scholarly journals Improved Seasonal Precipitation Forecasts for the Asian Monsoon Using 16 Atmosphere–Ocean Coupled Models. Part II: Anomaly

2012 ◽  
Vol 25 (1) ◽  
pp. 65-88 ◽  
Author(s):  
T. N. Krishnamurti ◽  
Vinay Kumar
Keyword(s):  
Asian Monsoon ◽  
Monsoon Rainfall ◽  
Systematic Errors ◽  
Rainfall Anomaly ◽  
Seasonal Rainfall ◽  
Seasonal Precipitation ◽  
Training Phase ◽  
Post Processing ◽  
Coupled Models ◽  
Rms Errors

Abstract This is the second part of a paper on the improved seasonal precipitation forecasts for the Asian monsoon using 16 atmosphere–ocean coupled models. This study utilizes a large suite of coupled atmosphere–ocean models; this second part largely addresses the skill of rainfall anomaly forecasts. These include both deterministic and probabilistic skill measures such as the RMS errors, anomaly correlations, equitable threat scores, and the Brier skill score. It was possible to improve the skills of rainfall climatology from the use of a downscaled multimodel superensemble to very high levels, and it is of interest to ask how far this methodology would go toward improving the skills of seasonal rainfall anomaly forecasts. It is possible to go through a sequence of multimodel post processing to improve upon these skills by using a dense rain gauge network over Asia, downscaling forecasts for each member model, and constructing a multimodel superensemble that benefits from the persistence of errors of the member models. This paper addresses the spinup issues of the downscaling and the superensemble results where the number of years of model data needed for training phase, for the downscaling, and for the construction of the superensemble, is addressed. In the context of cross validation, the training phase includes 14 seasons of monsoon data. The forecast phase is only one season; it is this season that was not included in the training phase each time. The relationship between data length and the number of models needed for enhanced skills is another issue that is addressed. Seasonal climate forecasts over the larger monsoon Asia domain and over the regional belts are evaluated. The superensemble forecasts invariably have the highest skill compared to the member models globally and regionally. This is largely due to the presence of large systematic errors in models that carry low seasonal prediction skills. Such models carry persistent signatures of systematic errors, and their errors are recognized by the multimodel superensemble. The probabilistic skills show that the superensemble-based forecasts carry a much higher reliability score compared to the member models. This implies that the superensemble-based forecasts are the most reliable among all the member models. It is possible to examine the performance of models and of the superensemble during periods of heavy monsoon rainfall versus those for deficient monsoon rainfall seasons. One of the conclusions of this study is that given the uncertainties in current modeling for seasonal rainfall forecasts, post processing of multimodel forecasts, using the superensemble methodology, seems to provide the most promising results for the rainfall anomaly forecasts. These results are confirmed by an additional skill metric where the RMS errors and the correlations of forecast skills are evaluated using a normalized precipitation anomaly for the forecasts and the observed estimates.

scholarly journals Improved Seasonal Precipitation Forecasts for the Asian Monsoon Using 16 Atmosphere–Ocean Coupled Models. Part I: Climatology

2012 ◽  
Vol 25 (1) ◽  
pp. 39-64 ◽  
Author(s):  
Vinay Kumar ◽  
T. N. Krishnamurti
Keyword(s):  
Climate Models ◽  
Detailed Examination ◽  
Rain Gauge ◽  
Rainfall Anomaly ◽  
Training Phase ◽  
Coupled Models ◽  
Recent Developments ◽  
Forecast Models ◽  
Rainfall Climatology ◽  
Rainfall Estimates

Abstract The goal of this study is to utilize several recent developments on rainfall data collection, downscaling of available climate models, training and forecasts from such models within the framework of a multimodel superensemble, and first a detailed examination of the seasonal climatology. The unique aspect of this study is that it became possible to use the forecast results from as many as 16 state-of-the-art coupled climate models. A downscaling component, with respect to observed rainfall estimates, uses a very dense Asian rain gauge network. This feature enables the forecasts of each model to be bias corrected to a common 25-km resolution. The downscaling statistics for each model, at each grid location, are developed during a training phase of the model forecasts. This is done wherever the observed rainfall estimates are available. In the “forecast phase,” the forecasts from all of the member models use the downscaling coefficients of the “training phase.” The downscaling and the extraction of the superensemble weights are done during the training phase. This makes use of the cross-validation principle. This means that the season to be forecasted is left out of the entire forecast dataset. Thus all of the statistics for downscaling and the superensemble construction are done separately for the forecasts of each season for all the years. The forecast phase is the season that is being forecast, where the aforementioned statistics are deployed for constructing the final downscaled superensemble. These forecasts are next used for the construction of a multimodel superensemble. The geographical distributions of the downscaling coefficients provide a first look at the systematic errors of the member model forecasts. This combination of multimodels, the vast rain gauge dataset, the downscaling, and the superensemble provides a major improvement for the rainfall climatology and anomalies for the forecast phase. One of the main results of this paper is on the improvement of rainfall climatology of the member models. The downscaled multimodel superensemble shows a correlation of nearly 1.0 with respect to the observed climatology. This high skill is important for addressing the rainfall anomaly forecasts, which are defined in terms of departures from the observed (rather than a model based) climatology. This first part of the paper provides a description of the member models, the length of the training and forecast phases, the sensitivity of results as the numbers of forecast models are increased, and the skills of the downscaled climatology forecasts.

scholarly journals Tectonic and climatic drivers of Asian monsoon evolution

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
James R. Thomson ◽  
Philip B. Holden ◽  
Pallavi Anand ◽  
Neil R. Edwards ◽  
Cécile A. Porchier ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Asian Monsoon ◽  
Monsoon Rainfall ◽  
East Asian Monsoon ◽  
East Asian ◽  
Meridional Overturning Circulation ◽  
Great Debate ◽  
Climatic Drivers ◽  
Meridional Overturning

AbstractAsian Monsoon rainfall supports the livelihood of billions of people, yet the relative importance of different drivers remains an issue of great debate. Here, we present 30 million-year model-based reconstructions of Indian summer monsoon and South East Asian monsoon rainfall at millennial resolution. We show that precession is the dominant direct driver of orbital variability, although variability on obliquity timescales is driven through the ice sheets. Orographic development dominated the evolution of the South East Asian monsoon, but Indian summer monsoon evolution involved a complex mix of contributions from orography (39%), precession (25%), atmospheric CO2 (21%), ice-sheet state (5%) and ocean gateways (5%). Prior to 15 Ma, the Indian summer monsoon was broadly stable, albeit with substantial orbital variability. From 15 Ma to 5 Ma, strengthening was driven by a combination of orography and glaciation, while closure of the Panama gateway provided the prerequisite for the modern Indian summer monsoon state through a strengthened Atlantic meridional overturning circulation.

The Role of Intraseasonal Variability in the Nature of Asian Monsoon Precipitation

2007 ◽  
Vol 20 (17) ◽  
pp. 4402-4424 ◽  
Author(s):  
Carlos D. Hoyos ◽  
Peter J. Webster
Keyword(s):  
Indian Ocean ◽  
Time Scales ◽  
Temporal Variability ◽  
Asian Monsoon ◽  
Monsoon Rainfall ◽  
Mountain Range ◽  
Monsoon Precipitation ◽  
The West ◽  
The Mean ◽  
Rainfall Estimates

Abstract The structure of the mean precipitation of the south Asian monsoon is spatially complex. Embedded in a broad precipitation maximum extending eastward from 70°E to the northwest tropical Pacific Ocean are strong local maxima to the west of the Western Ghats mountain range of India, in Cambodia extending into the eastern China Sea, and over the eastern tropical Indian Ocean and the Bay of Bengal (BoB), where the strongest large-scale global maximum in precipitation is located. In general, the maximum precipitation occurs over the oceans and not over the land regions. Distinct temporal variability also exists with time scales ranging from days to decades. Neither the spatial nor temporal variability of the monsoon can be explained simply as the response to the cross-equatorial pressure gradient force between the continental regions of Asia and the oceans of the Southern Hemisphere, as suggested in classical descriptions of the monsoon. Monthly (1979–2005) and daily (1997–present) rainfall estimates from the Global Precipitation Climatology Project (GPCP), 3-hourly (1998–present) rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) estimates of sea surface temperature (SST), reanalysis products, and satellite-determined outgoing longwave radiation (OLR) data were used as the basis of a detailed diagnostic study to explore the physical basis of the spatial and temporal nature of monsoon precipitation. Propagation characteristics of the monsoon intraseasonal oscillations (MISOs) and biweekly signals from the South China Sea, coupled with local and regional effects of orography and land–atmosphere feedbacks are found to modulate and determine the locations of the mean precipitation patterns. Long-term variability is found to be associated with remote climate forcing from phenomena such as El Niño–Southern Oscillation (ENSO), but with an impact that changes interdecadally, producing incoherent responses of regional rainfall. A proportion of the interannual modulation of monsoon rainfall is found to be the direct result of the cumulative effect of rainfall variability on intraseasonal (25–80 day) time scales over the Indian Ocean. MISOs are shown to be the main modulator of weather events and encompass most synoptic activity. Composite analysis shows that the cyclonic system associated with the northward propagation of a MISO event from the equatorial Indian Ocean tends to drive moist air toward the Burma mountain range and, in so doing, enhances rainfall considerably in the northeast corner of the bay, explaining much of the observed summer maximum oriented parallel to the mountains. Similar interplay occurs to the west of the Ghats. While orography does not seem to play a defining role in MISO evolution in any part of the basin, it directly influences the cumulative MISO-associated rainfall, thus defining the observed mean seasonal pattern. This is an important conclusion since it suggests that in order for the climate models to reproduce the observed seasonal monsoon rainfall structure, MISO activity needs to be well simulated and sharp mountain ranges well represented.

Improved trend-aware post-processing of GCM seasonal precipitation forecasts

2021 ◽  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Processing Method ◽  
Global Climate Models ◽  
Seasonal Precipitation ◽  
Seasonal Temperature ◽  
Post Processing ◽  
Climate Trends ◽  
Climate Forecasting ◽  
New Formulation

Abstract Climate trends have been observed over the recent decades in many parts of the world, but current global climate models (GCMs) for seasonal climate forecasting often fail to capture these trends. As a result, model forecasts may be biased above or below the trendline. In our previous research, we developed a trend-aware forecast post-processing method to overcome this problem. The method was demonstrated to be effective for embedding observed trends into seasonal temperature forecasts. In this study, we further develop the method for post-processing GCM seasonal precipitation forecasts. We introduce new formulation and evaluation features to cater for special characteristics of precipitation amounts, such as having a zero lower bound and highly positive skewness. We apply the improved method to calibrate ECMWF SEAS5 forecasts of seasonal precipitation for Australia. Our evaluation shows that the calibrated forecasts reproduce observed trends over the hindcast period of 36 years. In some regions where observed trends are statistically significant, forecast skill is greatly improved by embedding trends into the forecasts. In most regions, the calibrated forecasts outperform the raw forecasts in terms of bias, skill, and reliability. Wider applications of the new trend-aware post-processing method are expected to boost user confidence in seasonal precipitation forecasts.

John Tebbutt’s Observations of Halley’s Comet in 1910

1986 ◽  
Vol 6 (3) ◽  
pp. 354-358 ◽  
Author(s):  
Siegfried Röser ◽  
Graeme L. White
Keyword(s):  
Detailed Analysis ◽  
Systematic Errors ◽  
Astronomical Society ◽  
Royal Astronomical Society ◽  
Amateur Astronomer ◽  
Halley's Comet ◽  
Right Ascension ◽  
Rms Errors

AbstractThe Windsor amateur astronomer, John Tebbutt, had a ceased observing in 1907. However, in 1909, at the age of 75, he came out of retirement to observe Halley’s comet and his astrometric positions were published in the Monthly Notices of the Royal Astronomical Society. These data were used, together with most published observations from the 1835 and 1910 apparitions, for the computation of Halley’s orbit for ESA’s Halley intercept spacecraft, Giotto. A detailed analysis of the observations have shown minor imperfections that, when corrected, gave rms errors of 3''.5 arc in right ascension and 2''.8 in declination. His systematic errors are negligible at the 0''.2 level.

A 550,000-year record of East Asian monsoon rainfall from10Be in loess

Science ◽  
2018 ◽  
Vol 360 (6391) ◽  
pp. 877-881 ◽  
Author(s):  
J. Warren Beck ◽  
Weijian Zhou ◽  
Cheng Li ◽  
Zhenkun Wu ◽  
Lara White ◽  
...  
Keyword(s):  
Asian Monsoon ◽  
Monsoon Rainfall ◽  
East Asian Monsoon ◽  
East Asian

Phased evolution and variation of the South Asian monsoon, and resulting weathering and surface erosion in the Himalaya–Karakoram Mountains, since late Pliocene time using data from Arabian Sea core

2020 ◽  
Vol 157 (6) ◽  
pp. 864-878 ◽  
Author(s):  
Huayu Lu ◽  
Ruixuan Liu ◽  
Linhai Cheng ◽  
Han Feng ◽  
Hanzhi Zhang ◽  
...  
Keyword(s):  
South Asian ◽  
Arabian Sea ◽  
Asian Monsoon ◽  
Monsoon Rainfall ◽  
South Asian Monsoon ◽  
Monsoon Precipitation ◽  
Late Pliocene ◽  
Weathering Intensity ◽  
Karakoram Mountains ◽  
The Himalaya

AbstractWe investigate the phased evolution and variation of the South Asian monsoon and resulting weathering intensity and physical erosion in the Himalaya–Karakoram Mountains since late Pliocene time (c. 3.4 Ma) using a comprehensive approach. Neodymium and strontium isotopic compositions and single-grain zircon U–Pb age spectra reveal the sources of the deposits in the east Arabian Sea, and show a combination of sources from the Himalaya and the Karakoram–Kohistan–Ladakh Mountains, with sediments from the Indian Peninsula such as the Deccan Traps or Craton. We interpret shifts in the sediment sources to have been forced by sea-level changes that correlate with South Asian monsoon rainfall variation since late Pliocene time. We collected 908 samples from the International Ocean Discovery Program Hole U1456A, which was drilled in the east Arabian Sea. Time series of hematite content and grain size of the sediments were examined downcore. We found South Asian monsoon precipitation and weathering intensity experienced three phases from late Pliocene time. Lower monsoon precipitation, with a lower variability and strong weathering intensity, occurred during 3.4–2.4 Ma; an increased and more variable South Asian monsoon rainfall, along with strengthened but fluctuating weathering intensity, occurred at 1.8–1.1 Ma; and a reduced rainfall with lower South Asian monsoon precipitation variability and moderate weathering intensity marked the period 1.1–0.1 Ma. Maximum entropy spectral analysis and wavelet transform show that there were orbital-dominated cycles of periods c. 100 and c. 41 ka in these proxy-based time series. We propose that the monsoon, sea level, global temperature and insolation together forced the weathering and erosion in SW Asia.

scholarly journals Seasonal climate forecasts of the South Asian monsoon using multiple coupled models

2006 ◽  
Vol 58 (4) ◽  
pp. 487-507 ◽  
Author(s):  
Tiruvalam N. Krishnamurti ◽  
Ashis K. Mitra ◽  
Tallapragada S. V. Vijaya Kumar ◽  
Wontae T. Yun ◽  
William K. Dewar
Keyword(s):  
South Asian ◽  
Asian Monsoon ◽  
South Asian Monsoon ◽  
The South ◽  
Coupled Models ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Seasonal Climate Forecasts

scholarly journals Use of APHRODITE Rain Gauge–Based Precipitation and TRMM 3B43 Products for Improving Asian Monsoon Seasonal Precipitation Forecasts by the Superensemble Method

2014 ◽  
Vol 27 (3) ◽  
pp. 1062-1069 ◽  
Author(s):  
Akiyo Yatagai ◽  
T. N. Krishnamurti ◽  
Vinay Kumar ◽  
A. K. Mishra ◽  
Anu Simon
Keyword(s):  
Asian Monsoon ◽  
Rain Gauge ◽  
Training Data ◽  
Monthly Precipitation ◽  
Observation Data ◽  
Training Phase ◽  
Multiple Model ◽  
Seasonal Forecasts ◽  
Past Performance ◽  
Seasonal Climate

Abstract A multimodel superensemble developed by the Florida State University combines multiple model forecasts based on their past performance (training phase) to make a consensus forecast. Because observed precipitation reflects local characteristics such as orography, quantitative high-resolution precipitation products are useful for downscaling coarse model outputs. The Asian Precipitation–Highly-Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) and Tropical Rainfall Measuring Mission (TRMM) 3B43 products are used for downscaling and as training data in the superensemble training phase. Seven years (1998–2004) of monthly precipitation (June–August) over the Asian monsoon region (0°–50°N, 60°–150°E) and results of four coupled climate models were used. TRMM 3B43 was adjusted by APHRODITE (m-TRMM). For seasonal climate forecasts, a synthetic superensemble technique was used. A cross-validation technique was adopted, in which the year to be forecast was excluded from the calculations for obtaining the regression coefficients. The principal results are as follows: 1) Seasonal forecasts of Asian monsoon precipitation were considerably improved by use of APHRODITE rain gauge–based data or the m-TRMM product. These forecasts are much superior to those from the best model of the suite and ensemble mean. 2) Use of a statistical downscaling and synthetic superensemble method for multimodel forecasts of seasonal climate significantly improved precipitation prediction at higher resolution. This is confirmed by cross-evaluation of superensemble with using other observation data than the data used in the training phase. 3) Availability of a dense rain gauge network–based analysis was essential for the success of this work.

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