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.

The Argo Project: Global Ocean Observations for Understanding and Prediction of Climate Variability

2002 ◽  
Author(s):  
Dean H. Roemmich ◽  
Russ E. Davis ◽  
Stephen C. Riser ◽  
W. B. Owens ◽  
Robert L. Molinari ◽  
...  
Keyword(s):  
Climate Variability ◽  
Global Ocean ◽  
Ocean Observations ◽  
Argo Project

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.

A Statistical Model of Cloud Vertical Structure Based on Reconciling Cloud Layer Amounts Inferred from Satellites and Radiosonde Humidity Profiles

2005 ◽  
Vol 18 (17) ◽  
pp. 3587-3605 ◽  
Author(s):  
William B. Rossow ◽  
Yuanchong Zhang ◽  
Junhong Wang
Keyword(s):  
Statistical Model ◽  
Vertical Structure ◽  
Atmospheric Temperature ◽  
Radiative Flux ◽  
Cloud Layer ◽  
Cloud Base ◽  
Polar Regions ◽  
Flux Divergence ◽  
Climate Scale ◽  
Humidity Profiles

Abstract To diagnose how cloud processes feed back on weather- and climate-scale variations of the atmosphere requires determining the changes that clouds produce in the atmospheric diabatic heating by radiation and precipitation at the same scales of variation. In particular, not only the magnitude of these changes must be quantified but also their correlation with atmospheric temperature variations; hence, the space–time resolution of the cloud perturbations must be sufficient to account for the majority of these variations. Although extensive new global cloud and radiative flux datasets have recently become available, the vertical profiles of clouds and consequent radiative flux divergence have not been systematically measured covering weather-scale variations from about 100 km, 3 h up to climate-scale variations of 10 000 km, decadal inclusive. By combining the statistics of cloud layer occurrence from the International Satellite Cloud Climatology Project (ISCCP) and an analysis of radiosonde humidity profiles, a statistical model has been developed that associates each cloud type, recognizable from satellite measurements, with a particular cloud vertical structure. Application of this model to the ISCCP cloud layer amounts produces estimates of low-level cloud amounts and average cloud-base pressures that are quantitatively closer to observations based on surface weather observations, capturing the variations with latitude and season and land and ocean (results are less good in the polar regions). The main advantage of this statistical model is that the correlations of cloud vertical structure with meteorology are qualitatively similar to “classical” information relating cloud properties to weather. These results can be evaluated and improved with the advent of satellites that can directly probe cloud vertical structures over the globe, providing statistics with changing meteorological conditions.

Detecting Climate Fingerprints in Southern Ocean Carbon using a Biogeochemical Ocean Model

2021 ◽  
Author(s):  
Rebecca Wright ◽  
Corinne Le Quéré ◽  
Erik Buitenhuis ◽  
Dorothee Bakker
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Southern Ocean ◽  
Observational Data ◽  
Ecosystem Dynamics ◽  
Global Ocean ◽  
Subsurface Waters ◽  
Climatic Drivers ◽  
Ocean Carbon ◽  
And Storage

<p>The Southern Ocean plays an important role in the uptake, transport and storage of carbon by the global oceans. These properties are dominated by the response to the rise in anthropogenic CO<sub>2</sub> in the atmosphere, but they are modulated by climate variability and climate change. Here we explore the effect of climate variability and climate change on ocean carbon uptake and storage in the Southern Ocean. We assess the extent to which climate change may be distinguishable from the anthropogenic CO<sub>2</sub> signal and from the natural background variability. We use a combination of biogeochemical ocean modelling and observations from the GLODAPv2020 database to detect climate fingerprints in dissolved inorganic carbon (DIC).</p><p>We conduct an ensemble of hindcast model simulations of the period 1920-2019, using a global ocean biogeochemical model which incorporates plankton ecosystem dynamics based on twelve plankton functional types. We use the model ensemble to isolate the changes in DIC due to rising anthropogenic CO<sub>2</sub> alone and the changes due to climatic drivers (both climate variability and climate change), to determine their relative roles in the emerging total DIC trends and patterns. We analyse these DIC trends for a climate fingerprint over the past four decades, across spatial scales from the Southern Ocean, to basin level and down to regional ship transects. Highly sampled ship transects were extracted from GLODAPv2020 to obtain locations with the maximum spatiotemporal coverage, to reduce the inherent biases in patchy observational data. Model results were sampled to the ship transects to compare the climate fingerprints directly to the observational data.</p><p>Model results show a substantial change in DIC over a 35-year period, with a range of more than +/- 30 µmol/L. In the surface ocean, both anthropogenic CO<sub>2</sub> and climatic drivers act to increase DIC concentration, with the influence of anthropogenic CO<sub>2</sub> dominating at lower latitudes and the influence of climatic drivers dominating at higher latitudes. In the deep ocean, the anthropogenic CO<sub>2</sub> generally acts to increase DIC except in the subsurface waters at lower latitudes, while climatic drivers act to decrease DIC concentration. The combined fingerprint of anthropogenic CO<sub>2</sub> and climatic drivers on DIC concentration is for an increasing trend at the surface and decreasing trends in low latitude subsurface waters. Preliminary comparison of the model fingerprints to observational ship transects will also be presented.</p>

scholarly journals Assessing the relationship between environmental factors and malaria vector breeding sites in Swaziland using multi-scale remotely sensed data

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Sabelo Nick Dlamini ◽  
Jonas Franke ◽  
Penelope Vounatsou
Keyword(s):  
Environmental Factors ◽  
Spatial Resolution ◽  
Malaria Vector ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Breeding Sites ◽  
Multi Scale ◽  
The Relationship

Many entomological studies have analyzed remotely sensed data to assess the relationship between malaria vector distribution and the associated environmental factors. However, the high cost of remotely sensed products with high spatial resolution has often resulted in analyses being conducted at coarse scales using open-source, archived remotely sensed data. In the present study, spatial prediction of potential breeding sites based on multi-scale remotely sensed information in conjunction with entomological data with special reference to presence or absence of larvae was realized. Selected water bodies were tested for mosquito larvae using the larva scooping method, and the results were compared with data on land cover, rainfall, land surface temperature (LST) and altitude presented with high spatial resolution. To assess which environmental factors best predict larval presence or absence, Decision Tree methodology and logistic regression techniques were applied. Both approaches showed that some environmental predictors can reliably distinguish between the two alternatives (existence and non-existence of larvae). For example, the results suggest that larvae are mainly present in very small water pools related to human activities, such as subsistence farming that were also found to be the major determinant for vector breeding. Rainfall, LST and altitude, on the other hand, were less useful as a basis for mapping the distribution of breeding sites. In conclusion, we found that models linking presence of larvae with high-resolution land use have good predictive ability of identifying potential breeding sites.

Alternate history: A synthetic ensemble of ocean chlorophyll concentrations

2021 ◽  
Author(s):  
Geneviève Elsworth ◽  
Nicole Lovenduski ◽  
Karen McKinnon
Keyword(s):  
Climate Variability ◽  
Internal Variability ◽  
Ensemble Member ◽  
Global Ocean ◽  
Large Ensemble ◽  
Internal Climate Variability ◽  
Surface Ocean ◽  
Large Ensembles ◽  
Long Term Trends

<p>Internal climate variability plays an important role in the abundance and distribution of phytoplankton in the global ocean. Previous studies using large ensembles of Earth system models (ESMs) have demonstrated their utility in the study of marine phytoplankton variability. These ESM large ensembles simulate the evolution of multiple alternate realities, each with a different phasing of internal climate variability. However, ESMs may not accurately represent real world variability as recorded via satellite and in situ observations of ocean chlorophyll over the past few decades. Observational records of surface ocean chlorophyll equate to a single ensemble member in the large ensemble framework, and this can cloud the interpretation of long-term trends: are they externally forced, caused by the phasing of internal variability, or both? Here, we use a novel statistical emulation technique to place the observational record of surface ocean chlorophyll into the large ensemble framework. Much like a large initial condition ensemble generated with an ESM, the resulting synthetic ensemble represents multiple possible evolutions of ocean chlorophyll concentration, each with a different phasing of internal climate variability. We further demonstrate the validity of our statistical approach by recreating a ESM ensemble of chlorophyll using only a single ESM ensemble member. We use the synthetic ensemble to explore the interpretation of long-term trends in the presence of internal variability. Our results suggest the potential to explore this approach for other ocean biogeochemical variables.</p>

scholarly journals Estimation of the Particulate Organic Carbon to Chlorophyll-a Ratio Using MODIS-Aqua in the East/Japan Sea, South Korea

2020 ◽  
Vol 12 (5) ◽  
pp. 840 ◽  
Author(s):  
Dabin Lee ◽  
SeungHyun Son ◽  
HuiTae Joo ◽  
Kwanwoo Kim ◽  
Myung Joon Kim ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Particulate Organic Carbon ◽  
Chlorophyll A ◽  
Primary Productivity ◽  
Seasonal Variability ◽  
Japan Sea ◽  
Global Ocean ◽  
Phytoplankton Communities

In recent years, the change of marine environment due to climate change and declining primary productivity have been big concerns in the East/Japan Sea, Korea. However, the main causes for the recent changes are still not revealed clearly. The particulate organic carbon (POC) to chlorophyll-a (chl-a) ratio (POC:chl-a) could be a useful indicator for ecological and physiological conditions of phytoplankton communities and thus help us to understand the recent reduction of primary productivity in the East/Japan Sea. To derive the POC in the East/Japan Sea from a satellite dataset, the new regional POC algorithm was empirically derived with in-situ measured POC concentrations. A strong positive linear relationship (R2 = 0.6579) was observed between the estimated and in-situ measured POC concentrations. Our new POC algorithm proved a better performance in the East/Japan Sea compared to the previous one for the global ocean. Based on the new algorithm, long-term POC:chl-a ratios were obtained in the entire East/Japan Sea from 2003 to 2018. The POC:chl-a showed a strong seasonal variability in the East/Japan Sea. The spring and fall blooms of phytoplankton mainly driven by the growth of large diatoms seem to be a major factor for the seasonal variability in the POC:chl-a. Our new regional POC algorithm modified for the East/Japan Sea could potentially contribute to long-term monitoring for the climate-associated ecosystem changes in the East/Japan Sea. Although the new regional POC algorithm shows a good correspondence with in-situ observed POC concentrations, the algorithm should be further improved with continuous field surveys.

scholarly journals Simulating Southern Hemisphere extra-tropical climate variability with an idealised coupled atmosphere-ocean model

2012 ◽  
Vol 5 (5) ◽  
pp. 1161-1175 ◽  
Author(s):  
H. Kurzke ◽  
M. V. Kurgansky ◽  
K. Dethloff ◽  
D. Handorf ◽  
S. Erxleben ◽  
...  
Keyword(s):  
Climate Variability ◽  
Southern Hemisphere ◽  
Ocean Circulation ◽  
Coupled Model ◽  
Circulation Model ◽  
Horizontal Resolution ◽  
Global Ocean ◽  
Simplified Model ◽  
Cmip5 Models ◽  
Decadal Climate Variability

Abstract. A quasi-geostrophic model of Southern Hemisphere's wintertime atmospheric circulation with horizontal resolution T21 has been coupled to a global ocean circulation model with a resolution of 2° × 2° and simplified physics. This simplified coupled model reproduces qualitatively some features of the first and the second EOF of atmospheric 833 hPa geopotential height in accordance with NCEP data. The variability patterns of the simplified coupled model have been compared with variability patterns simulated by four complex state-of-the-art coupled CMIP5 models. The first EOF of the simplified model is too zonal and does not reproduce the right position of the centre of action over the Pacific Ocean and its extension to the tropics. The agreement in the second EOF between the simplified and the CMIP5 models is better. The total variance of the simplified model is weaker than the observational variance and those of the CMIP5 models. The transport properties of the Southern Ocean circulation are in qualitative accord with observations. The simplified model exhibits skill in reproducing essential features of decadal and multi-decadal climate variability in the extratropical Southern Hemisphere. Notably, 800 yr long coupled model simulations reveal sea surface temperature fluctuations on the timescale of several decades in the Antarctic Circumpolar Current region.

scholarly journals Effects of the 18.6-yr Modulation of Tidal Mixing on the North Pacific Bidecadal Climate Variability in a Coupled Climate Model

2012 ◽  
Vol 25 (21) ◽  
pp. 7625-7642 ◽  
Author(s):  
Yuki Tanaka ◽  
Ichiro Yasuda ◽  
Hiroyasu Hasumi ◽  
Hiroaki Tatebe ◽  
Satoshi Osafune
Keyword(s):  
Climate Variability ◽  
North Pacific ◽  
Climate Model ◽  
Tidal Mixing ◽  
Global Ocean ◽  
Coupled Climate Model ◽  
Orbital Inclination ◽  
The North ◽  
Sst Anomaly ◽  
The North Pacific

Diapycnal mixing induced by tide–topography interaction, one of the essential factors maintaining the global ocean circulation and hence the global climate, is modulated by the 18.6-yr period oscillation of the lunar orbital inclination, and has therefore been hypothesized to influence bidecadal climate variability. In this study, the spatial distribution of diapycnal diffusivity together with its 18.6-yr oscillation estimated from a global tide model is incorporated into a state-of-the-art numerical coupled climate model to investigate its effects on climate variability over the North Pacific and to understand the underlying physical mechanism. It is shown that a significant sea surface temperature (SST) anomaly with a period of 18.6 years appears in the Kuroshio–Oyashio Extension region; a positive (negative) SST anomaly tends to occur during strong (weak) tidal mixing. This is first induced by anomalous horizontal circulation localized around the Kuril Straits, where enhanced modulation of tidal mixing exists, and then amplified through a positive feedback due to midlatitude air–sea interactions. The resulting SST and sea level pressure variability patterns are reminiscent of those associated with one of the most prominent modes of climate variability in the North Pacific known as the Pacific decadal oscillation, suggesting the potential for improving climate predictability by taking into account the 18.6-yr modulation of tidal mixing.

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