From climate variability to heavy precipitation – Learning transfer functions from data

2020 ◽  
Author(s):  
Michał Kałczyński ◽  
Krzysztof Krawiec ◽  
Zbigniew Kundzewicz
Keyword(s):  
Climate Variability ◽  
Transfer Functions ◽  
Southern Oscillation ◽  
Dominant Role ◽  
Heavy Precipitation ◽  
Statistical Characteristics ◽  
Decadal Climate Variability ◽  
Full Data ◽  
Data Set ◽  
Climate Oscillation

<p>The contribution deals with spatial extremes of intense precipitation at the global scale, with the help of data-driven modelling. We ask whether the inter-annual and inter-decadal climate variability track plays a dominant role in the interpretation of the variability of heavy precipitation, globally. The study aims at discovering spatially and temporally organized links between climate oscillation indices, such as El Niño-Southern Oscillation, North Atlantic Oscillation, Pacific Interdecadal Oscillation, Atlantic Multidecadal Oscillation and heavy precipitation. To this aim, we induce a range of machine-learning models, primarily recurrent neural networks, from multiple sources of global observations, including E-OBS data set from the UERRA project, GPCC Full Data Daily, and climate variability indices. The models are thoroughly tested and juxtaposed in hindcasting mode on a separate test set and scrutinized with respect to their statistical characteristics. We expect to identify climate-oscillation drivers for spatial dependence of heavy precipitation.</p>

scholarly journals Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

2014 ◽  
Vol 11 (5) ◽  
pp. 7685-7719 ◽  
Author(s):  
M. Broich ◽  
A. Huete ◽  
M. G. Tulbure ◽  
X. Ma ◽  
Q. Xin ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Land Management ◽  
Land Surface ◽  
Vegetation Index ◽  
Southern Oscillation ◽  
Quantitative Information ◽  
Decadal Climate Variability ◽  
Eastern Australia ◽  
Phenological Cycle

Abstract. Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually reoccurring patterns. Yet, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e. drylands) received much less attention, despite the fact that they cover more than 30% of the global land surface. Here we focused on Australia, the driest inhabited continent with one of the most variable rainfall climates in the world and vast areas of dryland systems. Detailed and internally consistent studies investigating phenological cycles and their response to climate variability across the entire continent designed specifically for Australian dryland conditions are missing. To fill this knowledge gap and to advance phenological research, we used existing methods more effectively to study geographic and climate-driven variability in phenology over Australia. We linked derived phenological metrics with rainfall and the Southern Oscillation Index (SOI). We based our analysis on Enhanced Vegetation Index (EVI) data from the MODerate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2013, which included extreme drought and wet years. We conducted a continent-wide investigation of the link between phenology and climate variability and a more detailed investigation over the Murray–Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles. Phenological cycle peaks occurred not only during the austral summer but at any time of the year, and their timing varied by more than a month in the interior of the continent. The phenological cycle peak magnitude and integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over north-eastern Australia and within the MDB predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of productivity) showed positive anomalies of more than two standard deviations over most of eastern Australia in 2009–2010, which coincided with the transition between the El Niño induced decadal droughts to flooding caused by La Niña. The quantified spatial-temporal variability in phenology across Australia in response to climate variability presented here provides important information for land management and climate change studies and applications.

Statistical Characteristics of Long-Term High-Resolution Precipitable Water Vapor Data at Darwin

2018 ◽  
Vol 10 (04) ◽  
pp. 1850010
Author(s):  
Kimberly Leung ◽  
Aneesh C. Subramanian ◽  
Samuel S. P. Shen
Keyword(s):  
Water Vapor ◽  
High Resolution ◽  
Southern Oscillation ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Statistical Characteristics ◽  
Convective Precipitation ◽  
Data Set ◽  
Best Estimates

This paper studies the statistical characteristics of a unique long-term high-resolution precipitable water vapor (PWV) data set at Darwin, Australia, from 12 March 2002 to 28 February 2011. To understand the convective precipitation processes for climate model development, the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) program made high-frequency radar observations of PWV at the Darwin ARM site and released the best estimates from the radar data retrievals for this time period. Based on the best estimates, we produced a PWV data set on a uniform 20-s time grid. The gridded data were sufficient to show the fractal behavior of precipitable water with Hausdorff dimension equal to 1.9. Fourier power spectral analysis revealed modulation instability due to two sideband frequencies near the diurnal cycle, which manifests as nonlinearity of an atmospheric system. The statistics of PWV extreme values and daily rainfall data show that Darwin’s PWV has El Nino Southern Oscillation (ENSO) signatures and has potential to be a predictor for weather forecasting. The right skewness of the PWV data was identified, which implies an important property of tropical atmosphere: ample capacity to hold water vapor. The statistical characteristics of this long-term high-resolution PWV data will facilitate the development and validation of climate models, particularly stochastic models.

El Niño Southern Oscillation and decadal climate variability impacts on crop yields and adaptation value.

2021 ◽  
Vol 16 (43) ◽  
pp. 1-13
Author(s):  
Minglu Wang ◽  
◽  
Yu-Kai Huang ◽  
Muxi Cheng ◽  
Bingru Sheng ◽  
...  
Keyword(s):  
Climate Variability ◽  
El Niño ◽  
Crop Yields ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Decadal Climate Variability ◽  
Research Issues ◽  
Decadal Climate

Ocean-atmospheric phenomena (OAP) have been found to be associated with regional climate variability and, in turn, agricultural production. Previous research has shown that advance information on OAP and its climate implications could provide valuable opportunities to adjust agriculture practices. In this study, we review OAP effects on crop yields, covering both shorter-term El Niño Southern Oscillation (ENSO) and longer-term ocean-related decadal climate variability (DCV) phenomena, such as Pacific Decadal Oscillation (PDO), the Tropical Atlantic Gradient (TAG), and the West Pacific Warm Pool (WPWP). We review both statistical approaches and simulation models that have been used to assess OAP impacts on crop yields. Findings show heterogeneous impacts across crops, regions, OAP phases, and seasons. Evidence also indicates that more frequent and extreme OAP phases would damage agriculture. However, economic gains could be achieved via adaptation strategies responding to the early release of OAP phase information. Discussions on current knowledge gaps and future research issues are included.

Learning recurrent transfer functions from data: From climate variability to high river discharge

2020 ◽  
Author(s):  
Mateusz Norel ◽  
Krzysztof Krawiec ◽  
Zbigniew Kundzewicz
Keyword(s):  
Time Series ◽  
River Discharge ◽  
Short Term Memory ◽  
Transfer Functions ◽  
Southern Oscillation ◽  
Flood Hazard ◽  
Atlantic Multidecadal Oscillation ◽  
Statistical Characteristics ◽  
Learning Tools ◽  
Discharge Data

<p>Interpretation of flood hazard and its variability remains a major challenge for climatologists, hydrologists and water management experts. This study investigates the existence of links between variability in high river discharge, worldwide, and inter-annual and inter-decadal climate oscillation indices: El Niño-Southern Oscillation, North Atlantic Oscillation, Pacific Interdecadal Oscillation, and Atlantic Multidecadal Oscillation. Global river discharge data used here stem from the ERA-20CM-R reconstruction at 0.5 degrees resolution and form a multidimensional time series, with each observation being a spatial matrix of estimated discharge volume. Elements of matrices aligned spatially form time series which were used to induce dedicated predictive models using machine learning tools, including multivariate regression (e.g. ARMA) and recurrent neural networks (RNNs), in particular the Long Short Term Memory model (LSTM) that proved to be effective in many other application areas. The models are thoroughly tested and juxtaposed in hindcasting mode on a separate test set and scrutinized with respect to their statistical characteristics. We hope to be able to contribute to improvement of interpretation of variability of flood hazard and reduction of uncertainty.</p>

scholarly journals Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

2014 ◽  
Vol 11 (18) ◽  
pp. 5181-5198 ◽  
Author(s):  
M. Broich ◽  
A. Huete ◽  
M. G. Tulbure ◽  
X. Ma ◽  
Q. Xin ◽  
...  
Keyword(s):  
Climate Variability ◽  
Land Surface ◽  
Spatial Information ◽  
Southern Oscillation ◽  
Austral Summer ◽  
Decadal Climate Variability ◽  
Eastern Australia ◽  
Agricultural Applications ◽  
Fuel Accumulation ◽  
Phenological Metrics

Abstract. Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative spatial information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually recurring patterns. However, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e., drylands) received much less attention, despite the fact that they cover more than 30% of the global land surface. Here, we focused on Australia, a continent with one of the most variable rainfall climates in the world and vast areas of dryland systems, where a detailed phenological investigation and a characterization of the relationship between phenology and climate variability are missing. To fill this knowledge gap, we developed an algorithm to characterize phenological cycles, and analyzed geographic and climate-driven variability in phenology from 2000 to 2013, which included extreme drought and wet years. We linked derived phenological metrics to rainfall and the Southern Oscillation Index (SOI). We conducted a continent-wide investigation and a more detailed investigation over the Murray–Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles across Australia. The peak of phenological cycles occurred not only during the austral summer, but also at any time of the year, and their timing varied by more than a month in the interior of the continent. The magnitude of the phenological cycle peak and the integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over northeastern Australia and within the MDB, predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of vegetation productivity) showed positive anomalies of more than 2 standard deviations over most of eastern Australia in 2009–2010, which coincided with the transition from the El Niño-induced decadal droughts to flooding caused by La Niña.

Multi-decadal climate variability, New South Wales, Australia

2004 ◽  
Vol 49 (7) ◽  
pp. 133-140 ◽  
Author(s):  
S.W. Franks
Keyword(s):  
Climate Variability ◽  
General Circulation ◽  
Decadal Variability ◽  
Southern Oscillation ◽  
New South ◽  
New South Wales ◽  
General Circulation Models ◽  
Drought Risk ◽  
Decadal Climate Variability ◽  
South Wales

Traditional hydrological risk estimation has treated the observations of hydro-climatological extremes as being independent and identically distributed, implying a static climate risk. However, recent research has highlighted the persistence of multi-decadal epochs of distinct climate states across New South Wales (NSW), Australia. Climatological studies have also revealed multi-decadal variability in the magnitude and frequency of El Niño/Southern Oscillation (ENSO) impacts. In this paper, examples of multi-decadal variability are presented with regard to flood and drought risk. The causal mechanisms for the observed variability are then explored. Finally, it is argued that the insights into climate variability provide (a) useful lead time for forecasting seasonal hydrological risk, (b) a strong rationale for a new framework for hydrological design and (c) a strong example of natural climate variability for use in the testing of General Circulation Models of climate change.

Relationships between Climate Variability and Fluctuations in Daily Precipitation over the United States

2007 ◽  
Vol 20 (14) ◽  
pp. 3561-3579 ◽  
Author(s):  
R. W. Higgins ◽  
V. B. S. Silva ◽  
W. Shi ◽  
J. Larson
Keyword(s):  
United States ◽  
Climate Variability ◽  
Climate Models ◽  
Daily Precipitation ◽  
Southern Oscillation ◽  
Heavy Precipitation ◽  
The United States ◽  
Global Scale ◽  
The Arctic ◽  
Teleconnection Patterns

Abstract Fluctuations in the frequency of daily precipitation occurrence and in the intensity of daily precipitation over the United States during the period 1948–2004 are identified and linked to leading sources of interannual and interdecadal climate variability. The El Niño–Southern Oscillation (ENSO) phenomena are implicated in interannual fluctuations while the Pacific decadal oscillation (PDO) and the Arctic Oscillation (AO) are linked to recent interdecadal fluctuations. For the conterminous United States as a whole there have been increases in the annual frequency of occurrence of wet days and heavy precipitation days and in the mean daily and annual total precipitation over the past several decades, though these changes have not been uniform. The possibility of significant natural forcing of these interdecadal variations in precipitation is explored. It is shown that the PDO is associated with these fluctuations over the western and southern United States, while the AO is also associated with them but to a much lesser extent over the southeastern United States. Because the interdecadal fluctuations are linked to changes in the global-scale circulation and sea surface temperatures associated with the PDO, the results imply that a significant portion of the skill of climate models in anticipating fluctuations in daily precipitation statistics over the United States will arise from an ability to forecast the temporal and spatial variability of the interdecadal shifts in tropical precipitation and in the associated teleconnection patterns into the midlatitudes.

El Niño southern oscillation and decadal climate variability impacts on crop yields and adaptation value.

2021 ◽  
Vol 16 (043) ◽  
Author(s):  
Ming-lu Wang
Keyword(s):  
Climate Variability ◽  
El Niño ◽  
Crop Yields ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Decadal Climate Variability ◽  
Research Issues ◽  
Decadal Climate

Abstract Ocean-atmospheric phenomena (OAP) have been found to be associated with regional climate variability and, in turn, agricultural production. Previous research has shown that advance information on OAP and its climate implications could provide valuable opportunities to adjust agriculture practices. In this study, we review OAP effects on crop yields, covering both shorter-term El Niño Southern Oscillation (ENSO) and longer-term ocean-related decadal climate variability (DCV) phenomena, such as Pacific Decadal Oscillation (PDO), the Tropical Atlantic Gradient (TAG), and the West Pacific Warm Pool (WPWP). We review both statistical approaches and simulation models that have been used to assess OAP impacts on crop yields. Findings show heterogeneous impacts across crops, regions, OAP phases, and seasons. Evidence also indicates that more frequent and extreme OAP phases would damage agriculture. However, economic gains could be achieved via adaptation strategies responding to the early release of OAP phase information. Discussions on current knowledge gaps and future research issues are included.

scholarly journals A New Paradigm for South Pacific Climate Variability and Predictability

2021 ◽  
Author(s):  
Jiale Lou ◽  
Terence O'Kane ◽  
Neil Holbrook
Keyword(s):  
Climate Variability ◽  
Southern Oscillation ◽  
South Pacific ◽  
Decadal Climate Variability ◽  
The South ◽  
New Paradigm ◽  
Coherent Noise ◽  
Decadal Climate ◽  
South Pacific Climate ◽  
Pacific Climate Variability

Abstract Pacific climate variability is largely understood based on El Niño–Southern Oscillation (ENSO), the North Pacific focused Pacific decadal oscillation (PDO) and/or the whole of Pacific region interdecadal Pacific oscillation – which respectively represent the dominant modes of interannual and decadal climate variability. However, the role of the South Pacific, including atmospheric drivers and cross-scale interactions between interannual and decadal climate variability, has received considerably less attention. Here we propose a new paradigm for South Pacific climate variability whereby the Pacific-South American (PSA) mode, characterised by two mid-tropospheric modes (PSA1 and PSA2), provides coherent noise forcing that acts to excite multiple spatiotemporal scales of oceanic responses in the upper South Pacific Ocean ranging from seasonal to decadal. While PSA1 has long been recognised as highly correlated with ENSO, we find that PSA2 is critically important in generating a sea surface temperature (SST) quadrupole pattern in the extratropical South Pacific. This sets up a precursor that optimally determines the predictability and evolution of SST 9 months in advance of the peak phases of both the leading South Pacific SST mode and ENSO. Our results show that the atmospheric PSA mode is the key driver of oceanic variability in the South Pacific subtropics.

CONSIDERATION OF THE VALIDITY OF THE STATISTICAL CHARACTERISTICS OF pH IN SURFACE WATERS

2019 ◽  
Vol 13 (2) ◽  
pp. 52-58
Author(s):  
V. B. Korobov ◽  
I. V. Miskevich ◽  
A. S. Lokhov ◽  
K. A. Seredkin
Keyword(s):  
Field Measurements ◽  
Heavy Precipitation ◽  
The State ◽  
Statistical Characteristics ◽  
Mean Values ◽  
River Waters ◽  
Ph Values ◽  
State Of Water ◽  
Environment Temperature ◽  
Unit Characteristic

Abstract: pH is one of the most important parameters characterizing the state of water systems. The arithmetic mean values of samples are often used when averaging serial pH measurements in water bodies, as is usually done for other characteristics of the state of the natural environment (temperature, salinity, oxygen concentrations, suspended solids, etc.). However, in this case such an operation is illegal, since the addition of logarithms, which by definition are pH, is non-additive. The authors conducted a study to determine the extent to which pH variability in natural objects such an operation would not distort the results. For this, several samples of the pH index were generated in various ranges of its theoretically possible and natural variability. It was established that with pH variability of less than a unit characteristic of marine pH values, the statistical characteristics of the indicator and [H+ ] concentrations differ slightly, and the medians of the samples coincide. It is concluded that with such ranges characteristic of the waters of the oceans, there is no need to recalculate previously obtained results. However, for the estuaries of rivers flowing into tidal seas, as shown by field measurements, the pH variability in the mixing zone of sea and river waters is several times higher. Similar situations may occur when heavy precipitation falls on the water surface, as well as during floods. In these cases, a simple averaging of the pH values will no longer be correct. In such cases, the use of other averaging algorithms and the choice of stable statistical characteristics are required.

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