scholarly journals Influence of natural climate variability on extreme wave power over Indo-Pacific Ocean assessed using ERA5

2021 ◽  
Author(s):  
Prashant Kumar ◽  
Sukhwinder Kaur ◽  
Evan Weller ◽  
Ian R. Young
Keyword(s):  
Pacific Ocean ◽  
Climate Variability ◽  
Wave Power ◽  
Extreme Wave ◽  
Natural Climate Variability ◽  
Natural Climate

scholarly journals Influence of Natural Climate Variability on Extreme Wave Power over Indo-Pacific Ocean Assessed using ERA5

2021 ◽  
Author(s):  
Prashant Kumar ◽  
Sukhwinder Kaur ◽  
Evan Weller ◽  
Ian R. Young
Keyword(s):  
Pacific Ocean ◽  
Indian Ocean ◽  
Climate Variability ◽  
El Niño ◽  
El Nino ◽  
Extreme Value Analysis ◽  
Wave Power ◽  
Central Pacific ◽  
Natural Climate ◽  
The Impact

Abstract In recent decades, wave power (WP) energy from the ocean is one of the cleanest renewable energy sources associated with oceanic warming. In Indo-Pacific Ocean, the WP is significantly influenced by natural climate variabilities, such as El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Pacific Decadal Oscillation (PDO). In this study, the impact of major climate variability modes on seasonal extreme WP is examined over the period 1979–2019 using ERA5 reanalysis data and the non-stationary generalized extreme value analysis is applied to estimate the climatic extremes. Independent ENSO influence after removing the IOD trends (ENSO|IOD) on WP are evident over the eastern and central Pacific during December–February (DJF) and March–May (MAM), respectively, which subsequently shifts towards the western Pacific in June–August (JJA) and September–November (SON). The ENSO|PDO impact on WP exhibits similar yet weaker intensity year round compared to ENSO. Extreme WP responses due to the IOD|ENSO include widespread decreases over the tropical and eastern Indian Ocean (IO), with localized increases only over the South China and Philippine (SCP) seas and Bay of Bengal (BOB) during JJA, and the Arabian Sea during SON. Lastly, for the PDO|ENSO, the significant increases in WP are mostly confined to the Pacific, and most prominent in the North Pacific. Composite analysis of different phase combinations of PDO (IOD) with El Niño (La Niña) reveals stronger (weaker) influences year-round. The response patterns in significant wave height (SWH), peak wave period (PWP), sea surface temperatures (SST), and sea level pressure (SLP) helps to explain the seasonal variations in WP.

scholarly journals Quantifying the influence of natural climate variability on in situ measurements of seasonal total and extreme daily precipitation

2021 ◽  
Author(s):  
Mark D. Risser ◽  
Michael F. Wehner ◽  
John P. O’Brien ◽  
Christina M. Patricola ◽  
Travis A. O’Brien ◽  
...  
Keyword(s):  
Climate Variability ◽  
Extreme Precipitation ◽  
Southern Oscillation ◽  
Statistical Significance ◽  
In Situ Measurements ◽  
Climate Indices ◽  
Natural Climate Variability ◽  
Modes Of Variability ◽  
Natural Climate

AbstractWhile various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United States. In this work, we introduce a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) in seasonal mean and extreme precipitation. An important aspect of our analysis is that we simultaneously isolate the individual effects of seven modes of variability while explicitly controlling for joint inter-mode relationships. Our method utilizes a spatial statistical component that uses in situ measurements to resolve relationships to their native scales; furthermore, we use a data-driven procedure to robustly determine statistical significance. In Part I of this work we focus on natural climate variability: detection is mostly limited to DJF and SON for the modes of variability considered, with the El Niño/Southern Oscillation, the Pacific–North American pattern, and the North Atlantic Oscillation exhibiting the largest influence. Across all climate indices considered, the signals are larger and can be detected more clearly for seasonal total versus extreme precipitation. We are able to detect at least some significant relationships in all seasons in spite of extremely large (> 95%) background variability in both mean and extreme precipitation. Furthermore, we specifically quantify how the spatial aspect of our analysis reduces uncertainty and increases detection of statistical significance while also discovering results that quantify the complex interconnected relationships between climate drivers and seasonal precipitation.

scholarly journals Recent natural variability in global warming weakened phenological mismatch and selection on seasonal timing in great tits ( Parus major )

2021 ◽  
Vol 288 (1963) ◽  
Author(s):  
Marcel E. Visser ◽  
Melanie Lindner ◽  
Phillip Gienapp ◽  
Matthew C. Long ◽  
Stephanie Jenouvrier
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Climate Variability ◽  
Parus Major ◽  
Great Tit ◽  
Trophic Levels ◽  
Natural Climate Variability ◽  
Seasonal Timing ◽  
Phenological Mismatch ◽  
Natural Climate

Climate change has led to phenological shifts in many species, but with large variation in magnitude among species and trophic levels. The poster child example of the resulting phenological mismatches between the phenology of predators and their prey is the great tit ( Parus major ), where this mismatch led to directional selection for earlier seasonal breeding. Natural climate variability can obscure the impacts of climate change over certain periods, weakening phenological mismatching and selection. Here, we show that selection on seasonal timing indeed weakened significantly over the past two decades as increases in late spring temperatures have slowed down. Consequently, there has been no further advancement in the date of peak caterpillar food abundance, while great tit phenology has continued to advance, thereby weakening the phenological mismatch. We thus show that the relationships between temperature, phenologies of prey and predator, and selection on predator phenology are robust, also in times of a slowdown of warming. Using projected temperatures from a large ensemble of climate simulations that take natural climate variability into account, we show that prey phenology is again projected to advance faster than great tit phenology in the coming decades, and therefore that long-term global warming will intensify phenological mismatches.

scholarly journals In Memoriam: Keith R Briffa, 1952–2017

The Holocene ◽  
2018 ◽  
Vol 28 (10) ◽  
pp. 1549-1553
Author(s):  
Timothy J Osborn ◽  
Philip D Jones ◽  
Edward R Cook
Keyword(s):  
Climate Variability ◽  
Late Holocene ◽  
Historical Context ◽  
Northern Eurasia ◽  
Primary Research ◽  
Natural Climate Variability ◽  
Climatic Trends ◽  
In Memoriam ◽  
Ring Analysis ◽  
Natural Climate

Keith R Briffa was one of the most influential palaeoclimatologists of the last 30 years. His primary research interests lay in Late-Holocene climate change with a geographical emphasis on northern Eurasia. His greatest impact was in the field of dendroclimatology, a field that he helped to shape. His contributions have been seminal to the development of sound methods for tree-ring analysis and in their proper application to allow the interpretation of climate variability from tree rings. This led to the development of many important records that allow us to understand natural climate variability on timescales from years to millennia and to set recent climatic trends in their historical context.

scholarly journals Impact of natural climate variability on runoff based on Monte Carlo method

2017 ◽  
Vol 10 (2) ◽  
pp. 344-359 ◽  
Author(s):  
Jie Yang ◽  
Jianxia Chang ◽  
Jun Yao ◽  
Yimin Wang ◽  
Qiang Huang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Climate Variability ◽  
Natural Condition ◽  
Global Climate ◽  
Test Method ◽  
Natural Climate Variability ◽  
Local Interference ◽  
Natural Climate ◽  
The Impact

Abstract Studying the impact of climate variability is important for the rational utilization of water resources, especially in the case of intensified global climate variability. Climate variability can be caused by natural climate variability or human-caused climate variability. The analysis of Jinghe River Basin (JRB) may not be comprehensive because few studies have concentrated on natural climate variability. Therefore, the primary goal is to explore the impact of natural climate variability on runoff. A modified Mann–Kendall test method was adopted to analyze the aberrance point to determine the natural condition period during which runoff was only influenced by natural climate variability. Then, the Monte Carlo method was employed to extract segments of monthly runoff in the natural condition period and combine them to construct a long series to reduce the instability. Results indicate that the percentage of runoff variability affected by natural climate variability is 30.52% at a confidence level of 95%. Next, a topography-based hydrological model and climate elasticity method were used to simulate runoff after the aberrance point without considering the impact caused by local interference. Through a comparison of the measured and simulated runoff, we discovered that local interference has the greatest impact on runoff in the JRB.

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