Introduction to Climate Variability

While anthropogenic signals in globally-averaged Sea Surface Temperature (SST) usually emerge quite early in the 20th century, regional SST changes are more difficult to detect due to large aliasing by internal climate variability.

The modes of climate variability affect the strength of the Leeuwin Current, a warm, poleward-flowing eastern boundary current, and air-sea heat exchanges off the coast, impacting on the upper ocean heat balance and sea surface temperature (SST) variability.

On the other hand, climate variability, land cover, and land use changes have altered the hydrologic response of the Kaptagat catchment, one of the major sources of water for Eldoret.

The Evapotranspiration was more sensitive to land-use change than climate variability in all sub-basins, and increased by 209% with vegetation restoration in the Yanhe watershed.

Climate variability at high latitudes on a multi-decadal time scale is dominated by large interannual variability superimposed on an underlying moderate warming trend.

Recent observational studies suggest that extratropical-tropical coupling between the KE and the central tropical Pacific El Niño Southern Oscillation (CP-ENSO) leads to the observed preferred decadal time-scale of Pacific climate variability.

The emergent constraint approach is a way of using multi-model ensembles to identify the linkage between current/past climate variability and future climate changes, which has been widely used for narrowing down the uncertainty of multi-model projections of future climate change.

ABSTRACT To understand Earth´s climate variability and improve predictions of future climate change, studying past climates is an important avenue to explore.

&#160;</p><p>We assess the capability of the isotope-enabled models HadCM3 and ECHAM5-MPI/OM to simulate decadal to centennial climate variability beyond the instrumental period.

&#160; This complexity has so far inhibited basic understanding of the drivers of western coastal climate, climate variability, and climate change.

During the last glaciation climate variability process, the cold signals of the YD and Heinrich events were recorded well in our core SST series, revealing a regional response to global climate changes.

Our results show that water quality of all water systems in the Netherlands is affected by climate variability in several ways: 1) through the process of global climate change (mainly temperature and sea level rise), 2) through changes Atlantic ocean circulation patterns (more southwestern winds), 3) through changes in continental precipitation and river discharge fluctuations, and 4) through local climatic fluctuations.

eu/), developed within the H2020 CLARITY project, is an innovative hazard/impact modeling tool that takes into account short- to long-term climate change scenarios and urban microclimate variability.

Estimates of agricultural Total Factor Productivity (TFP) can be highly sensitive to both short-run climate variability and long-term climate change.

Tree-ring reconstructions of climate variables provide valuable indirect evidence of climate variability and elucidate the relationship between large-scale circulation anomalies and the climate in the region.

To address this issue, we construct an ESD model using multiple-linear-regression techniques for the period 1982–2016 that is based on large-scale circulation indices representing tropical Pacific Ocean, Atlantic Ocean, and South American climate variability, to estimate austral summer [December–February (DJF)] precipitation over SA.

However, increased climate variability, driven by anthropogenic climate change, will likely enhance synchrony among spatially disparate populations.

However, climate variability and anthropogenic climate change may seriously impact small-scale fisheries by altering the abundance and distribution of target species.

M3, SM2 and SM3 are the most exposed AEZs but having a relatively better adaptive capacity whereas M1, M2 and M3 are the most sensitive AEZs with relatively low adaptive capacity to climate variability and change.

Ethiopia is vulnerable to climate variability and extremes due to its location and low adaptive capacity.

Finally, we evaluated the long-term evolution and inter-annual variability of the precipitation and the main large-scale atmospheric circulation patterns that govern climate variability in Iberia (NAO and EA modes) for the period 1851-1985.

Fingerprint-based formal detection and attribution approach is utilized Observed temperature changes are not due to natural internal climate variability Climate change impact is prominent in deep-tropics Change in temperature is significant over whole of Sri Lanka Large-scale atmospheric circulation patterns have a strong influence on hydroclimatology of Sri Lanka Fingerprint-based formal detection and attribution approach is utilized Observed temperature changes are not due to natural internal climate variability Climate change impact is prominent in deep-tropics Change in temperature is significant over whole of Sri Lanka Large-scale atmospheric circulation patterns have a strong influence on hydroclimatology of Sri Lanka.

Analysis of long-term rainfall trends provides a wealth of information on effective crop planning and water resource management, and a better understanding of climate variability over time.

The themes covered by the cooperation include natural resource management, marine and coastal areas, water resource management, climate variability and change, disaster risk reduction and food security.

This study examines the short-term impacts of land use land cover (LULC) changes as well as climate variability on the water quality of the Cahaba River watershed in Alabama.

Structured questionnaires, focused group discussions and interviews were used to gather relevant data on resident’s perception on the impacts of land use, land cover changes on climate variability.

Development in Sub-Saharan Africa (SSA) is adversely affected by climate variability and change due to the dependence of its economies and livelihoods primarily on rain-fed agriculture.

Its population is highly dependent on rain-fed agriculture, which makes them extremely sensitive to climate variability and extremes for their subsistence.

LVI analysis uses seven components of vulnerability which include climate variability, food, water, health, socio-demographic profile, livelihood strategies, and social networks.

Twenty-nine sub-components belonging to 10 major components (socio-demographic profile, livelihood strategies, social networks, revenue, health, food, water, housing, land, and natural hazards and climate variability) are conducted to calculate LVI, LVI-IPCC, and LEI.

Our findings advance a new understanding of natural climate variability in this region, with substantial implications for climate change adaptation of the region’s critical infrastructure.

The research identified challenges to climate change adaptation which include lack of markets to sell farming produce, inefficient institutions, poverty and high climate variability and increased uncertainty in the behavior of seasons.

Nevertheless, the findings from this study are relevant for planning of predictive adaptation to the effects of climate variability on the water resources management applications.

Present planning for sustainable water resources management should consider climate variability changes to minimize the risks of future water-related problems and hazards.

In this study, we analyze the climate variability of the past two centuries in the West Antarctic sector by reconstructing the key atmospheric variables (atmospheric circulation, near-surface air temperature and snow accumulation) as well as the sea-ice extent at the annual timescale using a data assimilation approach.

Our study focuses on a multi-model comparison of the CMIP6 simulations used to investigate the climate variability in this area to answer the next questions: (1) are the biases in HMA reduced in the new generation of climate models? (2) Do the model biases impact the simulated climate trends? (3) What are the links between the model biases in temperature, precipitation, and snow cover extent? (4) Which climate trajectories can be projected in this area until 2100? An analysis of 27 models over 1979-2014 still show a cold bias in near-surface air temperature over the HMA and TP reaching an annual value of -2.

Using historical data compiled by the Climate Research Unit, spatial and temporal analysis, trend analysis, empirical orthogonal function (EOF) analysis, and Taylor diagram analysis were applied to test the ability of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models to accurately simulate the annual mean surface air temperature in central Asia from the perspective of the average climate state and climate variability.

<p>Long observational records of land surface air temperature are vital to our understanding of climate variability and change, as well as for testing predictions of climatic trends.

While spatial correlations between the N-TREND NH temperature reconstruction and the model simulations are weak in terms of the ensemble mean model results, individual model simulations show good correlation over North America and Europe, suggesting the spatial heterogeneity of the 1810 cooling could be due to internal climate variability.

While spatial correlations between the N-TREND NH temperature reconstruction and the model simulations are weak in terms of the ensemble-mean model results, individual model simulations show good correlation over North America and Europe, suggesting the spatial heterogeneity of the 1810 cooling could be due to internal climate variability.

However, the impacts of climate variability on groundwater system dynamics in the VMD remain poorly understood.

The El Niño Southern Oscillation (ENSO) is a principal component of global climate variability known to influence a host of social and economic outcomes, but its systematic effects on human health remain poorly understood.

ABSTRACT Based on two single-model initial-condition 50-member ensembles of climate simulations conducted with two generations of Canadian Earth System Models (CanESM2 and its successor CanESM5), we analyze the ensemble mean and spread of the projected trends of wintertime North American surface air temperature (SAT) and extreme indices of cold (TX10) and warm (TX90) days over the next half-century (2021–2070) and explore the contribution of internal climate variability to these trends.

To better understand the role of internal climate variability (ICV) in climate change impact studies, this study quantifies the importance of ICV [defined as the intermember variability of a single model initial-condition large ensemble (SMILE)] in relation to the anthropogenic climate change (ACC; defined as multimodel ensemble mean) in global and regional climate change using a criterion of time of emergence (ToE).

These results support the notion that internal climate variability and aerosol-induced mid-to-late-20th century major hurricane frequency reductions16-24 have probably masked century-scale greenhouse-gas warming contributions to North Atlantic major hurricane frequency.

, Atlantic multidecadal) climate variability and aerosol-induced mid-to-late-20th century major hurricane frequency reductions have probably masked century-scale greenhouse-gas warming contributions to North Atlantic major hurricane frequency.

An effective approach is to focus on the management of current climate variability as it relates to water, which strengthens the ability of communities and countries to foresee, manage, and adapt to the impacts of longer-term climate change on water-related activities.

Our results solidify the link between northern high latitude climate variability, Antarctic temperature and Southern Ocean surface temperature variability during MIS-3.

An important limitation of our CMIP5 analysis is that internal climate variability is large on the short time scales considered.

In this study, the total uncertainty (T) of climate projections is decomposed into the greenhouse gas emission scenario uncertainty (S, mean inter-scenario variance of the signals over all the models), GCM uncertainty (M, mean inter-model variance of signals over all emission scenarios), and internal climate variability uncertainty (V, variance in noises over all models, emission scenarios, and projection lead times); namely, T = S + M + V.

Climate change, defined by the United Nations Framework Convention on Climate Change as “a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods” [3] may create environmental pressures that result in new diseases caused by fungi [4].

Lawrence are documented in this paper with the aim of determining and understanding the natural climate variability and the impact of anthropogenic forcing at a regional scale.

The most sensitive trees were the individuals located in the highly seasonal vegetation of the epikarst, in contrast to the complacent trees (non-sensitive to regular interannual climate variability) inhabiting the less-seasonal vegetation in the deep soil epikarst and valley.

The El Nino-Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts.

The timing of ocean warming and cooling in our record indicates that millennial scale climate variability plays a crucial role in setting mean ocean temperature during this interval, as seen during other periods, such as the last deglaciation.

More specific sensitivity experiments performed under MIS 3 boundary conditions are needed in order to (1) better understand the mechanisms behind millennial-scale climate variability, (2) explore AMOC variability under intermediate glacial conditions, and (3) help answer the question: &#8220;are models too stable?&#8221;.

This region is presently subject to severe droughts, desertification, and land degradation, and likely facing increased climate variability and socioeconomic stress in the future.

However, increased climate variability, driven by anthropogenic climate change, will likely enhance synchrony among spatially disparate populations.

The thermal state of the South China Sea (SCS) modulates the regional climate variability over Southeast Asia.

Ocean-atmospheric phenomena (OAP) have been found to be associated with regional climate variability and, in turn, agricultural production.

In the light of increasing climate variability and change, we investigated weather and rapid weather changes as possible short-term risk factors for SAH.

Underground water is becoming depleted in many sectors, making now and future generations near the point of being deprived of protection from the increasing climate variability.

The frequent fluctuations in global climate variability (GCV), decreases in farmland and irrigation water, soil degradation and erosion, and increasing fertilizer costs are the significant factors in declining rice productivity, mainly in Asia and Africa.

El Niño Southern Oscillation (ENSO) is the principal predictor of global climate variability at interannual timescales.

In this paper we present results for the whole of the European power sector regarding on how considering current and future climate variability affects the results of a TIMES energy system model for the whole European power sector (eTIMES-EU) up to 2050.

However, precipitation changes in maximum decline and increase are more noticeable for CMIP6, addressing the better resolving for future climate variability.

The emergent constraint approach is a way of using multi-model ensembles to identify the linkage between current/past climate variability and future climate changes, which has been widely used for narrowing down the uncertainty of multi-model projections of future climate change.

During these four cold and warm periods, the δ18O and δ13C records of all four speleothems and the Mg concentration of the speleothems Bu4 (Bunker Cave) and TV1 (Herbstlabyrinth cave system) show common features and are thus interpreted to be related to past climate variability.

Quantitative paleoclimate reconstructions based on biological fossils over glacial-interglacial timescales are a major source of information on long-term climate variability.

Long term climate variability in the Southern hemisphere showed the effects of a Southern Annular Mode (SAM) in positive mode (+1.

This study presents annual and intra-annual climate variability and trend analysis carried out across the Volta Delta, using the coefficient of variation (CV), anomaly, Mann-Kendall and Pettit statistics.

Conclusions: Different oak species have analogous nutrient conservation strategies in response to annual climate variability, and interannual climate variations strongly impacted plant nutrient resorption.

We find that extending the production pipeline of short-range forecast timescales beyond the medium-range, such that the medium-range forecast timescales can be fed into existing tools for applying short-range forecasts, assisted in mitigating the risks of sub-seasonal climate variability on socio-economic activities in Nigeria.

It is in this context that the study investigates water flows abstraction from headwaters to floodplains and how the water resources are been impacted by seasonal climate variability.

CRC Press, Taylor & Francis Group, 2016; (3) Natural Decadal Climate Variability: Societal Impacts.

The interdecadal Pacific oscillation (IPO) and Atlantic multidecadal oscillation (AMO), two leading modes of decadal climate variability, are not independent.

Better-targeted food reserves and other adaptation measures could help fill the consumption gap in the face of extreme climate variability.

Change in climatic scenarios due to global warming is characterized by extreme climate variability, land and water degradation which resulted in water scarcity.

Sudanian zone is one of the regions in West Africa most confronted with high climate variability, challenging agricultural sustainability and rural livelihoods.

It’s likely that forming greater connectedness and more complex culture was necessary in order to survive the periods of high climate variability that were a feature of the last ice age.

Paleoclimate studies in Patagonia show a high Holocene climate variability, strongly controlled by the intensity and latitudinal position of the Southern Westerly Winds.

The investigation of formation, stabilization and age of the respective landforms permitted an assessment of Holocene climate variability in Rondane and its connectivity to landform evolution.

Long-term, large-scale perspectives are necessary for understanding climate variability and its effects on ecosystems and cultures.

There has been an enormous amount of work on understanding climate variability over the last decades.

The influence from extra-tropical climate variability, such as Atlantic multidecadal oscillation, tends to modify the rainfall dipole pattern to a monopole pattern from the Gulf of Guinea to Sahara through influencing the Sahara heat low.

The non-assimilation experiments exhibit a moderate historical global surface temperature evolution and tropical climate variability characteristics that compare favourably with observations.

Using an assemblage of four ice cores collected around the Pacific basin, one of the first basinwide histories of Pacific climate variability has been created.

Finally, the correlation coefficients between the two droughts and Indo-Pacific climate variability including El Niño-Southern Oscillation and Indian Ocean Dipole (IOD) are computed.

This circulation is linked to the tropical Atlantic Climate variability and the coupled pattern of wind and ocean dynamics.

Causality techniques showed that the MJO impacts the North Atlantic Oscillation (NAO) — a regional mode of North Atlantic climate variability — via two pathways.

Since few other records of comparable length are available, the Halley record has been used extensively in studies of long-term Antarctic climate variability and change.

Changes in the deep-water oxygen concentrations mostly resemble the deglacial dynamics of the Southern Ocean upwelling intensity which is supposed to be closely coupled with the Antarctic climate variability.

BACKGROUND AND AIM: Ongoing climate variability and change is altering the timing of spring onset, a critical event that signifies the start of tree pollen season in North America.

Ongoing climate variability and change is impacting pollen exposure dynamics among sensitive populations.

The persistence of all stands despite climate variability indicates high resilience of this species.

Our results show that from 563 AD (beginning of the record) to the twentieth century, the cladoceran assemblage remained stable, despite climate variability of 3 °C in air temperature.

However, little is known about their long-term response to multi-millennial climate variability, in particular in the Southern Hemisphere.

However, their long-term response to multi-millennial climate variability has rarely been studied, in particular in the Southern Hemisphere.