Introduction to Large Scale Climate

Most studies have focused on large-scale climate migration events triggered by extreme weather events such as droughts, storms, or floods.

The performance of our method is demonstrated on benchmark examples and used for the analysis of a large-scale climate modeling application which involves the WRF model.

Our results show that the polynya witnessed step-wise environmental shifts in parallel with the phases and strength of large-scale climate patterns, i.

Cross-wavelet analysis (CWA) is performed to identify the substantial effect of large-scale climate anomalies on the derived drought indices.

The proposed model shows strong potential for predicting the extreme wave heights by linking large-scale climate patterns, providing motivation for developing dynamic coastal flood risk management strategies.

Shifts in the large-scale climate drivers such as El Niño-Southern Oscillation, Pacific Decadal Oscillation and Interdecadal Pacific Oscillation were observed to influence on changes to the low frequency of variability of the headland bypassing in the study area by affecting the predominant wave direction and updrift beach sand availability.

In addition to global-warming caused fire increase, large-scale climate oscillations have significantly contributed to fire variability.

In this study, a circular statistical method was used for spatio-temporal assessment of seasonality of daily and monthly precipitation extremes and their teleconnections with large-scale climate patterns over the contiguous United States.

We use large-scale climate gradients to predict current species extinction risk across global regions, which also offers an important tool for predicting future extinction risk due to ongoing space loss and climate change.

Here we explore the more detailed evolution of the 2019 conditions over subtropical eastern Australia and infer its predictability through linear relationships with large-scale climate drivers.

Biotic responses to large-scale climate processes are scale-dependant and can influence population trajectories of highly migratory species such as short-tailed shearwaters Ardenna tenuirostris.

Large-scale climate oscillations, particularly the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO), have widespread influences on climate systems across the Tibetan Plateau (TP).

Models that explicitly represent competition between plants with alternative leaf trait plasticity in response to elevated CO$_2$ are needed to capture these influences on tropical forest functioning and large-scale climate.

Substantial progress has been made in identifying large-scale climate effect on somatic growth through the use of ageing-based methods in aquatic environments, yet their annual/seasonal temporal resolution seems too coarse for such a fast process.

In this study, we investigate the dominant modes of surface air temperature variations of the cold season (from November through to the next March) and the warm season (from May to September) over Central Asia, and their associations with large-scale climate patterns for the period of 1979–2016.

Our main focus will be how the atmospheric circulation may influence the polar sea ice and large-scale climate patterns when imposing combinations of solar and volcanic forcing.

Compared with floods, the relationship between droughts and locust plague was closer, and large-scale climate change may also affect locust outbreaks.

Spatial variations exist among colonies for some species, indicating that local conditions may affect the trajectories of specific populations and potentially counter-balance the consequences of large-scale climate warming.

In order to facilitate more equal and less random weighting among downscaled GCM results, we present a method to emulate the missing combinations in such a matrix, enabling equal weighting among participating GCMs and hence among regional consequences of large-scale climate change simulated by each GCM.

ABSTRACT This study investigates the indirect effect of large-scale climate oscillations and the corresponding teleconnection with lake water level (WL) oscillations.

A total of 22 IFCSs were tested for their responses to three types of pressures including anthropogenic activities (fishing), large-scale climate change, and regional environmental variables.

Furthermore, cross-wavelet analysis was employed to further analyze the relationship between meteorological drought and agricultural drought, explore the teleconnection and large-scale climate patterns and investigate possible causes of drought variations in this region.

Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate-Earth surface interactions and feedbacks.

The response of sedimentary systems to the large-scale climate changes of the PETM are, however, still uncertain.

One type of model utilizes temperature and precipitation for individual cropping areas (the TP model type) to represent the current service, whereas the other type relies on large-scale climate indices (the CI model).

This study investigates the influence of El Niño-Southern Oscillation (ENSO), an important large-scale climate phenomenon, on surface dust levels in different regions.

Overall, the d18OTR records for northwestern Canada reflect the same significant large-scale climate patterns as precipitation δ18O for spring-summer, and therefore have potential for reconstructions past atmospheric dynamics in addition to temperature variability.

We present a hurricane risk assessment model that simulates North Atlantic Ocean tropical cyclone (TC) tracks and intensity, conditioned on the early season large-scale climate state.

Studies analysed suggest that variables reflecting the physical and biological environment in situ, features reflecting the geomorphic marine landscape, and large-scale climate indices all significantly affect euphausiid biomass and distribution.

In this study, we investigate the dominant modes of surface air temperature variations of the cold season (from November through to the next March) and the warm season (from May to September) over Central Asia, and their associations with large-scale climate patterns for the period of 1979–2016.

The Southern Ocean represents a fascinating system for assessing large-scale climate-driven biological change, as it contains extremely isolated island groups within a predominantly westerly, circumpolar wind and current system.

Analysis shows that this 5–7 year oscillation is linked to a sequence of anomalous large-scale climate patterns with an eastward propagation in both the ocean and atmosphere.

Practice Relevance Large-scale climate projections for the coming decades show robust trends in rising air temperatures, including more warm days and nights, and longer/more intense warm spells and heatwaves.

The outcomes of our study provide critical knowledge of river thermal condition and heat transport to the northern seas, which will be useful for large-scale climate and ocean model development and validation, and climate/hydrology change investigations in the broader northern regions.

Common large-scale climate variability was confirmed by significant relationship between oxygen isotope data in tree-ring cellulose from the Taimyr Peninsula and northeastern Yakutia, and oxygen isotope ice core data from Severnaya Zemlja during the Medieval Warm period and the recent one.

flood forecasting, large-scale climate impact analyses, and estimation of time-dynamic water fluxes into sea basins.

By comparing different large-scale climate parameters for the 2 months, we found that during El Niño (La Niña) phases, a negative (positive) anomaly of geopotential height and a positive (negative) anomaly of zonal wind and specific humidity are observed over the region in December, while the opposite situation occurs in January.

Here, we analyze teleconnections between the regimes and their descriptors, and large-scale climate variability.

The strong role of early growing season anomalies in vegetative activity within the growing season emphasizes the importance of a grid-wise definition of the growing season when studying the large-scale climate-vegetation coupling in Europe.

An illustrative example, drawn from a large-scale climate adaptation research program, provides evidence on how this method might advance our methodological strategies for studying learning in these programs.

Our results show that the temporal variation of submissions over the years is driven by fluctuating topicality of mosquito-borne diseases in the media and large-scale climate conditions.

These results indicate that the feedback due to the mesoscale wind stress-SST coupling to the ocean has the potential for reducing the warm SST bias often seen in the large-scale climate model simulations in this region.

Flooding in the Amazon basin is frequently attributed to modes of large-scale climate variability, but little attention is paid to how these modes influence the timing and duration of floods despite their importance to early warning systems and the significant impacts that these flood characteristics can have on communities.

This study demonstrates that the variability of LVS lakes is strongly related to the South American Monsoon System dynamics and large-scale climate forcings from the Pacific and Atlantic Oceans.

This low-frequency variability, generated by large-scale climate variability and modulated by the physical properties of hydrosystems, is clearly imprinted in aquifers of northern France.

However, it is still difficult to establish accurate and reliable forecasts only large-scale climate indexes used.

This occurred when some large-scale climate influences, including the sea surface temperatures suggested conditions would not lead to major flooding.

All it shows right now is what was done in this paper, followed by a generic sentence “although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate-Earth surface interactions and feedbacks”, which tells nothing.

This study focuses on modelling the changes in rainfall patterns in different agro-climatic zones due to climate change through statistical downscaling of large-scale climate variables using machine learning approaches.

These trends appear to be consistent with the changes observed in the large-scale climate modes (i.

In this study we combine new, high resolution geochemical and sedimentological records from the Jurassic boreal seaway, where widespread deposition of the Jurassic Kimmeridge Clay Formation (KFC) is well documented with large scale climate and biogeochemical modelling, placing the seaway into a global climatic context.