference for the S2S models. Forecasts with strong initial MJO tend to be more skillful in the NAO prediction than weak MJO. Among the eight phases of MJO in the initial condition, phases 3-4 and phase 7 have better NAO forecast skills compared with the other phases.The results of this study have implications for improving our understanding of sources of predictability of the NAO. The situation dependence of the NAO prediction skill is likely useful in identifying “ windows of opportunity” for subseasonal to seasonal predictions.

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