The Dependence of Tropical Modes of Variability on Zonal Asymmetry

2021 ◽  
Author(s):  
Xiaoning Wu ◽  
Kevin A. Reed ◽  
Christopher L. P. Wolfe ◽  
Gustavo M. Marques ◽  
Scott D. Bachman ◽  
...  
Keyword(s):  
Modes Of Variability ◽  
Zonal Asymmetry

scholarly journals The Dependence of Tropical Modes of Variability on Zonal Asymmetry

2021 ◽  
Author(s):  
Xiaoning Wu ◽  
Kevin A. Reed ◽  
Christopher Lee Pitt Wolfe ◽  
Gustavo M Marques ◽  
Scott Daniel Bachman ◽  
...  
Keyword(s):  
Modes Of Variability ◽  
Zonal Asymmetry

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.

“Modes of Variability” and Climate Change

2009 ◽  
Vol 22 (10) ◽  
pp. 2639-2658 ◽  
Author(s):  
Grant Branstator ◽  
Frank Selten
Keyword(s):  
Climate Change ◽  
Stochastic Model ◽  
General Circulation ◽  
Climate Changes ◽  
Circulation Model ◽  
Meridional Wind ◽  
Model Atmosphere ◽  
Linear Pattern ◽  
Modes Of Variability ◽  
Mean State

Abstract A 62-member ensemble of coupled general circulation model (GCM) simulations of the years 1940–2080, including the effects of projected greenhouse gas increases, is examined. The focus is on the interplay between the trend in the Northern Hemisphere December–February (DJF) mean state and the intrinsic modes of variability of the model atmosphere as given by the upper-tropospheric meridional wind. The structure of the leading modes and the trend are similar. Two commonly proposed explanations for this similarity are considered. Several results suggest that this similarity in most respects is consistent with an explanation involving patterns that result from the model dynamics being well approximated by a linear system. Specifically, the leading intrinsic modes are similar to the leading modes of a stochastic model linearized about the mean state of the GCM atmosphere, trends in GCM tropical precipitation appear to excite the leading linear pattern, and the probability density functions (PDFs) of prominent circulation patterns are quasi-Gaussian. There are, on the other hand, some subtle indications that an explanation for the similarity involving preferred states (which necessarily result from nonlinear influences) has some relevance. For example, though unimodal, PDFs of prominent patterns have departures from Gaussianity that are suggestive of a mixture of two Gaussian components. And there is some evidence of a shift in probability between the two components as the climate changes. Interestingly, contrary to the most prominent theory of the influence of nonlinearly produced preferred states on climate change, the centroids of the components also change as the climate changes. This modification of the system’s preferred states corresponds to a change in the structure of its dominant patterns. The change in pattern structure is reproduced by the linear stochastic model when its basic state is modified to correspond to the trend in the general circulation model’s mean atmospheric state. Thus, there is a two-way interaction between the trend and the modes of variability.

scholarly journals Two major modes of variability of the East Asian summer monsoon

2010 ◽  
Vol 136 (649) ◽  
pp. 829-841 ◽  
Author(s):  
Xuguang Sun ◽  
Richard J. Greatbatch ◽  
Wonsun Park ◽  
Mojib Latif
Keyword(s):  
Summer Monsoon ◽  
East Asian Summer Monsoon ◽  
Asian Summer Monsoon ◽  
East Asian ◽  
Modes Of Variability ◽  
Asian Summer

scholarly journals Atmospheric tropical modes are important drivers of Sahelian springtime heatwaves

2020 ◽  
Author(s):  
Kiswendsida H. Guigma ◽  
Françoise Guichard ◽  
Martin Todd ◽  
Philippe Peyrille ◽  
Yi Wang
Keyword(s):  
Large Scale ◽  
Geographic Location ◽  
Longwave Radiation ◽  
Potential Predictability ◽  
Modes Of Variability ◽  
Physical Mechanisms ◽  
Hot Air ◽  
Western Sahel ◽  
Maximum Temperatures ◽  
Sahel Region

AbstractHeatwaves pose a serious threat to human health worldwide but remain poorly documented over Africa. This study uses mainly the ERA5 dataset to investigate their large-scale drivers over the Sahel region during boreal spring, with a focus on the role of tropical modes of variability including the Madden–Julian Oscillation (MJO) and the equatorial Rossby and Kelvin waves. Heatwaves were defined from daily minimum and maximum temperatures using a methodology that retains only intraseasonal scale events of large spatial extent. The results show that tropical modes have a large influence on the occurrence of Sahelian heatwaves, and, to a lesser extent, on their intensity. Depending on their convective phase, they can either increase or inhibit heatwave occurrence, with the MJO being the most important of the investigated drivers. A certain sensitivity to the geographic location and the diurnal cycle is observed, with nighttime heatwaves more impacted by the modes over the eastern Sahel and daytime heatwaves more affected over the western Sahel. The examination of the physical mechanisms shows that the modulation is made possible through the perturbation of regional circulation. Tropical modes thus exert a control on moisture and the subsequent longwave radiation, as well as on the advection of hot air. A detailed case study of a major event, which took place in April 2003, further supports these findings. Given the potential predictability offered by tropical modes at the intraseasonal scale, this study has key implications for heatwave risk management in the Sahel.

Reexamining the tropical Atlantic influence on ENSO using perfect model predictability experiments

2021 ◽  
Author(s):  
Ingo Richter ◽  
Yu Kosaka ◽  
Hiroki Tokinaga ◽  
Shoichiro Kido
Keyword(s):  
Initial Conditions ◽  
Boreal Summer ◽  
Similar Amount ◽  
Tropical Atlantic ◽  
Equatorial Atlantic ◽  
Boreal Spring ◽  
Modes Of Variability ◽  
Control Simulation ◽  
Starting Point ◽  
Perfect Model

<p>The potential influence of the tropical Atlantic on the development of ENSO has received increased attention over recent years. In particular equatorial Atlantic variability (also known as the Atlantic zonal mode or AZM) has been shown to be anticorrelated with ENSO, i.e. cold AZM events in boreal summer (JJA) tend to be followed by El Niño in winter (DJF), and vice versa for warm AZM events. One problem with disentangling the two-way interaction between the equatorial Atlantic and Pacific is that both ENSO and the AZM tend to develop in boreal spring (MAM).</p><p>Here we use a set of GCM sensitivity experiments to quantify the strength of the Atlantic-Pacific link. The starting point is a 1000-year free-running control simulation with the GFDL CM 2.1 model. From this control simulation, we pick years in which a cold AZM event in JJA is followed by an El Niño in DJF. These years serve as initial conditions for “perfect model” prediction experiments with 10 ensemble members each. In the control experiments, the predictions evolve freely for 12 months from January 1 of each selected year. In the second set of predictions, SSTs are gradually relaxed to climatology in the tropical Atlantic, so that the cold AZM event is suppressed. In the third set of predictions, we restore the tropical Pacific SSTs to climatology, so that the El Niño event is suppressed.</p><p>The results suggest that, on average, the tropical Atlantic SST anomalies increase the strength of El Niño in the following winter by about 10-20%. If, on the other hand, El Niño development is suppressed, the amplitude of the cold AZM event also reduces by a similar amount. The results suggest that, in the context of this GCM, the influence of AZM events on ENSO development is relatively weak but not negligible. The fact that ENSO also influences the AZM in boreal spring highlights the complex two-way interaction between these two modes of variability.</p>

scholarly journals Meteorological modes of variability for fine particulate matter (PM2.5) air quality in the United States: implications for PM2.5 sensitivity to climate change

2012 ◽  
Vol 12 (6) ◽  
pp. 3131-3145 ◽  
Author(s):  
A. P. K. Tai ◽  
L. J. Mickley ◽  
D. J. Jacob ◽  
E. M. Leibensperger ◽  
L. Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Transport Model ◽  
Circulation Model ◽  
Multiple Linear Regression Model ◽  
The United States ◽  
Chemical Transport Model ◽  
Modes Of Variability ◽  
The Us ◽  
Cyclone Frequency

Abstract. We applied a multiple linear regression model to understand the relationships of PM2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM2.5 to climate change. We used 2004–2008 PM2.5 observations from ~1000 sites (~200 sites for PM2.5 components) and compared to results from the GEOS-Chem chemical transport model (CTM). All data were deseasonalized to focus on synoptic-scale correlations. We find strong positive correlations of PM2.5 components with temperature in most of the US, except for nitrate in the Southeast where the correlation is negative. Relative humidity (RH) is generally positively correlated with sulfate and nitrate but negatively correlated with organic carbon. GEOS-Chem results indicate that most of the correlations of PM2.5 with temperature and RH do not arise from direct dependence but from covariation with synoptic transport. We applied principal component analysis and regression to identify the dominant meteorological modes controlling PM2.5 variability, and show that 20–40% of the observed PM2.5 day-to-day variability can be explained by a single dominant meteorological mode: cold frontal passages in the eastern US and maritime inflow in the West. These and other synoptic transport modes drive most of the overall correlations of PM2.5 with temperature and RH except in the Southeast. We show that interannual variability of PM2.5 in the US Midwest is strongly correlated with cyclone frequency as diagnosed from a spectral-autoregressive analysis of the dominant meteorological mode. An ensemble of five realizations of 1996–2050 climate change with the GISS general circulation model (GCM) using the same climate forcings shows inconsistent trends in cyclone frequency over the Midwest (including in sign), with a likely decrease in cyclone frequency implying an increase in PM2.5. Our results demonstrate the need for multiple GCM realizations (because of climate chaos) when diagnosing the effect of climate change on PM2.5, and suggest that analysis of meteorological modes of variability provides a computationally more affordable approach for this purpose than coupled GCM-CTM studies.

scholarly journals On The Determinations of Weather, Seasonal, Sub-Seasonal and Climate Scale Variability and Overall Trends in the Atmosphere and Ocean

2020 ◽  
pp. 1-17
Author(s):  
LJ Pietrafesa ◽  
◽  
S Bao ◽  
Keyword(s):  
Climate Variability ◽  
Seasonal Variability ◽  
Atmospheric Temperature ◽  
Global Ocean ◽  
Remotely Sensed Data ◽  
Modes Of Variability ◽  
Multi Scale ◽  
Naturally Occurring ◽  
Climate Scale ◽  
Coastal Water Level

The traditional concepts and definitions of multi-scale “weather”, “seasonal variability”, “sub-seasonal variability”, “climate variability”, “trends” and “climate change” for both the global atmosphere and the global ocean are considered. We build upon existing literature and present new evidence that atmospheric and oceanic temporal multi-scale variability are the result of a mix of well-known frequency and amplitude modulated nonlinear and phenomena that occur simultaneously [1-3]. We harvest representative atmospheric temperature and wind data, oceanic temperature and coastal water level from United States (U.S.) and United Kingdom (U.K.) agency archives, collected via in-situ and satellite remotely sensed data and employ a mathematical methodology that can decompose nonlinear data. The data decomposition reveals a continuum of well-defined, modulated, internal modes of oscillations, each with broad spectral peaks and each representative of naturally occurring phenomena. We reveal that the conventional notions of weather and seasonal to subseasonal to climate variability, actually constitute an over-lapping continuum, with shorter period oscillations commuting with longer period oscillations onto overall record length trends. We relate these internal, intrinsic modes of variability to naturally occurring causal agents, from relatively high frequency weather to lower frequency seasonal to sub-seasonal to climate scale variability. Correlative relationships between climate factors reveal causal couplings of the oceanic and atmospheric systems.

Sign in / Sign up

Export Citation Format

Share Document