scholarly journals Variability of the Indian Monsoon in the ECHAM3 Model: Sensitivity to Sea Surface Temperature, Soil Moisture, and the Stratospheric Quasi-Biennial Oscillation

1998 ◽  
Vol 11 (8) ◽  
pp. 1837-1858 ◽  
Author(s):  
K. Arpe ◽  
L. Dümenil ◽  
M. A. Giorgetta
Keyword(s):  
Soil Moisture ◽  
Arabian Sea ◽  
Large Scale ◽  
Monsoon Season ◽  
Sea Surface ◽  
Northern Indian Ocean ◽  
Quasi Biennial Oscillation ◽  
The Difference ◽  
Biennial Oscillation

Abstract The variability of the monsoon is investigated using a set of 90-day forecasts [MONEG (Tropical Ocean Global Atmosphere Monsoon Numerical Experimentation Group) experiments] and a set of AMIP-type (Atmospheric Model Intercomparison Project) long-term simulations of the atmospheric circulation with the ECHAM3 model. The large-scale aspects of the summer monsoon circulation as represented by differences of dynamical quantities between the two extreme years 1987 and 1988 were reproduced well by the model in both kinds of experiments forced with observed sea surface temperature (SST). At the regional scale the difference of precipitation over India during summer 1987 and 1988 was well reproduced by the model in the 90-day forecasts using interannually varying SSTs; however, similarly good results were achieved in forecasts using climatological SSTs. The long-term simulations forced with interannually varying SST at the lower boundary of the atmosphere over a period of 14 years, on the other hand, only partly reproduce the observed differences of precipitation over India between 1987 and 1988. For the ensemble mean of five simulations averaged from June to September and for the whole of India an increase from 1987 to 1988 is simulated by the model as observed but with smaller values. The difference in observed precipitation between 1987 and 1988 is of opposite sign for May to that for September. The simulations and observations agree in the manifestation of this sense of opposing variability within a monsoon season for these two years and also for other years. The simulations and observations differ most during July. The paper concentrates on the question why the interannual variability in the long-term simulations on one hand and the 90-day forecasts and in the observations of precipitation on the other hand differ so strongly during the peak of the monsoon in July. Large-scale dynamics over India are mainly forced by the anomalies of Pacific SST. For the variability of precipitation over India other forcings than the Pacific SST are important as well. Due to enhanced evaporation, warmer SSTs over the northern Indian Ocean lead to increased precipitation over India. Changes in the SST there within the range of uncertainty (0.5 K) can lead to clear impacts. As a further boundary forcing, the impact of soil moisture is investigated. The use of realistic soil moisture differences between 1987 and 1988 in the MONEG forecasts resulted in improved skill of precipitation forecasts over India. Also the two individual AMIP simulations with realistic precipitation differences over India had more realistic soil moisture differences over east Asia in the beginning of the monsoon season between the two years than those experiments that failed to produce the correct precipitation differences. The years 1987 and 1988 were quite different with respect to the phase of the stratospheric quasi-biennial oscillation (QBO). As atmospheric circulation models cannot yet reproduce stratospheric QBOs realistically, their impact was tested by nudging observed QBOs into AMIP simulations for July 1987 and 1988. Seven out of eight experiments showed an impact toward a more realistic simulation of precipitation over India; however, during the west phase of the QBO (1987) impacts are very small. None of these forcings gave a dominant effect. If this finding is confirmed by further experimentation, improvements of practical long-range forecasts may be very difficult as two of these quantities are hardly known with the required accuracy (northern Indian Ocean SSTs and the Eurasian soil moisture) and because models are not yet able to simulate the stratospheric QBO realistically. This study confirms that El Niño has two direct effects: it reduces the precipitation over India and reduces the surface winds over the Arabian Sea. Due to the latter, the SST of the Arabian Sea can increase as there is less mixing and upwelling in the ocean. Here it is suggested that because of this increased SST there would be more precipitation over India, thus counteracting the expected decrease from the direct El Niño effect. Sensitivity experiments were carried out with the ECHAM3 model to substantiate this hypothesis. The results may be model-dependent and model deficiencies might influence sensitivities from boundary forcings adversely. Therefore observational data have been investigated as far as possible to seek independent confirmation of the findings obtained through the model simulations.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

scholarly journals Influence of the Stratospheric Quasi-Biennial Oscillation on the Madden–Julian Oscillation during Austral Summer

2017 ◽  
Vol 74 (4) ◽  
pp. 1105-1125 ◽  
Author(s):  
Eriko Nishimoto ◽  
Shigeo Yoden
Keyword(s):  
Large Scale ◽  
Statistical Significance ◽  
Austral Summer ◽  
Horizontal Wind ◽  
Convective System ◽  
Madden Julian Oscillation ◽  
Quasi Biennial Oscillation ◽  
Enso Events ◽  
Convective Region ◽  
Biennial Oscillation

Abstract Influence of the stratospheric quasi-biennial oscillation (QBO) on the Madden–Julian oscillation (MJO) and its statistical significance are examined for austral summer (DJF) in neutral ENSO events during 1979–2013. The amplitude of the OLR-based MJO index (OMI) is typically larger in the easterly phase of the QBO at 50 hPa (E-QBO phase) than in the westerly (W-QBO) phase. Daily composite analyses are performed by focusing on phase 4 of the OMI, when the active convective system is located over the eastern Indian Ocean through the Maritime Continent. The composite OLR anomaly shows a larger negative value and slower eastward propagation with a prolonged period of active convection in the E-QBO phase than in the W-QBO phase. Statistically significant differences of the MJO activities between the QBO phases also exist with dynamical consistency in the divergence of horizontal wind, the vertical wind, the moisture, the precipitation, and the 100-hPa temperature. A conditional sampling analysis is also performed by focusing on the most active convective region for each day, irrespective of the MJO amplitude and phase. Composite vertical profiles of the conditionally sampled data over the most active convective region reveal lower temperature and static stability around the tropopause in the E-QBO phase than in the W-QBO phase, which indicates more favorable conditions for developing deep convection. This feature is more prominent and extends into lower levels in the upper troposphere over the most active convective region than other tropical regions. Composite longitude–height sections show similar features of the large-scale convective system associated with the MJO, including a vertically propagating Kelvin response.

Influence of Intrinsic Factors on Erosive Tooth Wear in a Large-Scale Epidemiological Study

2016 ◽  
Vol 50 (5) ◽  
pp. 508-516 ◽  
Author(s):  
Viivi Alaraudanjoki ◽  
Marja-Liisa Laitala ◽  
Leo Tjäderhane ◽  
Paula Pesonen ◽  
Adrian Lussi ◽  
...  
Keyword(s):  
Large Scale ◽  
Erosive Wear ◽  
Tooth Wear ◽  
Risk Indicators ◽  
Health Examination ◽  
Convenience Sample ◽  
Erosive Tooth Wear ◽  
Intrinsic Factors ◽  
The Difference

Objective: To assess the influence of self-reported intrinsic factors [gastroesophageal reflux disease (GERD), long-term alcoholism, long-term heavy use of alcohol and multiple pregnancies] on erosive tooth wear in a middle-aged cohort sample. Materials and Methods: Of the total Northern Finland Birth Cohort (NFBC 1966), a convenience sample (n = 3,181) was invited for an oral health examination in 2012-2013, of which 1,962 participated, comprising the final study group. Erosive tooth wear was assessed by sextants using the Basic Erosive Wear Examination Index (BEWE, 0-18). Clinical data were supplemented by questionnaires conducted in 1997/1998 and 2012/2013. The participants were divided into severe (BEWE sum ≥9) and no-to-moderate (BEWE sum 0-8) erosive wear groups, and the logistic regression model was applied. Results: Selected intrinsic factors were quite rare in this cohort sample and explained only 5.9% of the difference in the prevalence and severity of erosive wear. Daily symptoms of GERD [odds ratio (OR) 3.8, confidence interval (CI) 1.2-12.0] and hyposalivation (OR 3.8, CI 1.2-11.8) were the strongest risk indicators for severe erosive wear. Additionally, variables associated with an elevated risk for severe erosive wear were diagnosed alcoholism at any point (OR 2.5, CI 0.7-9.7) and self-reported heavy use of alcohol in both questionnaires (OR 2.0, CI 0.6-6.2). Even low-dose long-term consumption of alcohol was associated with erosive wear. Conclusions: In this cohort sample, intrinsic factors such as GERD or alcoholism alone are relatively uncommon causes of erosive tooth wear. The role of long-term use of alcohol in the erosion process may be bigger than presumed.

scholarly journals Variability of seasonal and annual rainfall in the River Nile riparian countries and possible linkages to ocean–atmosphere interactions

2015 ◽  
Vol 47 (1) ◽  
pp. 171-184 ◽  
Author(s):  
Charles Onyutha
Keyword(s):  
Large Scale ◽  
Equatorial Region ◽  
Annual Rainfall ◽  
River Nile ◽  
Short Term ◽  
Ocean Atmosphere ◽  
Data Points ◽  
The Difference ◽  
Term Data

Variability analyses for the rainfall over the Nile Basin have been confined mostly to sub-basins and the annual mean of the hydroclimatic variable based on observed short-term data from a few meteorological stations. In this paper, long-term country-wide rainfall over the period 1901–2011 was used to assess variability in the seasonal and annual rainfall volumes in all the River Nile countries in Africa. Temporal variability was determined through temporal aggregation of series rescaled nonparametrically in terms of the difference between the exceedance and non-exceedance counts of data points such that the long-term average (taken as the reference) was zero. The co-occurrence of the variability of rainfall with those of the large-scale ocean–atmosphere interactions was analyzed. Between 2000 and 2012, while the rainfall in the equatorial region was increasing, that for the countries in the northern part of the River Nile was below the reference. Generally, the variability in the rainfall of the countries in the equatorial (northern) part of the River Nile was found to be significantly linked to occurrences in the Indian and Atlantic (Pacific and Atlantic) Oceans. Significant linkages to Niño 4 regarding the variability of both the seasonal and annual rainfall of some countries were also evident.

scholarly journals Long‐term predictability of soil moisture dynamics at the global scale: Persistence versus large‐scale drivers

2016 ◽  
Vol 43 (16) ◽  
pp. 8554-8562 ◽  
Author(s):  
Nadine Nicolai‐Shaw ◽  
Lukas Gudmundsson ◽  
Martin Hirschi ◽  
Sonia I. Seneviratne
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Global Scale ◽  
Soil Moisture Dynamics ◽  
Moisture Dynamics

scholarly journals Development of a climate record of tropospheric and stratospheric ozone from satellite remote sensing: evidence of an early recovery of global stratospheric ozone

2012 ◽  
Vol 12 (1) ◽  
pp. 3169-3211
Author(s):  
J. R. Ziemke ◽  
S. Chandra
Keyword(s):  
Climate Models ◽  
Stratospheric Ozone ◽  
Steady Increase ◽  
Ozone Monitoring Instrument ◽  
Early Recovery ◽  
Quasi Biennial Oscillation ◽  
Time Record ◽  
The Tropics ◽  
Biennial Oscillation

Abstract. Ozone data beginning October 2004 from the Aura Ozone Monitoring Instrument (OMI) and Aura Microwave Limb Sounder (MLS) are used to evaluate the accuracy of the Cloud Slicing technique in effort to develop long data records of tropospheric and stratospheric ozone and for studying their long-term changes. Using this technique, we have produced a 32-yr (1979–2010) long record of tropospheric and stratospheric ozone from the combined Total Ozone Mapping Spectrometer (TOMS) and OMI. The analyses of these time series suggest that the quasi-biennial oscillation (QBO) is the dominant source of inter-annual variability of stratospheric ozone and is clearest in the Southern Hemisphere during the Aura time record with related inter-annual changes of 30–40 Dobson Units. Tropospheric ozone also indicates a QBO signal in the tropics with peak-to-peak changes varying from 2 to 7 DU. The stratospheric ozone record indicates a steady increase since the mid-1990's with current ozone levels comparable to the mid-1980's. This is earlier than predicted by many of the current climate models which suggest recovery to the mid-1980's levels by year 2020 or later.

scholarly journals Evolutions of sea level high and warm pool in the southeastern Arabian Sea and their association with Asian monsoons : A study on cause-and-effect relationships

MAUSAM ◽  
2021 ◽  
Vol 59 (1) ◽  
pp. 87-94
Author(s):  
O. P. SINGH
Keyword(s):  
Sea Level ◽  
Summer Monsoon ◽  
El Niño ◽  
Arabian Sea ◽  
El Nino ◽  
Southern Oscillation ◽  
Monsoon Season ◽  
Warm Pool ◽  
Sea Surface ◽  
Southeastern Arabian Sea

The present study aims at gaining more insight into the evolution of warm pool and associated sea level dome in the southeastern Arabian Sea before the summer monsoon onset.  The results show that the Sea Surface Temperature (SST) maximum in the warm pool region is found during April close to the southwest coast of India.  The Sea Surface Height (SSH) maximum over the same region is observed during December. The collapse of sea level dome begins well in advance during the pre-monsoon whereas the warm pool collapses after the onset of summer monsoon during June.  Therefore, there is a lag of about three to four months between the collapses of the sea level high and the warm pool.  Most interesting aspect is the dramatic increase of SST from September and SSH from October which is continued throughout the post monsoon season (October - December). Therefore, both the collapse and evolution of warm pool are dramatic events before and after the summer monsoon.                    There are considerable variations in the intensity of warm pool and the height of sea level dome on interannual scale.  The variation during El-Nino Southern Oscillation (ENSO) epoch of 1987-88 has revealed many interesting features.  During El-Nino year 1987 the warm pool intensity reached its peak in June whereas during La Nina year 1988 the warm pool attained its maximum intensity much earlier, i.e., in April. 

scholarly journals Close co-variation between soil moisture and runoff emerging from multi-catchment data across Europe

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Navid Ghajarnia ◽  
Zahra Kalantari ◽  
René Orth ◽  
Georgia Destouni
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Large Scale ◽  
Temperature Anomaly ◽  
Important Variable ◽  
Climatic Variables ◽  
New Approach ◽  
European Climate ◽  
Moisture Conditions

AbstractSoil moisture is an important variable for land-climate and hydrological interactions. To investigate emergent large-scale, long-term interactions between soil moisture and other key hydro-climatic variables (precipitation, actual evapotranspiration, runoff, temperature), we analyze monthly values and anomalies of these variables in 1378 hydrological catchments across Europe over the period 1980–2010. The study distinguishes results for the main European climate regions, and tests how sensitive or robust they are to the use of three alternative observational and re-analysis datasets. Robustly across the European climates and datasets, monthly soil moisture anomalies correlate well with runoff anomalies, and extreme soil moisture and runoff values also largely co-occur. For precipitation, evapotranspiration, and temperature, anomaly correlation and extreme value co-occurrence with soil moisture are overall lower than for runoff. The runoff results indicate a possible new approach to assessing variability and change of large-scale soil moisture conditions by use of long-term time series of monitored catchment-integrating stream discharges.

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