Modeling impacts of CO2, ozone, and climate change on tree growth

Treesearch

George E. Host; Gary W. Theseira; J. G. Isebrands

1996-01-01

Understanding the influence of ozone, CO2, and changing climatic regimes on basic plant physiological processes is essential for predicting the response of forest ecosystems. To understand the relationships among these interacting factors, in the face of genetic and other environmental variability, requires a means of synthesis. Physiological...

Climate variability and demand growth as drivers of water scarcity in the Turkwel river basin: a bottom-up risk assessment of a data-sparse basin in Kenya

NASA Astrophysics Data System (ADS)

Hirpa, F. A.; Dyer, E.; Hope, R.; Dadson, S. J.

2017-12-01

Sustainable water management and allocation are essential for maintaining human well-being, sustaining healthy ecosystems, and supporting steady economic growth. The Turkwel river basin, located in north-western Kenya, experiences a high level of water scarcity due to its arid climate, high rainfall variability, and rapidly growing water demand. However, due to sparse hydro-climatic data and limited literature, the water resources system of the basin has been poorly understood. Here we apply a bottom-up climate risk assessment method to estimate the resilience of the basin's water resources system to growing demand and climate stressors. First, using a water resource system model and historical climate data, we construct a climate risk map that depicts the way in which the system responds to climate change and variability. Then we develop a set of water demand scenarios to identify the conditions that potentially lead to the risk of unmet water demand and groundwater depletion. Finally, we investigate the impact of climate change and variability by stress testing these development scenarios against historically strong El Niño/Southern Oscillation (ENSO) years and future climate projections from multiple Global Circulation Models (GCMs). The results reveal that climate variability and increased water demand are the main drivers of water scarcity in the basin. Our findings show that increases in water demand due to expanded irrigation and population growth exert the strongest influence on the ability of the system to meet water resource supply requirements, and in all cases considered increase the impacts of droughts caused by future climate variability. Our analysis illustrates the importance of combining analysis of future climate risks with other development decisions that affect water resources planning. Policy and investment decisions which maximise water use efficiency in the present day are likely to impart resilience to climate change and variability under a wide range of future scenarios and therefore constitute low regret measures for climate adaptation.

Relationship of suicide rates with climate and economic variables in Europe during 2000-2012.

PubMed

Fountoulakis, Konstantinos N; Chatzikosta, Isaia; Pastiadis, Konstantinos; Zanis, Prodromos; Kawohl, Wolfram; Kerkhof, Ad J F M; Navickas, Alvydas; Höschl, Cyril; Lecic-Tosevski, Dusica; Sorel, Eliot; Rancans, Elmars; Palova, Eva; Juckel, Georg; Isacsson, Goran; Jagodic, Helena Korosec; Botezat-Antonescu, Ileana; Rybakowski, Janusz; Azorin, Jean Michel; Cookson, John; Waddington, John; Pregelj, Peter; Demyttenaere, Koen; Hranov, Luchezar G; Stevovic, Lidija Injac; Pezawas, Lucas; Adida, Marc; Figuera, Maria Luisa; Jakovljević, Miro; Vichi, Monica; Perugi, Giulio; Andreassen, Ole A; Vukovic, Olivera; Mavrogiorgou, Paraskevi; Varnik, Peeter; Dome, Peter; Winkler, Petr; Salokangas, Raimo K R; From, Tiina; Danileviciute, Vita; Gonda, Xenia; Rihmer, Zoltan; Forsman, Jonas; Grady, Anne; Hyphantis, Thomas; Dieset, Ingrid; Soendergaard, Susan; Pompili, Maurizio; Bech, Per

2016-01-01

It is well known that suicidal rates vary considerably among European countries and the reasons for this are unknown, although several theories have been proposed. The effect of economic variables has been extensively studied but not that of climate. Data from 29 European countries covering the years 2000-2012 and concerning male and female standardized suicidal rates (according to WHO), economic variables (according World Bank) and climate variables were gathered. The statistical analysis included cluster and principal component analysis and categorical regression. The derived models explained 62.4 % of the variability of male suicidal rates. Economic variables alone explained 26.9 % and climate variables 37.6 %. For females, the respective figures were 41.7, 11.5 and 28.1 %. Male suicides correlated with high unemployment rate in the frame of high growth rate and high inflation and low GDP per capita, while female suicides correlated negatively with inflation. Both male and female suicides correlated with low temperature. The current study reports that the climatic effect (cold climate) is stronger than the economic one, but both are present. It seems that in Europe suicidality follows the climate/temperature cline which interestingly is not from south to north but from south to north-east. This raises concerns that climate change could lead to an increase in suicide rates. The current study is essentially the first successful attempt to explain the differences across countries in Europe; however, it is an observational analysis based on aggregate data and thus there is a lack of control for confounders.

Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma E.; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart J. J. M.

2018-06-01

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—`noise', intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM-GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in high-resolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability.

Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma E.; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart J. J. M.

2017-09-01

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—`noise', intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM-GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in high-resolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability.

Workshop on Bridging Satellite Climate Data Gaps.

PubMed

Cooksey, Catherine; Datla, Raju

2011-01-01

Detecting the small signals of climate change for the most essential climate variables requires that satellite sensors make highly accurate and consistent measurements. Data gaps in the time series (such as gaps resulting from launch delay or failure) and inconsistencies in radiometric scales between satellites undermine the credibility of fundamental climate data records, and can lead to erroneous analysis in climate change detection. To address these issues, leading experts in Earth observations from National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Adminstration (NOAA), United States Geological Survey (USGS), and academia assembled at the National Institute of Standards and Technology on December 10, 2009 for a workshop to prioritize strategies for bridging and mitigating data gaps in the climate record. This paper summarizes the priorities for ensuring data continuity of variables relevant to climate change in the areas of atmosphere, land, and ocean measurements and the recommendations made at the workshop for overcoming planned and unplanned gaps in the climate record.

An approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge

USDA-ARS?s Scientific Manuscript database

Land surface albedo has been recognized by the Global Terrestrial Observing System (GTOS) as an essential climate variable crucial for accurate modeling and monitoring of the Earth’s radiative budget. While global climate studies can leverage albedo datasets from MODIS, VIIRS, and other coarse-reso...

Uncovering the Anthropogenic Sea Level Change using an Improved Sea Level Reconstruction for the Indian Ocean

NASA Astrophysics Data System (ADS)

Kumar, P.; Hamlington, B.; Thompson, P. R.; Han, W.

2016-12-01

Despite having some of the world's most densely populated and vulnerable coastal regions, sea level (SL) variability in the Indian Ocean (IO) has received considerably less attention than the Pacific Ocean. Differentiating the internal variability from the long-term trend in global mean sea level (GMSL) at decadal time-scales is vital for planning and mitigation efforts in the IO region. Understanding the dynamics of internal and anthropogenic SL change is essential for understanding the dynamic pathways that link the IO basin to terrestrial climates world-wide. With a sparse pre-satellite observational record of the IO, the Indo-Pacific internal climate variability is difficult to represent accurately. However, an improved representation of pre-satellite SL variability can be achieved by using a multivariate reconstruction technique. By using cyclostationary empirical orthogonal functions (CSEOFs) that can capture time-varying spatial patterns, gaps in the historical record when observations are sparse are filled using spatial relationships from time periods when the observational network is dense. This reconstruction method combines SL data and sea surface temperature (SST) to create a SL reconstruction that spans a period from 1900 to present, long enough to study climate signals over interannual to decadal time scales. This study aims at estimating the component of SL rise that relates to anthropogenic forcing by identifying and removing the fraction related to internal variability. An improved understanding of how the internal climate variability can affect the IO SL trend and variability, will provide an insight into the future SL changes. It is also important to study links between SL and climate variability in the past to understand how SL will respond to similar climatic events in the future and if this response will be influenced by the changing climate.

Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts

NASA Astrophysics Data System (ADS)

Tommasi, Desiree; Stock, Charles A.; Hobday, Alistair J.; Methot, Rick; Kaplan, Isaac C.; Eveson, J. Paige; Holsman, Kirstin; Miller, Timothy J.; Gaichas, Sarah; Gehlen, Marion; Pershing, Andrew; Vecchi, Gabriel A.; Msadek, Rym; Delworth, Tom; Eakin, C. Mark; Haltuch, Melissa A.; Séférian, Roland; Spillman, Claire M.; Hartog, Jason R.; Siedlecki, Samantha; Samhouri, Jameal F.; Muhling, Barbara; Asch, Rebecca G.; Pinsky, Malin L.; Saba, Vincent S.; Kapnick, Sarah B.; Gaitan, Carlos F.; Rykaczewski, Ryan R.; Alexander, Michael A.; Xue, Yan; Pegion, Kathleen V.; Lynch, Patrick; Payne, Mark R.; Kristiansen, Trond; Lehodey, Patrick; Werner, Francisco E.

2017-03-01

Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.

Contrasting scaling properties of interglacial and glacial climates

PubMed Central

Shao, Zhi-Gang; Ditlevsen, Peter D.

2016-01-01

Understanding natural climate variability is essential for assessments of climate change. This is reflected in the scaling properties of climate records. The scaling exponents of the interglacial and the glacial climates are fundamentally different. The Holocene record is monofractal, with a scaling exponent H∼0.7. On the contrary, the glacial record is multifractal, with a significantly higher scaling exponent H∼1.2, indicating a longer persistence time and stronger nonlinearities in the glacial climate. The glacial climate is dominated by the strong multi-millennial Dansgaard–Oeschger (DO) events influencing the long-time correlation. However, by separately analysing the last glacial maximum lacking DO events, here we find the same scaling for that period as for the full glacial period. The unbroken scaling thus indicates that the DO events are part of the natural variability and not externally triggered. At glacial time scales, there is a scale break to a trivial scaling, contrasting the DO events from the similarly saw-tooth-shaped glacial cycles. PMID:26980084

Holocene climate and climate variability of the northern Gulf of Mexico and adjacent northern Gulf Coast: A review

USGS Publications Warehouse

Poore, Richard Z.

2008-01-01

Marine records from the northern Gulf of Mexico indicate that significant multidecadal- and century-scale variability was common during the Holocene. Mean annual sea-surface temperature (SST) during the last 1,400 years may have varied by 3°C, and excursions to cold SST coincide with reductions in solar output. Broad trends in Holocene terrestrial climate and environmental change along the eastern portion of the northern Gulf Coast are evident from existing pollen records, but the high-frequency details of climate variability are not well known. Continuous and well-dated records of climate change and climate variability in the western portion of the northern Gulf Coast are essentially lacking.Information on Holocene floods, droughts, and storm frequency along the northern Gulf Coast is limited. Records of floods may be preserved in continental shelf sediments, but establishing continuity and chronologies for sedimentary sequences on the shelf presents challenges due to sediment remobilization and redeposition during storms. Studies of past storm deposits in coastal lakes and marshes show promise for constructing records of past storm frequency. A recent summary of sea-level history of the northern Gulf Coast indicates sea level was higher than modern sea level several times during the last few thousand years.

Climate Drivers of Spatiotemporal Variability of Precipitation in the Source Region of Yangtze River

NASA Astrophysics Data System (ADS)

Du, Y.; Berndtsson, R.; An, D.; Yuan, F.

2017-12-01

Variability of precipitation regime has significant influence on the environment sustainability in the source region of Yangtze River, especially when the vegetation degradation and biodiversity reduction have already occurred. Understanding the linkage between variability of local precipitation and global teleconnection patterns is essential for water resources management. Based on physical reasoning, indices of the climate drivers can provide a practical way of predicting precipitation. Due to high seasonal variability of precipitation, climate drivers of the seasonal precipitation also varies. However, few reports have gone through the teleconnections between large scale patterns with seasonal precipitation in the source region of Yangtze River. The objectives of this study are therefore (1) assessment of temporal trend and spatial variability of precipitation in the source region of Yangtze River; (2) identification of climate indices with strong influence on seasonal precipitation anomalies; (3) prediction of seasonal precipitation based on revealed climate indices. Principal component analysis and Spearman rank correlation were used to detect significant relationships. A feed-forward artificial neural network(ANN) was developed to predict seasonal precipitation using significant correlated climate indices. Different influencing climate indices were revealed for precipitation in each season, with significant level and lag times. Significant influencing factors were selected to be the predictors for ANN model. With correlation coefficients between observed and simulated precipitation over 0.5, the results were eligible to predict the precipitation of spring, summer and winter using teleconnections, which can improve integrated water resources management in the source region of Yangtze River.

Estimating the impact of internal climate variability on ice sheet model simulations

NASA Astrophysics Data System (ADS)

Tsai, C. Y.; Forest, C. E.; Pollard, D.

2016-12-01

Rising sea level threatens human societies and coastal habitats and melting ice sheets are a major contributor to sea level rise (SLR). Thus, understanding uncertainty of both forcing and variability within the climate system is essential for assessing long-term risk of SLR given their impact on ice sheet evolution. The predictability of polar climate is limited by uncertainties from the given forcing, the climate model response to this forcing, and the internal variability from feedbacks within the fully coupled climate system. Among those sources of uncertainty, the impact of internal climate variability on ice sheet changes has not yet been robustly assessed. Here we investigate how internal variability affects ice sheet projections using climate fields from two Community Earth System Model (CESM) large-ensemble (LE) experiments to force a three-dimensional ice sheet model. Each ensemble member in an LE experiment undergoes the same external forcings but with unique initial conditions. We find that for both LEs, 2m air temperature variability over Greenland ice sheet (GrIS) can lead to significantly different ice sheet responses. Our results show that the internal variability from two fully coupled CESM LEs can cause about 25 35 mm differences of GrIS's contribution to SLR in 2100 compared to present day (about 20% of the total change), and 100m differences of SLR in 2300. Moreover, only using ensemble-mean climate fields as the forcing in ice sheet model can significantly underestimate the melt of GrIS. As the Arctic region becomes warmer, the role of internal variability is critical given the complex nonlinear interactions between surface temperature and ice sheet. Our results demonstrate that internal variability from coupled atmosphere-ocean general circulation model can affect ice sheet simulations and the resulting sea-level projections. This study highlights an urgent need to reassess associated uncertainties of projecting ice sheet loss over the next few centuries to obtain robust estimates of the contribution of ice sheet melt to SLR.

Observationally-based Metrics of Ocean Carbon and Biogeochemical Variables are Essential for Evaluating Earth System Model Projections

NASA Astrophysics Data System (ADS)

Russell, J. L.; Sarmiento, J. L.

2017-12-01

The Southern Ocean is central to the climate's response to increasing levels of atmospheric greenhouse gases as it ventilates a large fraction of the global ocean volume. Global coupled climate models and earth system models, however, vary widely in their simulations of the Southern Ocean and its role in, and response to, the ongoing anthropogenic forcing. Due to its complex water-mass structure and dynamics, Southern Ocean carbon and heat uptake depend on a combination of winds, eddies, mixing, buoyancy fluxes and topography. Understanding how the ocean carries heat and carbon into its interior and how the observed wind changes are affecting this uptake is essential to accurately projecting transient climate sensitivity. Observationally-based metrics are critical for discerning processes and mechanisms, and for validating and comparing climate models. As the community shifts toward Earth system models with explicit carbon simulations, more direct observations of important biogeochemical parameters, like those obtained from the biogeochemically-sensored floats that are part of the Southern Ocean Carbon and Climate Observations and Modeling project, are essential. One goal of future observing systems should be to create observationally-based benchmarks that will lead to reducing uncertainties in climate projections, and especially uncertainties related to oceanic heat and carbon uptake.

Assessing the role of internal climate variability in Antarctica's contribution to future sea-level rise

NASA Astrophysics Data System (ADS)

Tsai, C. Y.; Forest, C. E.; Pollard, D.

2017-12-01

The Antarctic ice sheet (AIS) has the potential to be a major contributor to future sea-level rise (SLR). Current projections of SLR due to AIS mass loss remain highly uncertain. Better understanding of how ice sheets respond to future climate forcing and variability is essential for assessing the long-term risk of SLR. However, the predictability of future climate is limited by uncertainties from emission scenarios, model structural differences, and the internal variability that is inherently generated within the fully coupled climate system. Among those uncertainties, the impact of internal variability on the AIS changes has not been explicitly assessed. In this study, we quantify the effect of internal variability on the AIS evolutions by using climate fields from two large-ensemble experiments using the Community Earth System Model to force a three-dimensional ice sheet model. We find that internal variability of climate fields, particularly atmospheric fields, among ensemble members leads to significantly different AIS responses. Our results show that the internal variability can cause about 80 mm differences of AIS contribution to SLR by 2100 compared to the ensemble-mean contribution of 380-450 mm. Moreover, using ensemble-mean climate fields as the forcing in the ice sheet model does not produce realistic simulations of the ice loss. Instead, it significantly delays the onset of retreat of the West Antarctic Ice Sheet for up to 20 years and significantly underestimates the AIS contribution to SLR by 0.07-0.11 m in 2100 and up to 0.34 m in the 2250's. Therefore, because the uncertainty caused by internal variability is irreducible, we seek to highlight a critical need to assess the role of internal variability in projecting the AIS loss over the next few centuries. By quantifying the impact of internal variability on AIS contribution to SLR, policy makers can obtain more robust estimates of SLR and implement suitable adaptation strategies.

North Atlantic sub-decadal variability in climate models

NASA Astrophysics Data System (ADS)

Reintges, Annika; Martin, Thomas; Latif, Mojib; Park, Wonsun

2017-04-01

The North Atlantic Oscillation (NAO) is the dominant variability mode for the winter climate of the North Atlantic sector. During a positive (negative) NAO phase, the sea level pressure (SLP) difference between the subtropical Azores high and the subpolar Icelandic low is anomalously strong (weak). This affects, for example, temperature, precipitation, wind, and surface heat flux over the North Atlantic, and over large parts of Europe. In observations we find enhanced sub-decadal variability of the NAO index that goes along with a dipolar sea surface temperature (SST) pattern. The corresponding SLP and SST patterns are reproduced in a control experiment of the Kiel Climate Model (KCM). Large-scale air-sea interaction is suggested to be essential for the North Atlantic sub-decadal variability in the KCM. The Atlantic Meridional Overturning Circulation (AMOC) plays a key role, setting the timescale of the variability by providing a delayed negative feedback to the NAO. The interplay of the NAO and the AMOC on the sub-decadal timescale is further investigated in the CMIP5 model ensemble. For example, the average CMIP5 model AMOC pattern associated with sub-decadal variability is characterized by a deep-reaching dipolar structure, similar to the KCM's sub-decadal AMOC variability pattern. The results suggest that dynamical air-sea interactions are crucial to generate enhanced sub-decadal variability in the North Atlantic climate.

Climate, Birth Weight, and Agricultural Livelihoods in Kenya and Mali

PubMed Central

Grace, Kathryn; Nawrotzki, Raphael J.

2018-01-01

Objectives. To examine an association between climate variability and birth weight in Mali and Kenya in relation to the local agricultural specialization. Methods. We combined health and sociodemographic data from the Demographic Health Surveys for Kenya (2008 and 2014) and Mali (2006 and 2012) with detailed data on precipitation, temperature, and vegetation. We analyzed the association between climate variability and birth weight by using multilevel regression models for the most common agricultural specializations: food cropping, cash cropping, and pastoralism. Results. There are differences in sensitivity to climate among different agricultural communities. An additional 100 millimeters of rainfall during the 12-month period before birth was associated with a 47-gram (P = .001) and 89-gram (P = .10) increase in birth weight for food croppers in Kenya and Mali, respectively. Every additional hot month in food-cropping communities in Kenya was associated with a 71-gram decrease in birth weight (P = .030), likely because of food croppers’ limited use of modern agricultural techniques. Overall, cash croppers are least sensitive to climate variability in both countries. Conclusions. Effective climate change adaptation strategies are essential for protecting and improving health outcomes and should be tailored to local households’ livelihood strategies. PMID:29072943

Climate change impacts on crop yield in the Euro-Mediterranean region

NASA Astrophysics Data System (ADS)

Toreti, Andrea; Ceglar, Andrej; Dentener, Frank; Niemeyer, Stefan; Dosio, Alessandro; Fumagalli, Davide

2017-04-01

Agriculture is strongly influenced by climate variability, climate extremes and climate changes. Recent studies on past decades have identified and analysed the effects of climate variability and extremes on crop yields in the Euro-Mediterranean region. As these effects could be amplified in a changing climate context, it is essential to analyse available climate projections and investigate the possible impacts on European agriculture in terms of crop yield. In this study, five model runs from the Euro-CORDEX initiative under two scenarios (RCP4.5 and RCP8.5) have been used. Climate model data have been bias corrected and then used to feed a mechanistic crop growth model. The crop model has been run under different settings to better sample the intrinsic uncertainties. Among the main results, it is worth to report a weak but significant and spatially homogeneous increase in potential wheat yield at mid-century (under a CO2 fertilisation effect scenario). While more complex changes seem to characterise potential maize yield, with large areas in the region showing a weak-to-moderate decrease.

Noise-induced transitions and shifts in a climate-vegetation feedback model.

PubMed

Alexandrov, Dmitri V; Bashkirtseva, Irina A; Ryashko, Lev B

2018-04-01

Motivated by the extremely important role of the Earth's vegetation dynamics in climate changes, we study the stochastic variability of a simple climate-vegetation system. In the case of deterministic dynamics, the system has one stable equilibrium and limit cycle or two stable equilibria corresponding to two opposite (cold and warm) climate-vegetation states. These states are divided by a separatrix going across a point of unstable equilibrium. Some possible stochastic scenarios caused by different externally induced natural and anthropogenic processes inherit properties of deterministic behaviour and drastically change the system dynamics. We demonstrate that the system transitions across its separatrix occur with increasing noise intensity. The climate-vegetation system therewith fluctuates, transits and localizes in the vicinity of its attractor. We show that this phenomenon occurs within some critical range of noise intensities. A noise-induced shift into the range of smaller global average temperatures corresponding to substantial oscillations of the Earth's vegetation cover is revealed. Our analysis demonstrates that the climate-vegetation interactions essentially contribute to climate dynamics and should be taken into account in more precise and complex models of climate variability.

Selenium deficiency risk predicted to increase under future climate change.

PubMed

Jones, Gerrad D; Droz, Boris; Greve, Peter; Gottschalk, Pia; Poffet, Deyan; McGrath, Steve P; Seneviratne, Sonia I; Smith, Pete; Winkel, Lenny H E

2017-03-14

Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980-1999) global distributions of Selenium (Se), an essential trace element that is required for humans. Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate-soil interactions. Using moderate climate-change scenarios for 2080-2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate-soil interactions to Se distributions suggests that other trace elements with similar retention mechanisms will be similarly affected by climate change.

Use of Climatic Information In Regional Water Resources Assessment

NASA Astrophysics Data System (ADS)

Claps, P.

Relations between climatic parameters and hydrological variables at the basin scale are investigated, with the aim of evaluating in a parsimonious way physical parameters useful both for a climatic classification of an area and for supporting statistical models of water resources assessment. With reference to the first point, literature methods for distributed evaluation of parameters such as temperature, global and net solar radiation, precipitation, have been considered at the annual scale with the aim of considering the viewpoint of the robust evaluation of parameters based on few basic physical variables of simple determination. Elevation, latitude and average annual number of sunny days have demonstrated to be the essential parameters with respect to the evaluation of climatic indices related to the soil water deficit and to the radiative balance. The latter term was evaluated at the monthly scale and validated (in the `global' term) with measured data. in questo caso riferite al bilancio idrico a scala annuale. Budyko, Thornthwaite and Emberger climatic indices were evaluated on the 10,000 km2 territory of the Basilicata region (southern Italy) based on a 1.1. km grid. They were compared in terms of spatial variability and sensitivity to the variation of the basic variables in humid and semi-arid areas. The use of the climatic index data with respect to statistical parameters of the runoff series in some gauging stations of the region demonstrated the possibility to support regionalisation of the annual runoff using climatic information, with clear distinction of the variability of the coefficient of variation in terms of the humidity-aridity of the basin.

Contrasting scaling properties of interglacial and glacial climates

NASA Astrophysics Data System (ADS)

Ditlevsen, Peter; Shao, Zhi-Gang

2017-04-01

Understanding natural climate variability is essential for assessments of climate change. This is reflected in the scaling properties of climate records. The scaling exponents of the interglacial and the glacial climates are fundamentally different. The Holocene record is monofractal, with a scaling exponent H˜0.7. On the contrary, the glacial record is multifractal, with a significantly higher scaling exponent H˜1.2, indicating a longer persistence time and stronger nonlinearities in the glacial climate. The glacial climate is dominated by the strong multi-millennial Dansgaard-Oeschger (DO) events influencing the long-time correlation. However, by separately analysing the last glacial maximum lacking DO events, here we find the same scaling for that period as for the full glacial period. The unbroken scaling thus indicates that the DO events are part of the natural variability and not externally triggered. At glacial time scales, there is a scale break to a trivial scaling, contrasting the DO events from the similarly saw-tooth-shaped glacial cycles. Ref: Zhi-Gang Shao and Peter Ditlevsen, Nature Comm. 7, 10951, 2016

Assessment of Satellite Radiometry in the Visible Domain

NASA Technical Reports Server (NTRS)

Melin, Frederick; Franz, Bryan A.

2014-01-01

Marine reflectance and chlorophyll-a concentration are listed among the Essential Climate Variables by the Global Climate Observing System. To contribute to climate research, the satellite ocean color data records resulting from successive missions need to be consistent and well characterized in terms of uncertainties. This chapter reviews various approaches that can be used for the assessment of satellite ocean color data. Good practices for validating satellite products with in situ data and the current status of validation results are illustrated. Model-based approaches and inter-comparison techniques can also contribute to characterize some components of the uncertainty budget, while time series analysis can detect issues with the instrument radiometric characterization and calibration. Satellite data from different missions should also provide a consistent picture in scales of variability, including seasonal and interannual signals. Eventually, the various assessment approaches should be combined to create a fully characterized climate data record from satellite ocean color.

Estimation of the fractional coverage of rainfall in climate models

NASA Technical Reports Server (NTRS)

Eltahir, E. A. B.; Bras, R. L.

1993-01-01

The fraction of the grid cell area covered by rainfall, mu, is an essential parameter in descriptions of land surface hydrology in climate models. A simple procedure is presented for estimating this fraction, based on extensive observations of storm areas and rainfall volumes. Storm area and rainfall volume are often linearly related; this relation can be used to compute the storm area from the volume of rainfall simulated by a climate model. A formula is developed for computing mu, which describes the dependence of the fractional coverage of rainfall on the season of the year, the geographical region, rainfall volume, and the spatial and temporal resolution of the model. The new formula is applied in computing mu over the Amazon region. Significant temporal variability in the fractional coverage of rainfall is demonstrated. The implications of this variability for the modeling of land surface hydrology in climate models are discussed.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa.

PubMed

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-06-14

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

PubMed Central

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-01-01

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of KnowledgeSM databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably. PMID:27314369

Effects of climate change on soil moisture over China from 1960-2006

USGS Publications Warehouse

Zhu, Q.; Jiang, H.; Liu, J.

2009-01-01

Soil moisture is an important variable in the climate system and it has sensitive impact on the global climate. Obviously it is one of essential components in the climate change study. The Integrated Biosphere Simulator (IBIS) is used to evaluate the spatial and temporal patterns of soil moisture across China under the climate change conditions for the period 1960-2006. Results show that the model performed better in warm season than in cold season. Mean errors (ME) are within 10% for all the months and root mean squared errors (RMSE) are within 10% except winter season. The model captured the spatial variability higher than 50% in warm seasons. Trend analysis based on the Mann-Kendall method indicated that soil moisture in most area of China is decreased especially in the northern China. The areas with significant increasing trends in soil moisture mainly locate at northwestern China and small areas in southeastern China and eastern Tibet plateau. ?? 2009 IEEE.

Smallholder agriculture in India and adaptation to current and future climate variability and climate change

NASA Astrophysics Data System (ADS)

Murari, K. K.; Jayaraman, T.

2014-12-01

Modeling studies have indicated that global warming, in many regions, will increase the exposure of major crops to rainfall and temperature stress, leading to lower crop yields. Climate variability alone has a potential to decrease yield to an extent comparable to or greater than yield reductions expected due to rising temperature. For India, where agriculture is important, both in terms of food security as well as a source of livelihoods to a majority of its population, climate variability and climate change are subjects of serious concern. There is however a need to distinguish the impact of current climate variability and climate change on Indian agriculture, especially in relation to their socioeconomic impact. This differentiation is difficult to determine due to the secular trend of increasing production and yield of the past several decades. The current research in this aspect is in an initial stage and requires a multi-disciplinary effort. In this study, we assess the potential differential impacts of environmental stress and shock across different socioeconomic strata of the rural population, using village level survey data. The survey data from eight selected villages, based on the Project on Agrarian Relations in India conducted by the Foundation for Agrarian Studies, indicated that income from crop production of the top 20 households (based on the extent of operational land holding, employment of hired labour and asset holdings) is a multiple of the mean income of the village. In sharp contrast, the income of the bottom 20 households is a fraction of the mean and sometimes negative, indicating a net loss from crop production. The considerable differentials in output and incomes suggest that small and marginal farmers are far more susceptible to climate variability and climate change than the other sections. Climate change is effectively an immediate threat to small and marginal farmers, which is driven essentially by socioeconomic conditions. The impact of climate variability on smallholder agriculture in the present can therefore provide important insights into the nature of its vulnerability to future climate change.

Towards Consistent Aerosol and Cloud Climate Data Records from the Along Track Scanning Radiometer instruments

NASA Astrophysics Data System (ADS)

Thomas, G.

2015-12-01

The ESA Climate Change Initiative (CCI) programme has provided a mechanism for the production of new long-term data records of essential climate variables (ECVs) defined by WMO Global Climate Observing System (GCOS). These include consistent cloud (from the MODIS, AVHRR, ATSR-2 and AATSR instruments) and aerosol (from ATSR-2 and AATSR) products produced using the Optimal Retrieval of Aerosol and Cloud (ORAC) scheme. This talk will present an overview of the newly produced ORAC cloud and aerosol datasets, their evaluation and a joint aerosol-cloud product produced for the 1995-2012 ATSR-2-AATSR data record.

Online Impact Prioritization of Essential Climate Variables on Climate Change

NASA Astrophysics Data System (ADS)

Forsythe-Newell, S. P.; Barkstrom, B. B.; Roberts, K. P.

2007-12-01

The National Oceanic & Atmospheric Administration (NOAA)'s NCDC Scientific Data Stewardship (SDS) Team has developed an online prototype that is capable of displaying the "big picture" perspective of all Essential Climate Variable (ECV) impacts on society and value to the IPCC. This prototype ECV-Model provides the ability to visualize global ECV information with options to drill down in great detail. It offers a quantifiable prioritization of ECV impacts that potentially may significantly enhance collaboration with respect to dealing effectively with climate change. The ECV-Model prototype assures anonymity and provides an online input mechanism for subject matter experts and decision makers to access, review and submit: (1) ranking of ECV"s, (2) new ECV's and associated impact categories and (3) feedback about ECV"s, satellites, etc. Input and feedback are vetted by experts before changes or additions are implemented online. The SDS prototype also provides an intuitive one-stop web site that displays past, current and planned launches of satellites; and general as well as detailed information in conjunction with imagery. NCDC's version 1.0 release will be available to the public and provide an easy "at-a-glance" interface to rapidly identify gaps and overlaps of satellites and associated instruments monitoring climate change ECV's. The SDS version 1.1 will enhance depiction of gaps and overlaps with instruments associated with In-Situ and Satellites related to ECVs. NOAA's SDS model empowers decision makers and the scientific community to rapidly identify weaknesses and strengths in monitoring climate change ECV's and potentially significantly enhance collaboration.

The MeteoMet2 project—highlights and results

NASA Astrophysics Data System (ADS)

Merlone, A.; Sanna, F.; Beges, G.; Bell, S.; Beltramino, G.; Bojkovski, J.; Brunet, M.; del Campo, D.; Castrillo, A.; Chiodo, N.; Colli, M.; Coppa, G.; Cuccaro, R.; Dobre, M.; Drnovsek, J.; Ebert, V.; Fernicola, V.; Garcia-Benadí, A.; Garcia-Izquierdo, C.; Gardiner, T.; Georgin, E.; Gonzalez, A.; Groselj, D.; Heinonen, M.; Hernandez, S.; Högström, R.; Hudoklin, D.; Kalemci, M.; Kowal, A.; Lanza, L.; Miao, P.; Musacchio, C.; Nielsen, J.; Nogueras-Cervera, M.; Oguz Aytekin, S.; Pavlasek, P.; de Podesta, M.; Rasmussen, M. K.; del-Río-Fernández, J.; Rosso, L.; Sairanen, H.; Salminen, J.; Sestan, D.; Šindelářová, L.; Smorgon, D.; Sparasci, F.; Strnad, R.; Underwood, R.; Uytun, A.; Voldan, M.

2018-02-01

Launched in 2011 within the European Metrology Research Programme (EMRP) of EURAMET, the joint research project ‘MeteoMet’—Metrology for Meteorology—is the largest EMRP consortium; national metrology institutes, universities, meteorological and climate agencies, research institutes, collaborators and manufacturers are working together, developing new metrological techniques, as well as improving existing ones, for use in meteorological observations and climate records. The project focuses on humidity in the upper and surface atmosphere, air temperature, surface and deep-sea temperatures, soil moisture, salinity, permafrost temperature, precipitation, and the snow albedo effect on air temperature. All tasks are performed using a rigorous metrological approach and include the design and study of new sensors, new calibration facilities, the investigation of sensor characteristics, improved techniques for measurements of essential climate variables with uncertainty evaluation, traceability, laboratory proficiency and the inclusion of field influencing parameters, long-lasting measurements, and campaigns in remote and extreme areas. The vision for MeteoMet is to take a step further towards establishing full data comparability, coherency, consistency, and long-term continuity, through a comprehensive evaluation of the measurement uncertainties for the quantities involved in the global climate observing systems and the derived observations. The improvement in quality of essential climate variables records, through the inclusion of measurement uncertainty budgets, will also highlight possible strategies for the reduction of the uncertainty. This contribution presents selected highlights of the MeteoMet project and reviews the main ongoing activities, tasks and deliverables, with a view to its possible future evolution and extended impact.

Improved spectral comparisons of paleoclimate models and observations via proxy system modeling: Implications for multi-decadal variability

NASA Astrophysics Data System (ADS)

Dee, S. G.; Parsons, L. A.; Loope, G. R.; Overpeck, J. T.; Ault, T. R.; Emile-Geay, J.

2017-10-01

The spectral characteristics of paleoclimate observations spanning the last millennium suggest the presence of significant low-frequency (multi-decadal to centennial scale) variability in the climate system. Since this low-frequency climate variability is critical for climate predictions on societally-relevant scales, it is essential to establish whether General Circulation models (GCMs) are able to simulate it faithfully. Recent studies find large discrepancies between models and paleoclimate data at low frequencies, prompting concerns surrounding the ability of GCMs to predict long-term, high-magnitude variability under greenhouse forcing (Laepple and Huybers, 2014a, 2014b). However, efforts to ground climate model simulations directly in paleoclimate observations are impeded by fundamental differences between models and the proxy data: proxy systems often record a multivariate and/or nonlinear response to climate, precluding a direct comparison to GCM output. In this paper we bridge this gap via a forward proxy modeling approach, coupled to an isotope-enabled GCM. This allows us to disentangle the various contributions to signals embedded in ice cores, speleothem calcite, coral aragonite, tree-ring width, and tree-ring cellulose. The paper addresses the following questions: (1) do forward-modeled ;pseudoproxies; exhibit variability comparable to proxy data? (2) if not, which processes alter the shape of the spectrum of simulated climate variability, and are these processes broadly distinguishable from climate? We apply our method to representative case studies, and broaden these insights with an analysis of the PAGES2k database (PAGES2K Consortium, 2013). We find that current proxy system models (PSMs) can help resolve model-data discrepancies on interannual to decadal timescales, but cannot account for the mismatch in variance on multi-decadal to centennial timescales. We conclude that, specific to this set of PSMs and isotope-enabled model, the paleoclimate record may exhibit larger low-frequency variability than GCMs currently simulate, indicative of incomplete physics and/or forcings.

Job satisfaction and associated variables among nurse assistants working in residential care.

PubMed

Wallin, Anneli Orrung; Jakobsson, Ulf; Edberg, Anna-Karin

2012-12-01

While the work situation for nurse assistants in residential care is strenuous, they themselves often state that they are satisfied with their job. More knowledge is clearly needed of the interrelationship of variables associated with job satisfaction. This study aims to investigate job satisfaction and explore associated variables among nurse assistants working in residential care. A total of 225 respondents completed a questionnaire measuring general job satisfaction, satisfaction with nursing-care provision and measures concerning person-centered care, work climate, leadership, and health complaints. Job satisfaction was the outcome measure and comparisons were made among those reporting low, moderate, and high levels of job satisfaction; multiple regression analyses were used to explore associated variables. The caring climate and personalized care provision were associated with general job satisfaction. High levels of satisfaction with nursing-care provision were also associated with the general work climate, organizational and environmental support, and leadership. Low job satisfaction was mainly associated with health complaints. Nurse assistants working in a positive work climate, caring climate, with a positive attitude to their leaders, who receive organizational and environmental support, provide person-centered care and experience a higher degree of job satisfaction. It seems essential, however, to include both general and context-specific measures when investigating job satisfaction in this field as they reveal different aspects of the nurse assistant's work situation.

Interpretation of Recent Temperature Trends in California

DOE Office of Scientific and Technical Information (OSTI.GOV)

Duffy, P B; Bonfils, C; Lobell, D

2007-09-21

Regional-scale climate change and associated societal impacts result from large-scale (e.g. well-mixed greenhouse gases) and more local (e.g. land-use change) 'forcing' (perturbing) agents. It is essential to understand these forcings and climate responses to them, in order to predict future climate and societal impacts. California is a fine example of the complex effects of multiple climate forcings. The State's natural climate is diverse, highly variable, and strongly influenced by ENSO. Humans are perturbing this complex system through urbanization, irrigation, and emission of multiple types of aerosols and greenhouse gases. Despite better-than-average observational coverage, we are only beginning to understand themore » manifestations of these forcings in California's temperature record.« less

CEOS SEO and GISS Meeting

NASA Technical Reports Server (NTRS)

Killough, Brian; Stover, Shelley

2008-01-01

The Committee on Earth Observation Satellites (CEOS) provides a brief to the Goddard Institute for Space Studies (GISS) regarding the CEOS Systems Engineering Office (SEO) and current work on climate requirements and analysis. A "system framework" is provided for the Global Earth Observation System of Systems (GEOSS). SEO climate-related tasks are outlined including the assessment of essential climate variable (ECV) parameters, use of the "systems framework" to determine relevant informational products and science models and the performance of assessments and gap analyses of measurements and missions for each ECV. Climate requirements, including instruments and missions, measurements, knowledge and models, and decision makers, are also outlined. These requirements would establish traceability from instruments to products and services allowing for benefit evaluation of instruments and measurements. Additionally, traceable climate requirements would provide a better understanding of global climate models.

Changing precipitation in western Europe, climate change or natural variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart

2017-04-01

Multi-model RCM-GCM ensembles provide high resolution climate projections, valuable for among others climate impact assessment studies. While the application of multiple models (both GCMs and RCMs) provides a certain robustness with respect to model uncertainty, the interpretation of differences between ensemble members - the combined result of model uncertainty and natural variability of the climate system - is not straightforward. Natural variability is intrinsic to the climate system, and a potentially large source of uncertainty in climate change projections, especially for projections on the local to regional scale. To quantify the natural variability and get a robust estimate of the forced climate change response (given a certain model and forcing scenario), large ensembles of climate model simulations of the same model provide essential information. While for global climate models (GCMs) a number of such large single model ensembles exists and have been analyzed, for regional climate models (RCMs) the number and size of single model ensembles is limited, and the predictability of the forced climate response at the local to regional scale is still rather uncertain. We present a regional downscaling of a 16-member single model ensemble over western Europe and the Alps at a resolution of 0.11 degrees (˜12km), similar to the highest resolution EURO-CORDEX simulations. This 16-member ensemble was generated by the GCM EC-EARTH, which was downscaled with the RCM RACMO for the period 1951-2100. This single model ensemble has been investigated in terms of the ensemble mean response (our estimate of the forced climate response), as well as the difference between the ensemble members, which measures natural variability. We focus on the response in seasonal mean and extreme precipitation (seasonal maxima and extremes with a return period up to 20 years) for the near to far future. For most precipitation indices we can reliably determine the climate change signal, given the applied model chain and forcing scenario. However, the analysis also shows how limited the information in single ensemble members is on the local scale forced climate response, even for high levels of global warming when the forced response has emerged from natural variability. Analysis and application of multi-model ensembles like EURO-CORDEX should go hand-in-hand with single model ensembles, like the one presented here, to be able to correctly interpret the fine-scale information in terms of a forced signal and random noise due to natural variability.

Quantifying Tropical Glacier Mass Balance Sensitivity to Climate Change Through Regional-Scale Modeling and The Randolph Glacier Inventory

NASA Astrophysics Data System (ADS)

Malone, A.

2017-12-01

Quantifying mass balance sensitivity to climate change is essential for forecasting glacier evolution and deciphering climate signals embedded in archives of past glacier changes. Ideally, these quantifications result from decades of field measurement, remote sensing, and a hierarchy modeling approach, but in data-sparse regions, such as the Himalayas and tropical Andes, regional-scale modeling rooted in first principles provides a first-order picture. Previous regional-scaling modeling studies have applied a surface energy and mass balance approach in order to quantify equilibrium line altitude sensitivity to climate change. In this study, an expanded regional-scale surface energy and mass balance model is implemented to quantify glacier-wide mass balance sensitivity to climate change for tropical Andean glaciers. Data from the Randolph Glacier Inventory are incorporated, and additional physical processes are included, such as a dynamic albedo and cloud-dependent atmospheric emissivity. The model output agrees well with the limited mass balance records for tropical Andean glaciers. The dominant climate variables driving interannual mass balance variability differ depending on the climate setting. For wet tropical glaciers (annual precipitation >0.75 m y-1), temperature is the dominant climate variable. Different hypotheses for the processes linking wet tropical glacier mass balance variability to temperature are evaluated. The results support the hypothesis that glacier-wide mass balance on wet tropical glaciers is largely dominated by processes at the lowest elevation where temperature plays a leading role in energy exchanges. This research also highlights the transient nature of wet tropical glaciers - the vast majority of tropical glaciers and a vital regional water resource - in an anthropogenic warming world.

Twentieth century bipolar seesaw of the Arctic and Antarctic surface air temperatures

NASA Astrophysics Data System (ADS)

Chylek, Petr; Folland, Chris K.; Lesins, Glen; Dubey, Manvendra K.

2010-04-01

Understanding the phase relationship between climate changes in the Arctic and Antarctic regions is essential for our understanding of the dynamics of the Earth's climate system. In this paper we show that the 20th century de-trended Arctic and Antarctic temperatures vary in anti-phase seesaw pattern - when the Arctic warms the Antarctica cools and visa versa. This is the first time that a bi-polar seesaw pattern has been identified in the 20th century Arctic and Antarctic temperature records. The Arctic (Antarctic) de-trended temperatures are highly correlated (anti-correlated) with the Atlantic Multi-decadal Oscillation (AMO) index suggesting the Atlantic Ocean as a possible link between the climate variability of the Arctic and Antarctic regions. Recent accelerated warming of the Arctic results from a positive reinforcement of the linear warming trend (due to an increasing concentration of greenhouse gases and other possible forcings) by the warming phase of the multidecadal climate variability (due to fluctuations of the Atlantic Ocean circulation).

Flood events across the North Atlantic region - past development and future perspectives

NASA Astrophysics Data System (ADS)

Matti, Bettina; Dieppois, Bastien; Lawler, Damian; Dahlke, Helen E.; Lyon, Steve W.

2016-04-01

Flood events have a large impact on humans, both socially and economically. An increase in winter and spring flooding across much of northern Europe in recent years opened up the question of changing underlying hydro-climatic drivers of flood events. Predicting the manifestation of such changes is difficult due to the natural variability and fluctuations in northern hydrological systems caused by large-scale atmospheric circulations, especially under altered climate conditions. Improving knowledge on the complexity of these hydrological systems and their interactions with climate is essential to be able to determine drivers of flood events and to predict changes in these drivers under altered climate conditions. This is particularly true for the North Atlantic region where both physical catchment properties and large-scale atmospheric circulations have a profound influence on floods. This study explores changes in streamflow across North Atlantic region catchments. An emphasis is placed on high-flow events, namely the timing and magnitude of past flood events, and selected flood percentiles were tested for stationarity by applying a flood frequency analysis. The issue of non-stationarity of flood return periods is important when linking streamflow to large-scale atmospheric circulations. Natural fluctuations in these circulations are found to have a strong influence on the outcome causing natural variability in streamflow records. Long time series and a multi-temporal approach allows for determining drivers of floods and linking streamflow to large-scale atmospheric circulations. Exploring changes in selected hydrological signatures consistency was found across much of the North Atlantic region suggesting a shift in flow regime. The lack of an overall regional pattern suggests that how catchments respond to changes in climatic drivers is strongly influenced by their physical characteristics. A better understanding of hydrological response to climate drivers is essential for example for forecasting purposes.

Usefulness of AIRS-Derived OLR, Temperature, Water Vapor and Cloudiness Anomaly Trends for GCM Validation

NASA Technical Reports Server (NTRS)

Molnar, Gyula I.; Susskind, Joel; Iredell, Lena F.

2010-01-01

Mainly due to their global nature, satellite observations can provide a very useful basis for GCM validations. In particular, satellite sounders such as AIRS provide 3-D spatial information (most useful for GCMs), so the question arises: can we use AIRS datasets for climate variability assessments? We show that the recent (September 2002 February 2010) CERES-observed negative trend in OLR of approx.-0.1 W/sq m/yr averaged over the globe is found in the AIRS OLR data as well. Most importantly, even minute details (down to 1 x 1 degree GCM-scale resolution) of spatial and temporal anomalies and trends of OLR as observed by CERES and computed based on AIRS-retrieved surface and atmospheric geophysical parameters over this time period are essentially the same. The correspondence can be seen even in the very large spatial variations of these trends with local values ranging from -2.6 W/sq m/yr to +3.0 W/sq m/yr in the tropics, for example. This essentially perfect agreement of OLR anomalies and trends derived from observations by two different instruments, in totally independent and different manners, implies that both sets of results must be highly accurate, and indirectly validates the anomalies and trends of other AIRS derived products as well. These products show that global and regional anomalies and trends of OLR, water vapor and cloud cover over the last 7+ years are strongly influenced by EI-Nino-La Nina cycles . We have created climate parameter anomaly datasets using AIRS retrievals which can be compared directly with coupled GCM climate variability assessments. Moreover, interrelationships of these anomalies and trends should also be similar between the observed and GCM-generated datasets, and, in cases of discrepancies, GCM parameterizations could be improved based on the relationships observed in the data. First, we assess spatial "trends" of variability of climatic parameter anomalies [since anomalies relative to the seasonal cycle are good proxies of climate variability] at the common 1x1 degree GCM grid-scale by creating spatial anomaly "trends" based on the first 7+ years of AIRS Version 5 Leve13 data. We suggest that modelers should compare these with their (coupled) GCM's performance covering the same period. We evaluate temporal variability and interrelations of climatic anomalies on global to regional e.g., deep Tropical Hovmoller diagrams, El-Nino-related variability scales, and show the effects of El-Nino-La Nina activity on tropical anomalies and trends of water vapor cloud cover and OLR. For GCMs to be trusted highly for long-term climate change predictions, they should be able to reproduce findings similar to these. In summary, the AIRS-based climate variability analyses provide high quality, informative and physically plausible interrelationships among OLR, temperature, humidity and cloud cover both on the spatial and temporal scales. GCM validations can use these results even directly, e. g., by creating 1x1 degree trendmaps for the same period in coupled climate simulations.

Ecosystem response to climatic variables - air temperature and precipitation: How can these variables alter plant productions in C4-grass dominant ecosystem?

NASA Astrophysics Data System (ADS)

Jung, C. G.; Jiang, L.; Luo, Y.

2017-12-01

Understanding net primary production (NPP) response to the key climatic variables, temperature and precipitation, is essential since the response could be represented by one of future consequences from ecosystem responses. Under future climatic warming, fluctuating precipitation is expected. In addition, NPP solely could not explain whole ecosystem response; therefore, not only NPP, but also above- and below-ground NPP (ANPP and BNPP, respectively) need to be examined. This examination needs to include how the plant productions response along temperature and precipitation gradients. Several studies have examined the response of NPP against each of single climatic variable, but understanding the response of ANPP and BNPP to the multiple variables is notably poor. In this study, we used the plant productions data (NPP, ANPP, and BNPP) with climatic variables, i.e., air temperature and precipitation, from 1999 to 2015 under warming and clipping treatments (mimicking hay-harvesting) in C4-grass dominant ecosystem located in central Oklahoma, United States. Firstly, we examined the nonlinear relationships with the climatic variables for NPP, ANPP and BNPP; and then predicted possible responses in the temperature - precipitation space by using a linear mixed effect model. Nonlinearities of NPP, ANPP and BNPP to the climatic variables have been found to show unimodal curves, and nonlinear models have better goodness of fit as shown lower Akaike information criterion (AIC) than linear models. Optimum condition for NPP is represented at high temperature and precipitation level whereas BNPP is maximized at moderate precipitation levels while ANPP has same range of NPP's optimum condition. Clipping significantly reduced ANPP while there was no clipping effect on NPP and BNPP. Furthermore, inclining NPP and ANPP have shown in a range from moderate to high precipitation level with increasing temperature while inclining pattern for BNPP was observed in moderate precipitation level. Overall, the C4-grass dominant ecosystem has a potential for considerable increases in NPP in hotter and wetter conditions as shown a range from moderate to high temperature and precipitation levels; ANPP has peaked at the high temperature and precipitation level, but maximum BNPP needs moderate precipitation level and high temperature.

NEON Data Products: Supporting the Validation of GCOS Essential Climate Variables

NASA Astrophysics Data System (ADS)

Petroy, S. B.; Fox, A. M.; Metzger, S.; Thorpe, A.; Meier, C. L.

2014-12-01

The National Ecological Observatory Network (NEON) is a continental-scale ecological observation platform designed to collect and disseminate data that contributes to understanding and forecasting the impacts of climate change, land use change, and invasive species on ecology. NEON will collect in-situ and airborne data over 60 sites across the US, including Alaska, Hawaii, and Puerto Rico. The NEON Biomass, Productivity, and Biogeochemistry protocols currently direct the collection of samples from distributed, gradient, and tower plots at each site, with sampling occurring either multiple times during the growing season, annually, or on three- or five-year centers (e.g. for coarse woody debris). These data are processed into a series of field-derived data products (e.g. Biogeochemistry, LAI, above ground Biomass, etc.), and when combined with the NEON airborne hyperspectral and LiDAR imagery, are used support validation efforts of algorithms for deriving vegetation characteristics from the airborne data. Sites are further characterized using airborne data combined with in-situ tower measurements, to create additional data products of interest to the GCOS community, such as Albedo and fPAR. Presented here are a summary of tower/field/airborne sampling and observation protocols and examples of provisional datasets collected at NEON sites that may be used to support the ongoing validation of GCOS Essential Climate Variables.

Some guidelines for helping natural resources adapt to climate change

USGS Publications Warehouse

Baron, Jill S.; Julius, Susan Herrod; West, Jordan M.; Joyce, Linda A.; Blate, Geoffrey; Peterson, Charles H.; Palmer, Margaret; Keller, Brian D.; Kareiva, Peter; Scott, J. Michael; Griffith, Brad

2008-01-01

The changes occurring in mountain regions are an epitome of climate change. The dramatic shrinkage of major glaciers over the past century – and especially in the last 30 years – is one of several iconic images that have come to symbolize climate change. Climate creates the context for ecosystems, and climate variables strongly influence the structure, composition, and processes that characterize distinct ecosystems. Climate change, therefore, is having direct and indirect effects on species attributes, ecological interactions, and ecosystem processes. Because changes in the climate system will continue regardless of emissions mitigation, management strategies to enhance the resilience of ecosystems will become increasingly important. It is essential that management responses to climate change proceed using the best available science despite uncertainties associated with the future path of climate change, the response of ecosystems to climate effects, and the effects of management. Given these uncertainties, management adaptation will require flexibility to reflect our growing understanding of climate change impacts and management effectiveness.

Crop Yield Simulations Using Multiple Regional Climate Models in the Southwestern United States

NASA Astrophysics Data System (ADS)

Stack, D.; Kafatos, M.; Kim, S.; Kim, J.; Walko, R. L.

2013-12-01

Agricultural productivity (described by crop yield) is strongly dependent on climate conditions determined by meteorological parameters (e.g., temperature, rainfall, and solar radiation). California is the largest producer of agricultural products in the United States, but crops in associated arid and semi-arid regions live near their physiological limits (e.g., in hot summer conditions with little precipitation). Thus, accurate climate data are essential in assessing the impact of climate variability on agricultural productivity in the Southwestern United States and other arid regions. To address this issue, we produced simulated climate datasets and used them as input for the crop production model. For climate data, we employed two different regional climate models (WRF and OLAM) using a fine-resolution (8km) grid. Performances of the two different models are evaluated in a fine-resolution regional climate hindcast experiment for 10 years from 2001 to 2010 by comparing them to the North American Regional Reanalysis (NARR) dataset. Based on this comparison, multi-model ensembles with variable weighting are used to alleviate model bias and improve the accuracy of crop model productivity over large geographic regions (county and state). Finally, by using a specific crop-yield simulation model (APSIM) in conjunction with meteorological forcings from the multi-regional climate model ensemble, we demonstrate the degree to which maize yields are sensitive to the regional climate in the Southwestern United States.

Planetary boundary layer as an essential component of the earth's climate system

NASA Astrophysics Data System (ADS)

Davy, Richard; Esau, Igor

2015-04-01

Following the traditional engineering approach proposed by Prandtl, the turbulent planetary boundary layers (PBLs) are considered in the climate science as complex, non-linear, essential but nevertheless subordinated components of the earth's climate system. Correspondingly, the temperature variations, dT - a popular and practically important measure of the climate variability, are seen as the system's response to the external heat forcing, Q, e.g. in the energy balance model of the type dT=Q/C (1). The moderation of this response by non-linear feedbacks embedded in the effective heat capacity, C, are to a large degree overlooked. The effective heat capacity is globally determined by the depth of the ocean mixed layer (on multi-decadal and longer time scales) but regionally, over the continents, C is much smaller and determined (on decadal time scales) by the depth, h, of the PBL. The present understanding of the climatological features of turbulent boundary layers is set by the works of Frankignoul & Hasselmann (1976) and Manabe & Stauffer (1980). The former explained how large-scale climate anomalies could be generated in the case of a large C (in the sea surface temperature) by the delta-correlated stochastic forcing (white noise). The latter demonstrated that the climate response to a given forcing is moderated by the depth, h, so that in the shallow PBL the signal should be significantly amplified. At present there are more than 3000 publications (ISI Web of Knowledge) which detail this understanding but the physical mechanisms, which control the boundary layer depth, and statistical relationships between the turbulent and climatological measures remain either unexplored or incorrectly attributed. In order to identify the climatic role of the PBL, the relationships between the PBL depth, h, - as the integral measure of the turbulent processes and micro-circulations due to the surface heterogeneity - and the climatic variability (variations and trends) of temperature have to be established. These relationships are necessary to complete the model (1) where the relationships between temperature variability, dT, and heat forcing, Q, are intensively studied. We demonstrate that the statistical dependences between dT and h becomes the primary factor in controlling the climate features of the earth's climate system when h is shallow (less than about 500 m). Such conditions are found in the cold (with negative surface heat balance on average) and dry (with large-scale air subsidence) climates. To get those climates and their variations correct, the climate models must be able to reproduce the shallow stably-stratified PBL. We show that the present-day CMIP-5 models are systematically and strongly biased towards producing deeper PBLs (between 20-50% deeper than observed) in this part of the parameter space which leads to large errors (around 15 K) and a damped variability of the surface temperatures under these conditions. More generally, this bias indicates that the models represent the earth's cooling processes incorrectly, which may be a part of the puzzle of the observed "hiatus" (or pause) in global warming. Frankignoul, C. & K. Hasselmann, 1977: Stochastic climate models. Part 2, Application to sea-surface temperature anomalies and thermocline variability, Tellus, 29, 289-305. Manabe, S. & R. Stouffer, 1980: Sensitivity of a Global Climate Model to an increase of CO2 concentration in the atmosphere, Journal of Geophysical Research, 85(C10): 5529-5554.

Modelling seasonal effects of temperature and precipitation on honey bee winter mortality in a temperate climate.

PubMed

Switanek, Matthew; Crailsheim, Karl; Truhetz, Heimo; Brodschneider, Robert

2017-02-01

Insect pollinators are essential to global food production. For this reason, it is alarming that honey bee (Apis mellifera) populations across the world have recently seen increased rates of mortality. These changes in colony mortality are often ascribed to one or more factors including parasites, diseases, pesticides, nutrition, habitat dynamics, weather and/or climate. However, the effect of climate on colony mortality has never been demonstrated. Therefore, in this study, we focus on longer-term weather conditions and/or climate's influence on honey bee winter mortality rates across Austria. Statistical correlations between monthly climate variables and winter mortality rates were investigated. Our results indicate that warmer and drier weather conditions in the preceding year were accompanied by increased winter mortality. We subsequently built a statistical model to predict colony mortality using temperature and precipitation data as predictors. Our model reduces the mean absolute error between predicted and observed colony mortalities by 9% and is statistically significant at the 99.9% confidence level. This is the first study to show clear evidence of a link between climate variability and honey bee winter mortality. Copyright © 2016 British Geological Survey, NERC. Published by Elsevier B.V. All rights reserved.

Selenium deficiency risk predicted to increase under future climate change

PubMed Central

Jones, Gerrad D.; Droz, Boris; Greve, Peter; Gottschalk, Pia; Poffet, Deyan; McGrath, Steve P.; Seneviratne, Sonia I.; Smith, Pete; Winkel, Lenny H. E.

2017-01-01

Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980–1999) global distributions of Selenium (Se), an essential trace element that is required for humans. Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate–soil interactions. Using moderate climate-change scenarios for 2080–2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate–soil interactions to Se distributions suggests that other trace elements with similar retention mechanisms will be similarly affected by climate change. PMID:28223487

Actor groups, related needs, and challenges at the climate downscaling interface

NASA Astrophysics Data System (ADS)

Rössler, Ole; Benestad, Rasmus; Diamando, Vlachogannis; Heike, Hübener; Kanamaru, Hideki; Pagé, Christian; Margarida Cardoso, Rita; Soares, Pedro; Maraun, Douglas; Kreienkamp, Frank; Christodoulides, Paul; Fischer, Andreas; Szabo, Peter

2016-04-01

At the climate downscaling interface, numerous downscaling techniques and different philosophies compete on being the best method in their specific terms. Thereby, it remains unclear to what extent and for which purpose these downscaling techniques are valid or even the most appropriate choice. A common validation framework that compares all the different available methods was missing so far. The initiative VALUE closes this gap with such a common validation framework. An essential part of a validation framework for downscaling techniques is the definition of appropriate validation measures. The selection of validation measures should consider the needs of the stakeholder: some might need a temporal or spatial average of a certain variable, others might need temporal or spatial distributions of some variables, still others might need extremes for the variables of interest or even inter-variable dependencies. Hence, a close interaction of climate data providers and climate data users is necessary. Thus, the challenge in formulating a common validation framework mirrors also the challenges between the climate data providers and the impact assessment community. This poster elaborates the issues and challenges at the downscaling interface as it is seen within the VALUE community. It suggests three different actor groups: one group consisting of the climate data providers, the other two groups being climate data users (impact modellers and societal users). Hence, the downscaling interface faces classical transdisciplinary challenges. We depict a graphical illustration of actors involved and their interactions. In addition, we identified four different types of issues that need to be considered: i.e. data based, knowledge based, communication based, and structural issues. They all may, individually or jointly, hinder an optimal exchange of data and information between the actor groups at the downscaling interface. Finally, some possible ways to tackle these issues are discussed.

Global Land Product Validation Protocols: An Initiative of the CEOS Working Group on Calibration and Validation to Evaluate Satellite-derived Essential Climate Variables

NASA Astrophysics Data System (ADS)

Guillevic, P. C.; Nickeson, J. E.; Roman, M. O.; camacho De Coca, F.; Wang, Z.; Schaepman-Strub, G.

2016-12-01

The Global Climate Observing System (GCOS) has specified the need to systematically produce and validate Essential Climate Variables (ECVs). The Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) and in particular its subgroup on Land Product Validation (LPV) is playing a key coordination role leveraging the international expertise required to address actions related to the validation of global land ECVs. The primary objective of the LPV subgroup is to set standards for validation methods and reporting in order to provide traceable and reliable uncertainty estimates for scientists and stakeholders. The Subgroup is comprised of 9 focus areas that encompass 10 land surface variables. The activities of each focus area are coordinated by two international co-leads and currently include leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR), vegetation phenology, surface albedo, fire disturbance, snow cover, land cover and land use change, soil moisture, land surface temperature (LST) and emissivity. Recent additions to the focus areas include vegetation indices and biomass. The development of best practice validation protocols is a core activity of CEOS LPV with the objective to standardize the evaluation of land surface products. LPV has identified four validation levels corresponding to increasing spatial and temporal representativeness of reference samples used to perform validation. Best practice validation protocols (1) provide the definition of variables, ancillary information and uncertainty metrics, (2) describe available data sources and methods to establish reference validation datasets with SI traceability, and (3) describe evaluation methods and reporting. An overview on validation best practice components will be presented based on the LAI and LST protocol efforts to date.

Linking the climatic and geochemical controls on global soil carbon cycling

NASA Astrophysics Data System (ADS)

Doetterl, Sebastian; Stevens, Antoine; Six, Johan; Merckx, Roel; Van Oost, Kristof; Casanova Pinto, Manuel; Casanova-Katny, Angélica; Muñoz, Cristina; Boudin, Mathieu; Zagal Venegas, Erick; Boeckx, Pascal

2015-04-01

Climatic and geochemical parameters are regarded as the primary controls for soil organic carbon (SOC) storage and turnover. However, due to the difference in scale between climate and geochemical-related soil research, the interaction of these key factors for SOC dynamics have rarely been assessed. Across a large geochemical and climatic transect in similar biomes in Chile and the Antarctic Peninsula we show how abiotic geochemical soil features describing soil mineralogy and weathering pose a direct control on SOC stocks, concentration and turnover and are central to explaining soil C dynamics at larger scales. Precipitation and temperature had an only indirect control by regulating geochemistry. Soils with high SOC content have low specific potential CO2 respiration rates, but a large fraction of SOC that is stabilized via organo-mineral interactions. The opposite was observed for soils with low SOC content. The observed differences for topsoil SOC stocks along this transect of similar biomes but differing geo-climatic site conditions are of the same magnitude as differences observed for topsoil SOC stocks across all major global biomes. Using precipitation and a set of abiotic geochemical parameters describing soil mineralogy and weathering status led to predictions of high accuracy (R2 0.53-0.94) for different C response variables. Partial correlation analyses revealed that the strength of the correlation between climatic predictors and SOC response variables decreased by 51 - 83% when controlling for geochemical predictors. In contrast, controlling for climatic variables did not result in a strong decrease in the strength of the correlations of between most geochemical variables and SOC response variables. In summary, geochemical parameters describing soil mineralogy and weathering were found to be essential for accurate predictions of SOC stocks and potential CO2 respiration, while climatic factors were of minor importance as a direct control, but are important through governing soil weathering and geochemistry. In conclusion, we pledge for a stronger implementation of geochemical soil properties to predict SOC stocks on a global scale. Understanding the effects of climate (temperature and precipitation) change on SOC dynamics also requires good understanding of the relationship between climate and soil geochemistry.

Juniperus phoenicea var. turbinata (Guss.) Parl. Leaf Essential Oil Variability in the Balkans.

PubMed

Rajčević, Nemanja F; Labus, Marina G; Dodoš, Tanja Z; Novaković, Jelica J; Marin, Petar D

2018-06-15

In the present work leaf essential oil from 97 individuals of Juniperus phoenicea var. turbinata (GUSS.) PARL. from the Balkan Peninsula was analysed. The essential oil was dominated by monoterpene hydrocarbons (45.5 - 71.8%), of which α-pinene was the most abundant in almost all of the samples (38.2 - 55.8%). Several other monoterpenes and sesquiterpenes were also present in relatively high abundances in samples such as myrcene, δ-3-carene, ß-phellandrene, α-terpinyl acetate, (E)-caryophyllene and germacrene D. Multivariate statistical analysis suggested the existence of three possible chemotypes based on the abundance of the four components. Even though the intrapopulation variability was high, discriminant analysis (DA) was able to separate populations. DA showed high separation between western and eastern populations but also grouped geographically closer populations along the west Balkan shoreline. The potential influence of the climate on the composition of the essential oil was also studied. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Accurately measuring sea level change from space: an ESA Climate Change Initiative for MSL closure budget studies

NASA Astrophysics Data System (ADS)

Legeais, JeanFrancois; Cazenave, Anny; Ablain, Michael; Larnicol, Gilles; Benveniste, Jerome; Johannessen, Johnny; Timms, Gary; Andersen, Ole; Cipollini, Paolo; Roca, Monica; Rudenko, Sergei; Fernandes, Joana; Balmaseda, Magdalena; Quartly, Graham; Fenoglio-Marc, Luciana; Meyssignac, Benoit; Scharffenberg, Martin

2016-04-01

Sea level is a very sensitive index of climate change and variability. Sea level integrates the ocean warming, mountain glaciers and ice sheet melting. Understanding the sea level variability and changes implies an accurate monitoring of the sea level variable at climate scales, in addition to understanding the ocean variability and the exchanges between ocean, land, cryosphere, and atmosphere. That is why Sea Level is one of the Essential Climate Variables (ECV) selected in the frame of the ESA Climate Change Initiative (CCI) program. It aims at providing long-term monitoring of the sea level ECV with regular updates, as required for climate studies. The program is now in its second phase of 3 year (following phase I during 2011-2013). The objectives are firstly to involve the climate research community, to refine their needs and collect their feedbacks on product quality. And secondly to develop, test and select the best algorithms and standards to generate an updated climate time series and to produce and validate the Sea Level ECV product. This will better answer the climate user needs by improving the quality of the Sea Level products and maintain a sustain service for an up-to-date production. This has led to the production of the Sea Level ECV which has benefited from yearly extensions and now covers the period 1993-2014. We will firstly present the main achievements of the ESA CCI Sea Level Project. On the one hand, the major steps required to produce the 22 years climate time series are briefly described: collect and refine the user requirements, development of adapted algorithms for climate applications and specification of the production system. On the other hand, the product characteristics are described as well as the results from product validation, performed by several groups of the ocean and climate modeling community. At last, new altimeter standards have been developed and the best one have been recently selected in order to produce a full reprocessing of the dataset (performed in 2016) adapted for climate studies. These new standards will be presented as well as other results regarding the improvement of the sea level estimation in the Arctic Ocean and in coastal areas for which preliminary results suggest that significant improvements can be achieved.

Accurately measuring sea level change from space: an ESA climate change initiative for MSL closure budget studies

NASA Astrophysics Data System (ADS)

Legeais, JeanFrancois; Benveniste, Jérôme

2016-07-01

Sea level is a very sensitive index of climate change and variability. Sea level integrates the ocean warming, mountain glaciers and ice sheet melting. Understanding the sea level variability and changes implies an accurate monitoring of the sea level variable at climate scales, in addition to understanding the ocean variability and the exchanges between ocean, land, cryosphere, and atmosphere. That is why Sea Level is one of the Essential Climate Variables (ECV) selected in the frame of the ESA Climate Change Initiative (CCI) program. It aims at providing long-term monitoring of the sea level ECV with regular updates, as required for climate studies. The program is now in its second phase of 3 year (following phase I during 2011-2013). The objectives are firstly to involve the climate research community, to refine their needs and collect their feedbacks on product quality. And secondly to develop, test and select the best algorithms and standards to generate an updated climate time series and to produce and validate the Sea Level ECV product. This will better answer the climate user needs by improving the quality of the Sea Level products and maintain a sustain service for an up-to-date production. This has led to the production of a first version of the Sea Level ECV which has benefited from yearly extensions and now covers the period 1993-2014. Within phase II, new altimeter standards have been developed and tested in order to reprocess the dataset with the best standards for climate studies. The reprocessed ECV will be released in summer 2016. We will present the main achievements of the ESA CCI Sea Level Project. On the one hand, the major steps required to produce the 22 years climate time series are briefly described: collect and refine the user requirements, development of adapted algorithms for climate applications and specification of the production system. On the other hand, the product characteristics are described as well as the results from product validation, performed by several groups of the ocean and climate modeling community. Efforts have also focused on the improvement of the sea level estimation in the Arctic Ocean and in coastal areas for which preliminary results suggest that significant improvements can be achieved.

The Bird Community Resilience Index: a novel remote sensing-based biodiversity variable for quantifying ecological integrity

NASA Astrophysics Data System (ADS)

Michel, N. L.; Wilsey, C.; Burkhalter, C.; Trusty, B.; Langham, G.

2017-12-01

Scalable indicators of biodiversity change are critical to reporting overall progress towards national and global targets for biodiversity conservation (e.g. Aichi Targets) and sustainable development (SDGs). These essential biodiversity variables capitalize on new remote sensing technologies and growth of community science participation. Here we present a novel biodiversity metric quantifying resilience of bird communities and, by extension, of their associated ecological communities. This metric adds breadth to the community composition class of essential biodiversity variables that track trends in condition and vulnerability of ecological communities. We developed this index for use with North American grassland birds, a guild that has experienced stronger population declines than any other avian guild, in order to evaluate gains from the implementation of best management practices on private lands. The Bird Community Resilience Index was designed to incorporate the full suite of species-specific responses to management actions, and be flexible enough to work across broad climatic, land cover, and bird community gradients (i.e., grasslands from northern Mexico through Canada). The Bird Community Resilience Index consists of four components: density estimates of grassland and arid land birds; weighting based on conservation need; a functional diversity metric to incorporate resiliency of bird communities and their ecosystems; and a standardized scoring system to control for interannual variation caused by extrinsic factors (e.g., climate). We present an analysis of bird community resilience across ranches in the Northern Great Plains region of the United States. As predicted, Bird Community Resilience was higher in lands implementing best management practices than elsewhere. While developed for grassland birds, this metric holds great potential for use as an Essential Biodiversity Variable for community composition in a variety of habitat.

Dynamic changes in the rice blast population in the USA over six decades

USDA-ARS?s Scientific Manuscript database

Rice blast disease caused by Magnaporthe oryzae is one of the most destructive diseases of rice. Field isolates of M. oryzae rapidly adapt to the hosts and climate. Tracking the genetic and pathogenic variability of the field isolates is essential to understand how M. oryzae interacts with hosts an...

Evaluation of the MIKE SHE model for application in the Loess Plateau, China

Treesearch

Zhiqiang Zhang; Shenping Wang; Ge Sun; Steven G. McNulty; Huayong Zhang; Jianlao Li; Manliang Zhang; Eduard Klaghofer; Peter Strauss

2008-01-01

Quantifying the hydrologic responses to land use / land cover change and climate variability is essential for integrated sustainable watershed management in water limited regions such as the Loess Plateau in Northwestern China where an adaptive watershed management approach is being implemented. Traditional empirical modeling approach to quantifying the accumulated...

A 50-year precipitation analysis over Europe at 5.5km within the UERRA project

NASA Astrophysics Data System (ADS)

Bazile, Eric; Abida, Rachid; Soci, Cornel; Verrelle, Antoine; Szczypta, Camille; Le Moigne, Patrick

2017-04-01

The UERRA project is a 4-year project (2014-2017) financed by the European Union under its 7th Framework Programme SPACE. One of its main objectives is to provide a 50-year reanalysis dataset of surface essential climate variables (ECV) at 5.5km grid at European scale, together with, as much as possible, uncertainty estimates. One of the ECV is the precipitation and this variable is of essential interest in weather forecasting, climate study and to "drive" hydrological model for water management, or agrometeorology. After a brief description of the method used for the precipitation analysis (Soci et al. 2016)during this project, the preliminary results will be presented. The estimation of uncertainties will be also discussed associated with the problem of the evolution of the observation density network and its impact on the long term series. Additional information about the UERRA project can be found at http://www.uerra.eu The research leading to these results has received funding from the European Union, Seventh Framework Programme (FP7-SPACE-2013-1) under grant agreement no 607193.

Voluntary Field Data Collection for Landscape Phenology and Surface Water Essential Climate Variable Research

NASA Astrophysics Data System (ADS)

Jones, J. W.; Hudson-Dunn, A.; Aquino, K.; Pasa, M.; Paez, F.

2013-12-01

The U.S. Geological Survey is developing techniques to monitor vegetation and surface water condition for improved resource management. Educational materials and data forms that allow volunteer Citizen Scientists to collect information on vegetation and surface water extent to enhance satellite and web camera data analyses (http://egsc.usgs.gov/shenandoah.html) have been developed, tested, and refined. Collection is focused on supplementing landscape phenology and surface water extent (SWE) essential climate variable (ECV) research. Low cost instrumentation, subject education, and measurement calibration techniques all have utility for multiple remote sensing and biophysical studies. Trials have been conducted with subjects ranging from elementary school-aged summer camp children to science major undergraduate and graduate students. Experiments were replicated in several project study areas in Virginia that are also phenology and SWE-ECV research sites. Analysis of volunteer responses and their narrative feedback have improved the ability to request and assess data from volunteers. Children ages 12 and over were able to provide reliable supplemental information for phenology and aquatic research. Finally, trial observation and subject feedback both confirmed that participation furthered volunteer interest in science.

The Climate Variability & Predictability (CVP) Program at NOAA - DYNAMO Recent Project Advancements

NASA Astrophysics Data System (ADS)

Lucas, S. E.; Todd, J. F.; Higgins, W.

2013-12-01

The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International Geosphere-Biosphere Programme (IGBP), and the U.S. Global Change Research Program (USGCRP). The CVP program sits within the Earth System Science (ESS) Division at NOAA's Climate Program Office. Dynamics of the Madden-Julian Oscillation (DYNAMO): The Indian Ocean is one of Earth's most sensitive regions because the interactions between ocean and atmosphere there have a discernable effect on global climate patterns. The tropical weather that brews in that region can move eastward along the equator and reverberate around the globe, shaping weather and climate in far-off places. The vehicle for this variability is a phenomenon called the Madden-Julian Oscillation, or MJO. The MJO, which originates over the Indian Ocean roughly every 30 to 90 days, is known to influence the Asian and Australian monsoons. It can also enhance hurricane activity in the northeast Pacific and Gulf of Mexico, trigger torrential rainfall along the west coast of North America, and affect the onset of El Niño. CVP-funded scientists participated in the DYNAMO field campaign in 2011-12. Results from this international campaign are expected to improve researcher's insights into this influential phenomenon. A better understanding of the processes governing MJO is an essential step toward improving their representations in numerical models and improving MJO simulation and prediction. Recent results from CVP-funded projects will be summarized in this poster.

An overview of new insights from satellite salinity missions on oceanography

NASA Astrophysics Data System (ADS)

Reul, Nicolas

2015-04-01

The Soil Moisture and Ocean Salinity (SMOS) mission, launched on 2 November 2009, is the European Space Agency's (ESA) second Earth Explorer Opportunity mission. The scientific objectives of the SMOS mission directly respond to the need for global observations of soil moisture and ocean salinity, two key variables describing the Earth's water cycle and having been identified as Essential Climate Variables (ECVs) by the Global Climate Observing System (GCOS). After five years of satellite Sea Surface Salinity (SSS) monitoring from SMOS data, we will present an overview of the scientific highlights these data have brougtht to the oceanographic communities. In particular, we shall review the impact of SMOS SSS and brightness tempeaerture data for the monitoring of: -Mesoscale variability of SSS (and density) in frontal structures, eddies, -Ocean propagative SSS signals (e.g. TIW, planetary waves), -Freshwater flux Monitoring (Evaportaion minus precipitation, river run off), -Large scale SSS anomalies related to climate fluctuations (e.g. ENSO, IOD), -Air-Sea interactions (equatorial upwellings, Tropical cyclone wakes) -Temperature-Salinity dependencies, -Sea Ice thickness, -Tropical Storm and high wind monitoring, -Ocean surface bio-geo chemistry.

Is it feasible to estimate radiosonde biases from interlaced measurements?

NASA Astrophysics Data System (ADS)

Kremser, Stefanie; Tradowsky, Jordis S.; Rust, Henning W.; Bodeker, Greg E.

2018-05-01

Upper-air measurements of essential climate variables (ECVs), such as temperature, are crucial for climate monitoring and climate change detection. Because of the internal variability of the climate system, many decades of measurements are typically required to robustly detect any trend in the climate data record. It is imperative for the records to be temporally homogeneous over many decades to confidently estimate any trend. Historically, records of upper-air measurements were primarily made for short-term weather forecasts and as such are seldom suitable for studying long-term climate change as they lack the required continuity and homogeneity. Recognizing this, the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) has been established to provide reference-quality measurements of climate variables, such as temperature, pressure, and humidity, together with well-characterized and traceable estimates of the measurement uncertainty. To ensure that GRUAN data products are suitable to detect climate change, a scientifically robust instrument replacement strategy must always be adopted whenever there is a change in instrumentation. By fully characterizing any systematic differences between the old and new measurement system a temporally homogeneous data series can be created. One strategy is to operate both the old and new instruments in tandem for some overlap period to characterize any inter-instrument biases. However, this strategy can be prohibitively expensive at measurement sites operated by national weather services or research institutes. An alternative strategy that has been proposed is to alternate between the old and new instruments, so-called interlacing, and then statistically derive the systematic biases between the two instruments. Here we investigate the feasibility of such an approach specifically for radiosondes, i.e. flying the old and new instruments on alternating days. Synthetic data sets are used to explore the applicability of this statistical approach to radiosonde change management.

The essential interactions between understanding climate variability and climate change

NASA Astrophysics Data System (ADS)

Neelin, J. D.

2017-12-01

Global change is sometimes perceived as a field separate from other aspects of atmospheric and oceanic sciences. Despite the long history of communication between the scientific communities studying global change and those studying interannual variability and weather, increasing specialization and conflicting societal demands on the fields can put these interactions at risk. At the same time, current trajectories for greenhouse gas emissions imply substantial adaptation to climate change will be necessary. Instead of simply projecting effects to be avoided, the field is increasingly being asked to provide regional-level information for specific adaptation strategies—with associated requirements for increased precision on projections. For extreme events, challenges include validating models for rare events, especially for events that are unprecedented in the historical record. These factors will be illustrated with examples of information transfer to climate change from work on fundamental climate processes aimed originally at timescales from hours to interannual. Work to understand the effects that control probability distributions of moisture, temperature and precipitation in historical weather can yield new factors to examine for the changes in the extremes of these distributions under climate change. Surprisingly simple process models can give insights into the behavior of vastly more complex climate models. Observation systems and model ensembles aimed at weather and interannual variations prove valuable for global change and vice versa. Work on teleconnections in the climate system, such as the remote impacts of El Niño, is informing analysis of projected regional rainfall change over California. Young scientists need to prepare to work across the full spectrum of climate variability and change, and to communicate their findings, as they and our society head for future that is more interesting than optimal.

Biomass and the Climatic Space from historical to future scenarios of a Seasonally Dry Tropical Forest - Caatinga

NASA Astrophysics Data System (ADS)

Castanho, A. D. D. A.; Coe, M. T.; Maia Andrade, E.; Walker, W.; Baccini, A.; Brando, P. M.; Farina, M.

2017-12-01

The Caatinga found in the semiarid region in northeastern Brazil is the largest continuous seasonally dry tropical forest in South America. The region has for centuries been subject to anthropogenic activities of land conversion, abandonment, and regrowth. The region also has a large spatial variability of edaphic-climatic properties. These effects together contribute to a wide variability of plant physiognomies and biomass concentration. In addition to land use change due to anthropogenic activities the region is exposed in the near and long term to dryer conditions. The main goal of this work was to validate a high spatial resolution (30 m) map of above ground biomass, understand the climatic role in the biomass spatial variability in the present, and the potential threat to vegetation for future climatic shifts. Satellite-derived biomass products are advanced tools that can address spatial changes in forest structure for an extended region. Here we combine a compilation of published field phytosociological observations across the region with a new 30-meter spatial resolution satellite biomass product. Climate data used for this analyses were based on the CRU (Climate Research Unit, UEA) for the historical time period and for the future a mean and 25-75% quantiles of the CMIP Global Climate model estimates for the RCP scenarios of 4.5 and 8.5 W/m2. The high heterogeneity in the biomass and physiognomy distribution across the Caatinga region is mostly explained by the climatic space defined by the precipitation and dryness index. The Caatinga region has historically already been exposed to shift in its climatic properties, driving all the physiognomies, to a dryer climatic space within the last decade. Future climate intensify the observed trends. This study provides a clearer understanding of the spatial distribution of Caatinga vegetation, its biomass, and relationships to climate, which are essential for strategic development planning, preservation of the biome functions, human services, and biodiversity, face future climate scenarios.

Integrated ocean management as a strategy to meet rapid climate change: the Norwegian case.

PubMed

Hoel, Alf Håkon; Olsen, Erik

2012-02-01

The prospects of rapid climate change and the potential existence of tipping points in marine ecosystems where nonlinear change may result from them being overstepped, raises the question of strategies for coping with ecosystem change. There is broad agreement that the combined forces of climate change, pollution and increasing economic activities necessitates more comprehensive approaches to oceans management, centering on the concept of ecosystem-based oceans management. This article addresses the Norwegian experience in introducing integrated, ecosystem-based oceans management, emphasizing how climate change, seen as a major long-term driver of change in ecosystems, is addressed in management plans. Understanding the direct effects of climate variability and change on ecosystems and indirect effects on human activities is essential for adaptive planning to be useful in the long-term management of the marine environment.

Evidence for lower plasticity in CTMAX at warmer developmental temperatures.

PubMed

Kellermann, Vanessa; Sgrò, Carla M

2018-06-07

Understanding the capacity for different species to reduce their susceptibility to climate change via phenotypic plasticity is essential for accurately predicting species extinction risk. The climatic variability hypothesis suggests that spatial and temporal variation in climatic variables should select for more plastic phenotypes. However, empirical support for this hypothesis is limited. Here, we examine the capacity for ten Drosophila species to increase their critical thermal maxima (CT MAX ) through developmental acclimation and/or adult heat hardening. Using four fluctuating developmental temperature regimes, ranging from 13 to 33 °C, we find that most species can increase their CT MAX via developmental acclimation and adult hardening, but found no relationship between climatic variables and absolute measures of plasticity. However, when plasticity was dissected across developmental temperatures, a positive association between plasticity and one measure of climatic variability (temperature seasonality) was found when development took place between 26 and 28 °C, whereas a negative relationship was found when development took place between 20 and 23 °C. In addition, a decline in CT MAX and egg-to-adult viability, a proxy for fitness, was observed in tropical species at the warmer developmental temperatures (26-28 °C); this suggests that tropical species may be at even greater risk from climate change than currently predicted. The combined effects of developmental acclimation and adult hardening on CT MAX were small, contributing to a <0.60 °C shift in CT MAX . Although small shifts in CT MAX may increase population persistence in the shorter term, the degree to which they can contribute to meaningful responses in the long term is unclear. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

The Grand Challenges of WCRP and the Climate Observing System of the Future

NASA Astrophysics Data System (ADS)

Brasseur, G. P.

2017-12-01

The successful implementation the Paris agreement on climate change (COP21) calls for a well-designed global monitoring system of essential climate variables, climate processes and Earth system budgets. The Grand Challenges implemented by the World Climate Research Programme (WCRP) provide an opportunity to investigate issues of high societal relevance, directly related to sea level rise, droughts, floods, extreme heat events, food security, and fresh water availability. These challenges would directly benefit from a well-designed suite of systematic climate observations. Quantification of the evolution of the global energy, water and carbon budgets as well as the development and the production of near-term and regional climate predictions require that a comprehensive, focused, multi-platform observing system (satellites, ground-based and in situ observations) be established in an international context. This system must be accompanied by the development of climate services that should translate and disseminate scientific outcomes as actionable information for users and stakeholders.

Marine biodiversity–ecosystem functions under uncertain environmental futures

PubMed Central

Bulling, Mark T.; Hicks, Natalie; Murray, Leigh; Paterson, David M.; Raffaelli, Dave; White, Piran C. L.; Solan, Martin

2010-01-01

Anthropogenic activity is currently leading to dramatic transformations of ecosystems and losses of biodiversity. The recognition that these ecosystems provide services that are essential for human well-being has led to a major interest in the forms of the biodiversity–ecosystem functioning relationship. However, there is a lack of studies examining the impact of climate change on these relationships and it remains unclear how multiple climatic drivers may affect levels of ecosystem functioning. Here, we examine the roles of two important climate change variables, temperature and concentration of atmospheric carbon dioxide, on the relationship between invertebrate species richness and nutrient release in a model benthic estuarine system. We found a positive relationship between invertebrate species richness and the levels of release of NH4-N into the water column, but no effect of species richness on the release of PO4-P. Higher temperatures and greater concentrations of atmospheric carbon dioxide had a negative impact on nutrient release. Importantly, we found significant interactions between the climate variables, indicating that reliably predicting the effects of future climate change will not be straightforward as multiple drivers are unlikely to have purely additive effects, resulting in increased levels of uncertainty. PMID:20513718

Marine biodiversity-ecosystem functions under uncertain environmental futures.

PubMed

Bulling, Mark T; Hicks, Natalie; Murray, Leigh; Paterson, David M; Raffaelli, Dave; White, Piran C L; Solan, Martin

2010-07-12

Anthropogenic activity is currently leading to dramatic transformations of ecosystems and losses of biodiversity. The recognition that these ecosystems provide services that are essential for human well-being has led to a major interest in the forms of the biodiversity-ecosystem functioning relationship. However, there is a lack of studies examining the impact of climate change on these relationships and it remains unclear how multiple climatic drivers may affect levels of ecosystem functioning. Here, we examine the roles of two important climate change variables, temperature and concentration of atmospheric carbon dioxide, on the relationship between invertebrate species richness and nutrient release in a model benthic estuarine system. We found a positive relationship between invertebrate species richness and the levels of release of NH(4)-N into the water column, but no effect of species richness on the release of PO(4)-P. Higher temperatures and greater concentrations of atmospheric carbon dioxide had a negative impact on nutrient release. Importantly, we found significant interactions between the climate variables, indicating that reliably predicting the effects of future climate change will not be straightforward as multiple drivers are unlikely to have purely additive effects, resulting in increased levels of uncertainty.

Joint measurements of black carbon and particle mass for heavy-duty diesel vehicles using a portable emission measurement system

EPA Science Inventory

The black carbon (BC) emitted from heavy-duty diesel vehicles(HDDVs) is an important source of urban atmospheric pollution and createsstrong climate-forcing impacts. The emission ratio of BC to totalparticle mass (PM) (i.e., BC/PM ratio) is an essential variable used toestimate t...

Application of scenario-neutral methods to quantify impacts of climate change on water resources in East Africa

NASA Astrophysics Data System (ADS)

Ascott, M.; Macdonald, D.; Lapworth, D.; Tindimugaya, C.

2017-12-01

Quantification of the impact of climate change on water resources is essential for future resource planning. Unfortunately, climate change impact studies in African regions are often hindered by the extent in variability in future rainfall predictions, which also diverge from current drying trends. To overcome this limitation, "scenario-neutral" methods have been developed which stress a hydrological system using a wide range of climate futures to build a "climate response surface". We developed a hydrological model and scenario-neutral framework to quantify climate change impacts on river flows in the Katonga catchment, Uganda. Using the lumped catchment model GR4J, an acceptable calibration to historic daily flows (1966 - 2010, NSE = 0.69) was achieved. Using a delta change approach, we then systematically changed rainfall and PET inputs to develop response surfaces for key metrics, developed with Ugandan water resources planners (e.g. Q5, Q95). Scenarios from the CMIP5 models for 2030s and 2050s were then overlain on the response surface. The CMIP5 scenarios show consistent increases in temperature but large variability in rainfall increases, which results in substantial variability in increases in river flows. The developed response surface covers a wide range of climate futures beyond the CMIP5 projections, and can help water resources planners understand the sensitivity of water resource systems to future changes. When future climate scenarios are available, these can be directly overlain on the response surface without the need to re-run the hydrological model. Further work will consider using scenario-neutral approaches in more complex, semi-distributed models (e.g. SWAT), and will consider land use and socioeconomic change.

Impact of transient climate change upon Grouse population dynamics in the Italian Alps

NASA Astrophysics Data System (ADS)

Pirovano, Andrea; Bocchiola, Daniele

2010-05-01

Understanding the effect of short to medium term weather condition, and of transient global warming upon wildlife species life history is essential to predict the demographic consequences therein, and possibly develop adaptation strategies, especially in game species, where hunting mortality may play an important role in population dynamics. We carried out a preliminary investigation of observed impact of weather variables upon population dynamics indexes of three alpine Grouse species (i.e. Rock Ptarmigan, Lagopus Mutus, Black Grouse, Tetrao Tetrix, Rock Partridge, Alectoris Graeca), nested within central Italian Alps, based upon 15 years (1995-2009) of available censuses data, provided by the Sondrio Province authority. We used a set of climate variables already highlighted within recent literature for carrying considerable bearing on Grouse population dynamics, including e.g. temperature at hatching time and during winter, snow cover at nesting, and precipitation during nursing period. We then developed models of Grouses' population dynamics by explicitly driving population change according to their dependence upon the significant weather variables and population density and we evaluated objective indexes to assess the so obtained predictive power. Eventually, we develop projection of future local climate, based upon locally derived trends, and upon projections from GCMs (A2 IPCC storyline) already validated for the area, to project forward in time (until 2100 or so) the significant climatic variables, which we then use to force population dynamics models of the target species. The projected patterns obtained through this exercise are discussed and compared against those expected under stationary climate conditions at present, and preliminary conclusions are drawn.

Satellite-based trends of solar radiation and cloud parameters in Europe

NASA Astrophysics Data System (ADS)

Pfeifroth, Uwe; Bojanowski, Jedrzej S.; Clerbaux, Nicolas; Manara, Veronica; Sanchez-Lorenzo, Arturo; Trentmann, Jörg; Walawender, Jakub P.; Hollmann, Rainer

2018-04-01

Solar radiation is the main driver of the Earth's climate. Measuring solar radiation and analysing its interaction with clouds are essential for the understanding of the climate system. The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates satellite-based, high-quality climate data records, with a focus on the energy balance and water cycle. Here, multiple of these data records are analyzed in a common framework to assess the consistency in trends and spatio-temporal variability of surface solar radiation, top-of-atmosphere reflected solar radiation and cloud fraction. This multi-parameter analysis focuses on Europe and covers the time period from 1992 to 2015. A high correlation between these three variables has been found over Europe. An overall consistency of the climate data records reveals an increase of surface solar radiation and a decrease in top-of-atmosphere reflected radiation. In addition, those trends are confirmed by negative trends in cloud cover. This consistency documents the high quality and stability of the CM SAF climate data records, which are mostly derived independently from each other. The results of this study indicate that one of the main reasons for the positive trend in surface solar radiation since the 1990's is a decrease in cloud coverage even if an aerosol contribution cannot be completely ruled out.

Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers.

PubMed

Axelsen, Jacob Bock; Yaari, Rami; Grenfell, Bryan T; Stone, Lewi

2014-07-01

Human influenza occurs annually in most temperate climatic zones of the world, with epidemics peaking in the cold winter months. Considerable debate surrounds the relative role of epidemic dynamics, viral evolution, and climatic drivers in driving year-to-year variability of outbreaks. The ultimate test of understanding is prediction; however, existing influenza models rarely forecast beyond a single year at best. Here, we use a simple epidemiological model to reveal multiannual predictability based on high-quality influenza surveillance data for Israel; the model fit is corroborated by simple metapopulation comparisons within Israel. Successful forecasts are driven by temperature, humidity, antigenic drift, and immunity loss. Essentially, influenza dynamics are a balance between large perturbations following significant antigenic jumps, interspersed with nonlinear epidemic dynamics tuned by climatic forcing.

Achieving Climate Change Absolute Accuracy in Orbit

NASA Technical Reports Server (NTRS)

Wielicki, Bruce A.; Young, D. F.; Mlynczak, M. G.; Thome, K. J; Leroy, S.; Corliss, J.; Anderson, J. G.; Ao, C. O.; Bantges, R.; Best, F.;

Communicating the Results and Activities of the U.S. Climate Change Science Program

NASA Astrophysics Data System (ADS)

Chatterjee, K.; Parker, K.

2004-12-01

The Climate Change Science Program (CCSP) has a responsibility for credible and effective communications on issues related to climate variability and climate change science. As an essential part of its mission and responsibilities, the CCSP aims to enhance the quality of public discussion by stressing openness and transparency in its scientific research processes and results, and ensuring the widespread availability of credible, science-based information. The CCSP and individual federal agencies generate substantial amounts of authoritative scientific information on climate variability and change. Research findings are generally well reported in the scientific literature, but relevant aspects of these findings need to be reported in formats suitable for use by diverse audiences whose understanding and familiarity with climate change science issues vary. To further its commitment to the effective communication of climate change science information, the CCSP has established the Communications Interagency Working Group, which has produced an implementation plan for Climate Change communication, aimed at achieving the following goals: * Disseminate the results of CCSP activities credibly and effectively * Make CCSP science findings and products easily available to a diverse set of audiences. In addition to CCSP efforts, the individual federal agencies that comprise CCSP disseminate science-based climate information through their agency networks. The agencies of the CCSP are the Departments of Agriculture, Commerce, Defense, Energy, Health and Human Services, Interior, State, and Transportation and the U.S. EPA, NASA, NSF, Smithsonian Institute, and USAID.

Response of lake chemistry to atmospheric deposition and climate in selected Class I wilderness areas in the western United States, 1993-2009

USGS Publications Warehouse

Mast, M. Alisa

2011-01-01

The U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Forest Service, Air Resource Management, conducted a study to evaluate long-term trends in lake-water chemistry for 64 high-elevation lakes in selected Class I wilderness areas in Colorado, Idaho, Utah, and Wyoming during 1993 to 2009. Understanding how and why lake chemistry is changing in mountain areas is essential for effectively managing and protecting high-elevation aquatic ecosystems. Trends in emissions, atmospheric deposition, and climate variables (air temperature and precipitation amount) were evaluated over a similar period of record. A main objective of the study was to determine if changes in atmospheric deposition of contaminants in the Rocky Mountain region have resulted in measurable changes in the chemistry of high-elevation lakes. A second objective was to investigate linkages between lake chemistry and air temperature and precipitation to improve understanding of the sensitivity of mountain lakes to climate variability.

Collaboration pathway(s) using new tools for optimizing operational climate monitoring from space

NASA Astrophysics Data System (ADS)

Helmuth, Douglas B.; Selva, Daniel; Dwyer, Morgan M.

2014-10-01

Consistently collecting the earth's climate signatures remains a priority for world governments and international scientific organizations. Architecting a solution requires transforming scientific missions into an optimized robust `operational' constellation that addresses the needs of decision makers, scientific investigators and global users for trusted data. The application of new tools offers pathways for global architecture collaboration. Recent (2014) rulebased decision engine modeling runs that targeted optimizing the intended NPOESS architecture, becomes a surrogate for global operational climate monitoring architecture(s). This rule-based systems tools provide valuable insight for Global climate architectures, through the comparison and evaluation of alternatives considered and the exhaustive range of trade space explored. A representative optimization of Global ECV's (essential climate variables) climate monitoring architecture(s) is explored and described in some detail with thoughts on appropriate rule-based valuations. The optimization tools(s) suggest and support global collaboration pathways and hopefully elicit responses from the audience and climate science shareholders.

Current status of the Essential Variables as an instrument to assess the Earth Observation Networks in Europe

NASA Astrophysics Data System (ADS)

Blonda, Palma; Maso, Joan; Bombelli, Antonio; Plag, Hans Peter; McCallum, Ian; Serral, Ivette; Nativi, Stefano Stefano

2016-04-01

ConnectinGEO (Coordinating an Observation Network of Networks EnCompassing saTellite and IN-situ to fill the Gaps in European Observations" is an H2020 Coordination and Support Action with the primary goal of linking existing Earth Observation networks with science and technology (S&T) communities, the industry sector, the Group on Earth Observations (GEO), and Copernicus. The project will end in February 2017. Essential Variables (EVs) are defined by ConnectinGEO as "a minimal set of variables that determine the system's state and developments, are crucial for predicting system developments, and allow us to define metrics that measure the trajectory of the system". . Specific application-dependent characteristics, such as spatial and temporal resolution of observations and data quality thresholds, are not generally included in the EV definition. This definition and the present status of EV developments in different societal benefit areas was elaborated at the ConnectinGEO workshop "Towards a sustainability process for GEOSS Essential Variables (EVs)," which was held in Bari on June 11-12, 2015 (http://www.gstss.org/2015_Bari/). Presentations and reports contributed by a wide range of communities provided important inputs from different sectors for assessing the status of the EV development. In most thematic areas, the development of sets of EVs is a community process leading to an agreement on what is essential for the goals of the community. While there are many differences across the communities in the details of the criteria, methodologies and processes used to develop sets of EVs, there is also a considerable common core across the communities, particularly those with a more advanced discussion. In particular, there is some level of overlap in different topics (e.g., Climate and Water), and there is a potential to develop an integrated set of EVs common to several thematic areas as well as specific ones that satisfy only one community. The thematic areas with a more mature development of EV lists are Climate (ECV), Ocean (EOV) and Biodiversity (EBV). Water is also developing a set of EVs in GEOSS. Agriculture is working with a common set of variables that can be considered essential to them such as Crop Area, Crop Type, Crop Condition, etc.. More work is required for an agreement on other sets of EVs for Disasters, Health and Ecosystems. Being cross-domain topics, these areas can make use of existing sets of EVs (such as ECVs, EOVs and EBVs) complemented by socioeconomic variables that can help to characterize services (e.g., ecosystem services) to human societies. Renewable energy also makes use of the ECVs but there is a need for additional ones: solar surface irradiance and wind at different heights are good candidates to explore. ConnectinGEO will link with Climate, Ocean and Biodiversity communities and support Water, Agriculture, Renewable Energy, Health, Disasters and Ecosystems to make progress in the EV development by stimulating the debate in their respective international forums (mainly in GEOSS). The final objective is to foster EV extraction from the integrated use of in-situ and satellite Earth Observation data.

Characterizing hydroclimatic variability in tributaries of the Upper Colorado River Basin—WY1911-2001

NASA Astrophysics Data System (ADS)

Matter, Margaret A.; Garcia, Luis A.; Fontane, Darrell G.; Bledsoe, Brian

2010-01-01

SummaryMountain snowpack is the main source of water in the semi-arid Colorado River Basin (CRB), and while the demands for water are increasing, competing and often conflicting, the supply is limited and has become increasingly variable over the 20th Century. Greater variability is believed to contribute to lower accuracy in water supply forecasts, plus greater variability violates the assumption of stationarity, a fundamental assumption of many methods used in water resources engineering planning, design and management. Thus, it is essential to understand the underpinnings of hydroclimatic variability in order to accurately predict effects of climate changes and effectively meet future water supply challenges. A new methodology was applied to characterized time series of temperature, precipitation, and streamflow (i.e., historic and reconstructed undepleted flows) according to the three climate regimes that occurred in CRB during the 20th Century. Results for two tributaries in the Upper CRB show that hydroclimatic variability is more deterministic than previously thought because it entails complementary temperature and precipitation patterns associated with wetter or drier conditions on climate regime and annual scales. Complementary temperature and precipitation patterns characterize climate regime type (e.g., cool/wet and warm/dry), and the patterns entail increasing or decreasing temperatures and changes in magnitude and timing of precipitation according to the climate regime type. Accompanying each climate regime on annual scales are complementary temperature ( T) and precipitation ( P) patterns that are associated with upcoming precipitation and annual basin yield (i.e., total annual flow volume at a streamflow gauge). Annual complementary T and P patterns establish by fall, are detectable as early as September, persist to early spring, are related to the relative magnitude of upcoming precipitation and annual basin yield, are unique to climate regime type, and are specific to each river basin. Thus, while most of the water supply in the Upper CRB originates from winter snowpack, statistically significant indictors of relative magnitude of upcoming precipitation and runoff are evident in the fall, well before appreciable snow accumulation. Results of this study suggest strategies that may integrated into existing forecast methods to potentially improve forecast accuracy and advance lead time by as much as six months (i.e., from April 1 to October 1 of the previous year). These techniques also have applications in downscaling climate models and in river restoration and management.

Varying geospatial analyses to assess climate risk and adaptive capacity in a hotter, drier Southwestern United States

NASA Astrophysics Data System (ADS)

Elias, E.; Reyes, J. J.; Steele, C. M.; Rango, A.

2017-12-01

Assessing vulnerability of agricultural systems to climate variability and change is vital in securing food systems and sustaining rural livelihoods. Farmers, ranchers, and forest landowners rely on science-based, decision-relevant, and localized information to maintain production, ecological viability, and economic returns. This contribution synthesizes a collection of research on the future of agricultural production in the American Southwest (SW). Research was based on a variety of geospatial methodologies and datasets to assess the vulnerability of rangelands and livestock, field crops, specialty crops, and forests in the SW to climate-risk and change. This collection emerged from the development of regional vulnerability assessments for agricultural climate-risk by the U.S. Department of Agriculture (USDA) Climate Hub Network, established to deliver science-based information and technologies to enable climate-informed decision-making. Authors defined vulnerability differently based on their agricultural system of interest, although each primarily focuses on biophysical systems. We found that an inconsistent framework for vulnerability and climate risk was necessary to adequately capture the diversity, variability, and heterogeneity of SW landscapes, peoples, and agriculture. Through the diversity of research questions and methodologies, this collection of articles provides valuable information on various aspects of SW vulnerability. All articles relied on geographic information systems technology, with highly variable levels of complexity. Agricultural articles used National Agricultural Statistics Service data, either as tabular county level summaries or through the CropScape cropland raster datasets. Most relied on modeled historic and future climate information, but with differing assumptions regarding spatial resolution and temporal framework. We assert that it is essential to evaluate climate risk using a variety of complementary methodologies and perspectives. In addition, we found that spatial analysis supports informed adaptation, within and outside the SW United States. The persistence and adaptive capacity of agriculture in the water-limited Southwest serves as an instructive example and may offer solutions to reduce future climate risk.

The Copernicus Climate Change Service (C3S): A European Answer to Climate Change

NASA Astrophysics Data System (ADS)

Thepaut, Jean-Noel

2016-04-01

Copernicus is the European Commission's flagship Earth observation programme that delivers freely accessible operational data and information services. ECMWF has been entrusted to operate two key parts of the Copernicus programme, which will bring a consistent standard to the measurement, forecasting and prediction of atmospheric conditions and climate change: • The Copernicus Atmosphere Monitoring Service, CAMS, provides daily forecasts detailing the makeup composition of the atmosphere from the ground up to the stratosphere. • The Copernicus Climate Change Service (C3S) (in development) will routinely monitor and analyse more than 20 essential climate variables to build a global picture of our climate, from the past to the future, as well as developing customisable climate indicators for relevant economic sectors, such as energy, water management, agriculture, insurance, health…. C3S has now taken off and a number of proof-of-concept sectoral climate services have been initiated. This paper will focus on the description and expected outcome of these proof-of-concept activities as well as the definition of a roadmap towards a fully operational European Climate Change Service.

Uncertainty information in climate data records from Earth observation

NASA Astrophysics Data System (ADS)

Merchant, C. J.

2017-12-01

How to derive and present uncertainty in climate data records (CDRs) has been debated within the European Space Agency Climate Change Initiative, in search of common principles applicable across a range of essential climate variables. Various points of consensus have been reached, including the importance of improving provision of uncertainty information and the benefit of adopting international norms of metrology for language around the distinct concepts of uncertainty and error. Providing an estimate of standard uncertainty per datum (or the means to readily calculate it) emerged as baseline good practice, and should be highly relevant to users of CDRs when the uncertainty in data is variable (the usual case). Given this baseline, the role of quality flags is clarified as being complementary to and not repetitive of uncertainty information. Data with high uncertainty are not poor quality if a valid estimate of the uncertainty is available. For CDRs and their applications, the error correlation properties across spatio-temporal scales present important challenges that are not fully solved. Error effects that are negligible in the uncertainty of a single pixel may dominate uncertainty in the large-scale and long-term. A further principle is that uncertainty estimates should themselves be validated. The concepts of estimating and propagating uncertainty are generally acknowledged in geophysical sciences, but less widely practised in Earth observation and development of CDRs. Uncertainty in a CDR depends in part (and usually significantly) on the error covariance of the radiances and auxiliary data used in the retrieval. Typically, error covariance information is not available in the fundamental CDR (FCDR) (i.e., with the level-1 radiances), since provision of adequate level-1 uncertainty information is not yet standard practice. Those deriving CDRs thus cannot propagate the radiance uncertainty to their geophysical products. The FIDUCEO project (www.fiduceo.eu) is demonstrating metrologically sound methodologies addressing this problem for four key historical CDRs. FIDUCEO methods of uncertainty analysis (which also tend to lead to improved FCDRs and CDRs) could support coherent treatment of uncertainty across FCDRs to CDRs and higher level products for a wide range of essential climate variables.

Unravelling connections between river flow and large-scale climate: experiences from Europe

NASA Astrophysics Data System (ADS)

Hannah, D. M.; Kingston, D. G.; Lavers, D.; Stagge, J. H.; Tallaksen, L. M.

2016-12-01

The United Nations has identified better knowledge of large-scale water cycle processes as essential for socio-economic development and global water-food-energy security. In this context, and given the ever-growing concerns about climate change/ variability and human impacts on hydrology, there is an urgent research need: (a) to quantify space-time variability in regional river flow, and (b) to improve hydroclimatological understanding of climate-flow connections as a basis for identifying current and future water-related issues. In this paper, we draw together studies undertaken at the pan-European scale: (1) to evaluate current methods for assessing space-time dynamics for different streamflow metrics (annual regimes, low flows and high flows) and for linking flow variability to atmospheric drivers (circulation indices, air-masses, gridded climate fields and vapour flux); and (2) to propose a plan for future research connecting streamflow and the atmospheric conditions in Europe and elsewhere. We believe this research makes a useful, unique contribution to the literature through a systematic inter-comparison of different streamflow metrics and atmospheric descriptors. In our findings, we highlight the need to consider appropriate atmospheric descriptors (dependent on the target flow metric and region of interest) and to develop analytical techniques that best characterise connections in the ocean-atmosphere-land surface process chain. We call for the need to consider not only atmospheric interactions, but also the role of the river basin-scale terrestrial hydrological processes in modifying the climate signal response of river flows.

A first-order global model of Late Cenozoic climatic change: Orbital forcing as a pacemaker of the ice ages

NASA Technical Reports Server (NTRS)

Saltzman, Barry

1992-01-01

The development of a theory of the evolution of the climate of the earth over millions of years can be subdivided into three fundamental, nested, problems: (1) to establish by equilibrium climate models (e.g., general circulation models) the diagnostic relations, valid at any time, between the fast-response climate variables (i.e., the 'weather statistics') and both the prescribed external radiative forcing and the prescribed distribution of the slow response variables (e.g., the ice sheets and shelves, the deep ocean state, and the atmospheric CO2 concentration); (2) to construct, by an essentially inductive process, a model of the time-dependent evolution of the slow-response climatic variables over time scales longer than the damping times of these variables but shorter than the time scale of tectonic changes in the boundary conditions (e.g., altered geography and elevation of the continents, slow outgassing, and weathering) and ultra-slow astronomical changes such as in the solar radiative output; and (3) to determine the nature of these ultra-slow processes and their effects on the evolution of the equilibrium state of the climatic system about which the above time-dependent variations occur. All three problems are discussed in the context of the theory of the Quaternary climate, which will be incomplete unless it is embedded in a more general theory for the fuller Cenozoic that can accommodate the onset of the ice-age fluctuations. We construct a simple mathematical model for the Late Cenozoic climatic changes based on the hypothesis that forced and free variations of the concentration of atmospheric greenhouse gases (notably CO2), coupled with changes in the deep ocean state and ice mass, under the additional 'pacemaking' influence of earth-orbital forcing, are primary determinants of the climate state over this period. Our goal is to illustrate how a single model governing both very long term variations and higher frequency oscillatory variations in the Pleistocene can be formulated with relatively few adjustable parameters.

Oscar: a portable prototype system for the study of climate variability

NASA Astrophysics Data System (ADS)

Madonna, Fabio; Rosoldi, Marco; Amato, Francesco

2015-04-01

The study of the techniques for the exploitation of solar energy implies the knowledge of nature, ecosystem, biological factors and local climate. Clouds, fog, water vapor, and the presence of large concentrations of dust can significantly affect the way to exploit the solar energy. Therefore, a quantitative characterization of the impact of climate variability at the regional scale is needed to increase the efficiency and sustainability of the energy system. OSCAR (Observation System for Climate Application at Regional scale) project, funded in the frame of the PO FESR 2007-2013, aims at the design of a portable prototype system for the study of correlations among the trends of several Essential Climate Variables (ECVs) and the change in the amount of solar irradiance at the ground level. The final goal of this project is to provide a user-friendly low cost solution for the quantification of the impact of regional climate variability on the efficiency of solar cell and concentrators to improve the exploitation of natural sources. The prototype has been designed on the basis of historical measurements performed at CNR-IMAA Atmospheric Observatory (CIAO). Measurements from satellite and data from models have been also considered as ancillary to the study, above all, to fill in the gaps of existing datasets. In this work, the results outcome from the project activities will be presented. The results include: the design and implementation of the prototype system; the development of a methodology for the estimation of the impact of climate variability, mainly due to aerosol, cloud and water vapor, on the solar irradiance using the integration of the observations potentially provided by prototype; the study of correlation between the surface radiation, precipitation and aerosols transport. In particular, a statistical study will be presented to assess the impact of the atmosphere on the solar irradiance at the ground, quantifying the contribution due to aerosol and clouds and separating their effect on the direct and the diffuse components of the solar radiation. This also aims to provide recommendations to the manufacturer of the devices used to exploit solar radiation.

Identification of reliable gridded reference data for statistical downscaling methods in Alberta

NASA Astrophysics Data System (ADS)

Eum, H. I.; Gupta, A.

2017-12-01

Climate models provide essential information to assess impacts of climate change at regional and global scales. However, statistical downscaling methods have been applied to prepare climate model data for various applications such as hydrologic and ecologic modelling at a watershed scale. As the reliability and (spatial and temporal) resolution of statistically downscaled climate data mainly depend on a reference data, identifying the most reliable reference data is crucial for statistical downscaling. A growing number of gridded climate products are available for key climate variables which are main input data to regional modelling systems. However, inconsistencies in these climate products, for example, different combinations of climate variables, varying data domains and data lengths and data accuracy varying with physiographic characteristics of the landscape, have caused significant challenges in selecting the most suitable reference climate data for various environmental studies and modelling. Employing various observation-based daily gridded climate products available in public domain, i.e. thin plate spline regression products (ANUSPLIN and TPS), inverse distance method (Alberta Townships), and numerical climate model (North American Regional Reanalysis) and an optimum interpolation technique (Canadian Precipitation Analysis), this study evaluates the accuracy of the climate products at each grid point by comparing with the Adjusted and Homogenized Canadian Climate Data (AHCCD) observations for precipitation, minimum and maximum temperature over the province of Alberta. Based on the performance of climate products at AHCCD stations, we ranked the reliability of these publically available climate products corresponding to the elevations of stations discretized into several classes. According to the rank of climate products for each elevation class, we identified the most reliable climate products based on the elevation of target points. A web-based system was developed to allow users to easily select the most reliable reference climate data at each target point based on the elevation of grid cell. By constructing the best combination of reference data for the study domain, the accurate and reliable statistically downscaled climate projections could be significantly improved.

Harmonising and semantically linking key variables from in-situ observing networks of an Integrated Atlantic Ocean Observing System, AtlantOS

NASA Astrophysics Data System (ADS)

Darroch, Louise; Buck, Justin

2017-04-01

Atlantic Ocean observation is currently undertaken through loosely-coordinated, in-situ observing networks, satellite observations and data management arrangements at regional, national and international scales. The EU Horizon 2020 AtlantOS project aims to deliver an advanced framework for the development of an Integrated Atlantic Ocean Observing System that strengthens the Global Ocean Observing System (GOOS) and contributes to the aims of the Galway Statement on Atlantic Ocean Cooperation. One goal is to ensure that data from different and diverse in-situ observing networks are readily accessible and useable to a wider community, including the international ocean science community and other stakeholders in this field. To help achieve this goal, the British Oceanographic Data Centre (BODC) produced a parameter matrix to harmonise data exchange, data flow and data integration for the key variables acquired by multiple in-situ AtlantOS observing networks such as ARGO, Seafloor Mapping and OceanSITES. Our solution used semantic linking of controlled vocabularies and metadata for parameters that were "mappable" to existing EU and international standard vocabularies. An AtlantOS Essential Variables list of terms (aggregated level) based on Global Climate Observing System (GCOS) Essential Climate Variables (ECV), GOOS Essential Ocean Variables (EOV) and other key network variables was defined and published on the Natural Environment Research Council (NERC) Vocabulary Server (version 2.0) as collection A05 (http://vocab.nerc.ac.uk/collection/A05/current/). This new vocabulary was semantically linked to standardised metadata for observed properties and units that had been validated by the AtlantOS community: SeaDataNet parameters (P01), Climate and Forecast (CF) Standard Names (P07) and SeaDataNet units (P06). Observed properties were mapped to biological entities from the internationally assured AphiaID from the WOrld Register of Marine Species (WoRMS), http://www.marinespecies.org/aphia.php?p=webservice. The AtlantOS parameter matrix offers a way to harmonise the globally important variables (such as ECVs and EOVs) from in-situ observing networks that use different flavours of exchange formats based on SeaDataNet and CF parameter metadata. It also offers a way to standardise data in the wider Integrated Ocean Observing System. It uses sustainable and trusted standardised vocabularies that are governed by internationally renowned and long-standing organisations and is interoperable through the use of persistent resource identifiers, such as URNs and PURLs. It is the first step to integrating and serving data in a variety of international exchange formats using Application programming interfaces (API) improving both data discoverability and utility for users.

Early Holocene hydroclimate of Baffin Bay: Understanding the interplay between abrupt climate change events and ice sheet fluctuations

NASA Astrophysics Data System (ADS)

Corcoran, M. C.; Thomas, E. K.; Castañeda, I. S.; Briner, J. P.

2017-12-01

Understanding the causes of ice sheet fluctuations resulting in sea level rise is essential in today's warming climate. In high-latitude ice-sheet-proximal environments such as Baffin Bay, studying both the cause and the rate of ice sheet variability during past abrupt climate change events aids in predictions. Past climate reconstructions are used to understand ice sheet responses to changes in temperature and precipitation. The 9,300 and 8,200 yr BP events are examples of abrupt climate change events in the Baffin Bay region during which there were multiple re-advances of the Greenland and Laurentide ice sheets. High-resolution (decadal-scale) hydroclimate variability near the ice sheet margins during these abrupt climate change events is still unknown. We will generate a decadal-scale record of early Holocene temperature and precipitation using leaf wax hydrogen isotopes, δ2Hwax, from a lake sediment archive on Baffin Island, western Baffin Bay, to better understand abrupt climate change in this region. Shifts in temperature and moisture source result in changes in environmental water δ2H, which in turn is reflected in δ2Hwax, allowing for past hydroclimate to be determined from these compound-specific isotopes. The combination of terrestrial and aquatic δ2Hwax is used to determine soil evaporation and is ultimately used to reconstruct moisture variability. We will compare our results with a previous analysis of δ2Hwax and branched glycerol dialkyl glycerol tetraethers, a temperature and pH proxy, in lake sediment from western Greenland, eastern Baffin Bay, which indicates that cool and dry climate occurred in response to freshwater forcing events in the Labrador Sea. Reconstructing and comparing records on both the western and eastern sides of Baffin Bay during the early Holocene will allow for a spatial understanding of temperature and moisture balance changes during abrupt climate events, aiding in ice sheet modeling and predictions of future sea level rise.

State of the Art in Large-Scale Soil Moisture Monitoring

NASA Technical Reports Server (NTRS)

Ochsner, Tyson E.; Cosh, Michael Harold; Cuenca, Richard H.; Dorigo, Wouter; Draper, Clara S.; Hagimoto, Yutaka; Kerr, Yan H.; Larson, Kristine M.; Njoku, Eni Gerald; Small, Eric E.;

Role of vegetation in interplay of climate, soil and groundwater recharge in a global dataset

NASA Astrophysics Data System (ADS)

Kim, J. H.; Jackson, R. B.

2010-12-01

Groundwater is an essential resource for people and ecosystems worldwide. Our capacity to ameliorate predicted global water shortages and to maintain sustainable water supplies depend on a better understanding of the controls of recharge and how vegetation change may affect recharge mechanisms. The goals of this study are to quantify the importance of vegetation as a dominant control on recharge globally and to compare the importance of vegetation with other hydrologically important variables, including climate and soil. We based our global analysis on > 500 recharge estimates from the literature that contained information on vegetation, soil and climate or location. Plant functional types significantly affected groundwater recharge rates substantially. After climatic factors (water inputs, PET, and seasonality), vegetation types explained about 15% of the residuals in the dataset. Across all climatic factors, croplands had the highest recharge rates, followed by grasslands, scrublands and woodlands (average recharge: 75, 63, 30, 22 mm/yr respectively). Recharge under woodlands showed the most nonlinear response to water inputs. Differences in recharge between the vegetation types were more exaggerated at arid climates and in clay soils, indicating greater biological control on soil water fluxes in these conditions. Our results shows that vegetation greatly affects recharge rates globally and alters relationship between recharge and physical variables allowing us to better predict recharge rates globally.

On the reliability of seasonal climate forecasts.

PubMed

Weisheimer, A; Palmer, T N

2014-07-06

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1-5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that 'goodness' should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a '5' should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of 'goodness' rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching '5' across all regions and variables in 30 years time.

Promoting Climate Literacy and Conceptual Understanding among In-service Secondary Science Teachers requires an Epistemological Perspective

NASA Astrophysics Data System (ADS)

Bhattacharya, D.; Forbes, C.; Roehrig, G.; Chandler, M. A.

2017-12-01

Promoting climate literacy among in-service science teachers necessitates an understanding of fundamental concepts about the Earth's climate System (USGCRP, 2009). Very few teachers report having any formal instruction in climate science (Plutzer et al., 2016), therefore, rather simple conceptions of climate systems and their variability exist, which has implications for students' science learning (Francies et al., 1993; Libarkin, 2005; Rebich, 2005). This study uses the inferences from a NASA Innovations in Climate Education (NICE) teacher professional development program (CYCLES) to establish the necessity for developing an epistemological perspective among teachers. In CYCLES, 19 middle and high school (male=8, female=11) teachers were assessed for their understanding of global climate change (GCC). A qualitative analysis of their concept maps and an alignment of their conceptions with the Essential Principles of Climate Literacy (NOAA, 2009) demonstrated that participants emphasized on EPCL 1, 3, 6, 7 focusing on the Earth system, atmospheric, social and ecological impacts of GCC. However, EPCL 4 (variability in climate) and 5 (data-based observations and modeling) were least represented and emphasized upon. Thus, participants' descriptions about global climatic patterns were often factual rather than incorporating causation (why the temperatures are increasing) and/or correlation (describing what other factors might influence global temperatures). Therefore, engaging with epistemic dimensions of climate science to understand the processes, tools, and norms through which climate scientists study the Earth's climate system (Huxter et al., 2013) is critical for developing an in-depth conceptual understanding of climate. CLiMES (Climate Modeling and Epistemology of Science), a NSF initiative proposes to use EzGCM (EzGlobal Climate Model) to engage students and teachers in designing and running simulations, performing data processing activities, and analyzing computational models to develop their own evidence-based claims about the Earth's climate system. We describe how epistemological investigations can be conducted using EzGCM to bring the scientific process and authentic climate science practice to middle and high school classrooms.

Burned areas for the conterminous U.S. from 1984 through 2015, an automated approach using dense time-series of Landsat data

NASA Astrophysics Data System (ADS)

Hawbaker, T. J.; Vanderhoof, M.; Beal, Y. J. G.; Takacs, J. D.; Schmidt, G.; Falgout, J.; Brunner, N. M.; Caldwell, M. K.; Picotte, J. J.; Howard, S. M.; Stitt, S.; Dwyer, J. L.

2016-12-01

Complete and accurate burned area data are needed to document patterns of fires, to quantify relationships between the patterns and drivers of fire occurrence, and to assess the impacts of fires on human and natural systems. Unfortunately, many existing fire datasets in the United States are known to be incomplete and that complicates efforts to understand burned area patterns and introduces a large amount of uncertainty in efforts to identify their driving processes and impacts. Because of this, the need to systematically collect burned area information has been recognized by the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change, which have both called for the production of essential climate variables. To help meet this need, we developed a novel algorithm that automatically identifies burned areas in temporally-dense time series of Landsat image stacks to produce Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm makes use of predictors derived from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference predictors. Outputs of the BAECV algorithm, generated for the conterminous United States for 1984 through 2015, consist of burn probabilities for each Landsat scene, in addition to, annual composites including: the maximum burn probability, burn classification, and the Julian date of the first Landsat scene a burn was observed. The BAECV products document patterns of fire occurrence that are not well characterized by existing fire datasets in the United States. We anticipate that these data could help to better understand past patterns of fire occurrence, the drivers that created them, and the impacts fires had on natural and human systems.

Operational Production of the Total Ozone Essential Climate Variable as Part of the Copernicus Climate Change Service (C3S)

NASA Astrophysics Data System (ADS)

Lerot, C.; Danckaert, T.; van Gent, J.; Coldewey-Egbers, M.; Loyola, D. G.; Errera, Q.; Spurr, R. J. D.; Garane, K.; Koukouli, M.; Balis, D.; Verhoelst, T.; Granville, J.; Lambert, J. C.; Van Roozendael, M.

2017-12-01

Total ozone is one of the Essential Climate Variables (ECV) operationally produced within the European Copernicus Climate Change Service (C3S), which aims at providing the geophysical information needed to monitor and study our climate system. The C3S total ozone processing chain relies on algorithmic developments realized for the last six years as part of the ESA's Ozone Climate Change Initiative (Ozone_cci) project. The C3S Climate Data Store currently contains a total ozone record based on observations from the nadir UV-Vis hyperspectral spectrometers GOME/ERS-2, SCIAMACHY/Envisat, GOME-2/Metop-A, GOME-2/Metop-B and OMI/Aura, spanning more than 23 years.Individual level-2 datasets were generated with the retrieval algorithm GODFIT (GOME-type Direct FITting). The retrievals are based on a non-linear least squares adjustment of reflectances simulated with radiative transfer tools from the LIDORT suite, to the measured spectra in the Huggins bands (325-335 nm). The inter-sensor consistency and the time stability of those data sets is significantly enhanced with the application of a soft-calibration procedure to the level-1 reflectances, in which GOME and OMI are used together as a long-term reference. Level-2 data sets are then combined to produce the level-3 GOME-type Total Ozone (GTO-ECV) record consisting of homogenized 1°x1° monthly mean grids. The merging procedure corrects for subsisting inter-satellite biases and temporal drifts. Some developments for minimizing sampling errors have also been recently investigated and will be discussed. Total ozone level-2 and level-3 data sets are regularly verified and validated by independent measurements both from space (independent algorithms and/or instruments) and ground (Brewer/Dobson/SAOZ) and their excellent quality and stability, as well as their consistency with other long-term total ozone data sets will be illustrated here. In future, in addition to be continuously extended in time, the C3S total ozone record will also incorporate new sensors such as OMPS aboard Suomi NPP or TROPOMI/S5p.

Sources of global climate data and visualization portals

USGS Publications Warehouse

Douglas, David C.

2014-01-01

Climate is integral to the geophysical foundation upon which ecosystems are structured. Knowledge about mechanistic linkages between the geophysical and biological environments is essential for understanding how global warming may reshape contemporary ecosystems and ecosystem services. Numerous global data sources spanning several decades are available that document key geophysical metrics such as temperature and precipitation, and metrics of primary biological production such as vegetation phenology and ocean phytoplankton. This paper provides an internet directory to portals for visualizing or servers for downloading many of the more commonly used global datasets, as well as a description of how to write simple computer code to efficiently retrieve these data. The data are broadly useful for quantifying relationships between climate, habitat availability, and lower-trophic-level habitat quality - especially in Arctic regions where strong seasonality is accompanied by intrinsically high year-to-year variability. If defensible linkages between the geophysical (climate) and the biological environment can be established, general circulation model (GCM) projections of future climate conditions can be used to infer future biological responses. Robustness of this approach is, however, complicated by the number of direct, indirect, or interacting linkages involved. For example, response of a predator species to climate change will be influenced by the responses of its prey and competitors, and so forth throughout a trophic web. The complexities of ecological systems warrant sensible and parsimonious approaches for assessing and establishing the role of natural climate variability in order to substantiate inferences about the potential effects of global warming.

Impacts of weather on long-term patterns of plant richness and diversity vary with location and management

USGS Publications Warehouse

Jonas, Jayne L.; Buhl, Deborah A.; Symstad, Amy J.

2015-01-01

Better understanding the influence of precipitation and temperature on plant assemblages is needed to predict the effects of climate change. Many studies have examined the relationship between plant productivity and weather (primarily precipitation), but few have directly assessed the relationship between plant richness or diversity and weather despite their increased use as metrics of ecosystem condition. We focus on the grasslands of central North America, which are characterized by high temporal climatic variability. Over the next 100 years, these grasslands are predicted to experience further increased variability in growing season precipitation, as well as increased temperatures, due to global climate change. We assess 1) the portion of interannual variability of richness and diversity explained by weather, 2) how relationships between these metrics and weather vary among plant assemblages, and 3) which aspects of weather best explain temporal variability. We used an information-theoretic approach to assess relationships between long-term plant richness and diversity patterns and a priori weather covariates using six datasets from four grasslands. Weather explained up to 49% and 63% of interannual variability in total plant species richness and diversity, respectively. However, richness and diversity responses to specific weather variables varied both among sites and among experimental treatments within sites. In general, we found many instances in which temperature was of equal or greater importance as precipitation, as well as evidence of the importance of lagged effects and precipitation or temperature variability. Although precipitation has been shown to be a key driver of productivity in grasslands, our results indicate that increasing temperatures alone, without substantial changes in precipitation patterns, could have measurable effects on Great Plains grassland plant assemblages and biodiversity metrics. Our results also suggest that richness and diversity will respond in unique ways to changing climate and management can affect these responses; additional research and monitoring will be essential for further understanding of these complex relationships.Read More: http://www.esajournals.org/doi/abs/10.1890/14-1989.1

Impacts of weather on long-term patterns of plant richness and diversity vary with location and management.

PubMed

Jonas, Jayne L; Buhl, Deborah A; Symstad, Amy J

2015-09-01

Better understanding the influence of precipitation and temperature on plant assemblages is needed to predict the effects of climate change. Many studies have examined the relationship between plant productivity and weather (primarily precipitation), but few have directly assessed the relationship between plant richness or diversity and weather despite their increased use as metrics of ecosystem condition. We focus on the grasslands of central North America, which are characterized by high temporal climatic variability. Over the next 100 years, these grasslands are predicted to experience further increased variability in growing season precipitation, as well as increased temperatures, due to global climate change. We assess the portion of interannual variability of richness and diversity explained by weather, how relationships between these metrics and weather vary among plant assemblages, and which aspects of weather best explain temporal variability. We used an information-theoretic approach to assess relationships between long-term plant richness and diversity patterns and a priori weather covariates using six data sets from four grasslands. Weather explained up to 49% and 63% of interannual variability in total plant species richness and diversity, respectively. However, richness and diversity responses to specific weather variables varied both among sites and among experimental treatments within sites. In general, we found many instances in which temperature was of equal or greater importance as precipitation, as well as evidence of the importance of lagged effects and precipitation or temperature variability. Although precipitation has been shown to be a key driver of productivity in grasslands, our results indicate that increasing temperatures alone, without substantial changes in precipitation patterns, could have measurable effects on Great Plains grassland plant assemblages and biodiversity metrics. Our results also suggest that richness and diversity will respond in unique ways to changing climate and management can affect these responses; additional research and monitoring will be essential for further understanding of these complex relationships.

Reconstructing coastal environmental condition in the eastern Norwegian Sea by means of Arctica islandica sclerochronological records

NASA Astrophysics Data System (ADS)

Trofimova, Tamara; Andersson, Carin

2015-04-01

Paleo archives are fundament in improving our knowledge of the natural climate variability. Established marine proxy records for the ocean, especially for high latitudes, are both sparsely distributed and are poorly resolved in time. The identification and development of new archives and proxies for studying key ocean processes at annual to sub-annual resolution that can extend the marine instrumental record is therefore a clear priority for marine climate science. The bivalve species Arctica islandica is a unique paleoclimatic archive with an exceptional longevity combined with high temporal resolution, due to accretion of annual growth increments. The aim of this study is to use sclerochronological records of A. islandica to extend instrumental hydrographic records and increase our understanding of a variability of a Norwegian Coastal Current (NCC). The NCC transports warm, low-salinity water northwards, which eventually plays role for the Arctic halocline. Moreover, previous investigations showed the connection of properties and variability of the NCC with catches of commercially valuable fishes. The knowledge of the variability of the NCC is also essential for possible future prediction climate conditions and fish stock variability in the region. In this study we use shells of Arctica islandica collected off the coast of Eggum (Lofoten, Norway). The material was obtained from the depth 5-10 m by dredging along the seabed and by means of scuba divers. We examine the growth patterns of living and subfossil shells. Ongoing work mainly focuses on the construction of a composite growth chronology based on increment-width time series. The results we will compare with existing time series of the environment and climatic parameters to determine the controlling factors and test the applicability of growth chronology in a climate reconstruction. Furthermore, we will perform geochemical analyses of the stable isotope composition (δ18O and δ13C) in shell carbonate to identify seasonal signals and reconstruct the surface water temperature on a sub-annual time-scale.

Japanese and Taiwanese pelagic longline fleet dynamics and the impacts of climate change in the southern Indian Ocean

NASA Astrophysics Data System (ADS)

Michael, P. E.; Wilcox, C.; Tuck, G. N.; Hobday, A. J.; Strutton, P. G.

2017-06-01

Climate change is projected to continue shifting the distribution of marine species, leading to changes in local assemblages and different interactions with human activities. With regard to fisheries, understanding the relationship between fishing fleets, target species catch per unit effort (CPUE), and the environment enhances our ability to anticipate fisher response and is an essential step towards proactive management. Here, we explore the potential impact of climate change in the southern Indian Ocean by modelling Japanese and Taiwanese pelagic longline fleet dynamics. We quantify the mean and variability of target species CPUE and the relative value and cost of fishing in different areas. Using linear mixed models, we identify fleet-specific effort allocation strategies most related to observed effort and predict the future distribution of effort and tuna catch under climate change for 2063-2068. The Japanese fleet's strategy targets high-value species and minimizes the variability in CPUE of the primary target species. Conversely, the Taiwanese strategy indicated flexible targeting of a broad range of species, fishing in areas of high and low variability in catch, and minimizing costs. The projected future mean and variability in CPUE across species suggest a slight increase in CPUE in currently high CPUE areas for most species. The corresponding effort projections suggest a slight increase in Japanese effort in the western and eastern study area, and Taiwanese effort increasing east of Madagascar. This approach provides a useful method for managers to explore the impacts of different fishing and fleet management strategies for the future.

Interannual Variability of Regional Hadley Circulation Intensity Over Western Pacific During Boreal Winter and Its Climatic Impact Over Asia-Australia Region

NASA Astrophysics Data System (ADS)

Huang, Ruping; Chen, Shangfeng; Chen, Wen; Hu, Peng

2018-01-01

This study investigates interannual variability of boreal winter regional Hadley circulation over western Pacific (WPHC) and its climatic impacts. A WPHC intensity index (WPHCI) is defined as the vertical shear of the divergent meridional winds. It shows that WPHCI correlates well with the El Niño-Southern Oscillation (ENSO). To investigate roles of the ENSO-unrelated part of WPHCI (WPHCIres), variables that are linearly related to the Niño-3 index have been removed. It reveals that meridional sea surface temperature gradient over the western Pacific plays an essential role in modulating the WPHCIres. The climatic impacts of WPHCIres are further investigated. Below-normal (above-normal) precipitation appears over south China (North Australia) when WPHCIres is stronger. This is due to the marked convergence (divergence) anomalies at the upper troposphere, divergence (convergence) at the lower troposphere, and the accompanied downward (upward) motion over south China (North Australia), which suppresses (enhances) precipitation there. In addition, a pronounced increase in surface air temperature (SAT) appears over south and central China when WPHCIres is stronger. A temperature diagnostic analysis suggests that the increase in SAT tendency over central China is primarily due to the warm zonal temperature advection and subsidence-induced adiabatic heating. In addition, the increase in SAT tendency over south China is primarily contributed by the warm meridional temperature advection. Further analysis shows that the correlation of WPHCIres with the East Asian winter monsoon (EAWM) is weak. Thus, this study may provide additional sources besides EAWM and ENSO to improve understanding of the Asia-Australia climate variability.

Signature of present and projected climate change at an urban scale: The case of Addis Ababa

NASA Astrophysics Data System (ADS)

Arsiso, Bisrat Kifle; Mengistu Tsidu, Gizaw; Stoffberg, Gerrit Hendrik

2018-06-01

Understanding climate change and variability at an urban scale is essential for water resource management, land use planning, development of adaption plans, mitigation of air and water pollution. However, there are serious challenges to meet these goals due to unavailability of observed and/or simulated high resolution spatial and temporal climate data. The statistical downscaling of general circulation climate model, for instance, is usually driven by sparse observational data hindering the use of downscaled data to investigate urban scale climate variability and change in the past. Recently, these challenges are partly resolved by concerted international effort to produce global and high spatial resolution climate data. In this study, the 1 km2 high resolution NIMR-HadGEM2-AO simulations for future projections under Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios and gridded observations provided by Worldclim data center are used to assess changes in rainfall, minimum and maximum temperature expected under the two scenarios over Addis Ababa city. The gridded 1 km2 observational data set for the base period (1950-2000) is compared to observation from a meteorological station in the city in order to assess its quality for use as a reference (baseline) data. The comparison revealed that the data set has a very good quality. The rainfall anomalies under RCPs scenarios are wet in the 2030s (2020-2039), 2050s (2040-2069) and 2080s (2070-2099). Both minimum and maximum temperature anomalies under RCPs are successively getting warmer during these periods. Thus, the projected changes under RCPs scenarios show a general increase in rainfall and temperatures with strong variabilities in rainfall during rainy season implying level of difficulty in water resource use and management as well as land use planning and management.

Integrating uncertainty propagation in GNSS radio occultation retrieval: from excess phase to atmospheric bending angle profiles

NASA Astrophysics Data System (ADS)

Schwarz, Jakob; Kirchengast, Gottfried; Schwaerz, Marc

2018-05-01

Global Navigation Satellite System (GNSS) radio occultation (RO) observations are highly accurate, long-term stable data sets and are globally available as a continuous record from 2001. Essential climate variables for the thermodynamic state of the free atmosphere - such as pressure, temperature, and tropospheric water vapor profiles (involving background information) - can be derived from these records, which therefore have the potential to serve as climate benchmark data. However, to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties quantified and traced throughout the retrieval chain from raw observations to essential climate variables. The new Reference Occultation Processing System (rOPS) at the Wegener Center aims to deliver such an accurate RO retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented in the rOPS for uncertainty propagation from excess phase to atmospheric bending angle profiles, for estimated systematic and random uncertainties, including vertical error correlations and resolution estimates. We estimated systematic uncertainty profiles with the same operators as used for the basic state profiles retrieval. The random uncertainty is traced through covariance propagation and validated using Monte Carlo ensemble methods. The algorithm performance is demonstrated using test day ensembles of simulated data as well as real RO event data from the satellite missions CHAllenging Minisatellite Payload (CHAMP); Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC); and Meteorological Operational Satellite A (MetOp). The results of the Monte Carlo validation show that our covariance propagation delivers correct uncertainty quantification from excess phase to bending angle profiles. The results from the real RO event ensembles demonstrate that the new uncertainty estimation chain performs robustly. Together with the other parts of the rOPS processing chain this part is thus ready to provide integrated uncertainty propagation through the whole RO retrieval chain for the benefit of climate monitoring and other applications.

Climate forcings and feedbacks

NASA Technical Reports Server (NTRS)

Hansen, James

1993-01-01

Global temperature has increased significantly during the past century. Understanding the causes of observed global temperature change is impossible in the absence of adequate monitoring of changes in global climate forcings and radiative feedbacks. Climate forcings are changes imposed on the planet's energy balance, such as change of incoming sunlight or a human-induced change of surface properties due to deforestation. Radiative feedbacks are radiative changes induced by climate change, such as alteration of cloud properties or the extent of sea ice. Monitoring of global climate forcings and feedbacks, if sufficiently precise and long-term, can provide a very strong constraint on interpretation of observed temperature change. Such monitoring is essential to eliminate uncertainties about the relative importance of various climate change mechanisms including tropospheric sulfate aerosols from burning of coal and oil smoke from slash and burn agriculture, changes of solar irradiance changes of several greenhouse gases, and many other mechanisms. The considerable variability of observed temperature, together with evidence that a substantial portion of this variability is unforced indicates that observations of climate forcings and feedbacks must be continued for decades. Since the climate system responds to the time integral of the forcing, a further requirement is that the observations be carried out continuously. However, precise observations of forcings and feedbacks will also be able to provide valuable conclusions on shorter time scales. For example, knowledge of the climate forcing by increasing CFC's relative to the forcing by changing ozone is important to policymakers, as is information on the forcing by CO2 relative to the forcing by sulfate aerosols. It will also be possible to obtain valuable tests of climate models on short time scales, if there is precise monitoring of all forcings and feedbacks during and after events such as a large volcanic eruption or an El Nino.

Drought change in the middle reach of the Yellow River since the late Ming Dynasty and their correlation with runoff

NASA Astrophysics Data System (ADS)

Chen, F.

2017-12-01

Because of the reported decreasing trends in precipitation and streamflow in north-central China (Starting point of Ancient Silk Road), it is essential to understand long-term in water resource availability in this area. Thus, this research presents a new February-August PDSI reconstruction spanning CE 1615-2013 for the southern edge of the Gobi Desert under a highly variable arid and semi-arid climate in northern China. In addition to this new PDSI reconstruction, some previously published annual precipitation/PDSI reconstructions from the neighbouring regions were also used to infer the large-scale hydro-climatic signal of the middle reach of the Yellow River. Spatial correlation analyses with gridded precipitation data showed that the tree-ring records were indeed able to capture much of the regional interannual hydro-climatic signal variability. Using principal component analyses on the reconstructions and documentary records, many large-scale dry and flood events were found during the period AD 1615-2006. Many of these dry events have had profound impacts on the people of the study area over the past several centuries. Temporal correlations among the reconstruction and climatic indices, such as the El Niño-Southern Oscillation, demonstrate that water availability is influenced by tropical and high-latitude forcings in the Pacific rim. Continued work in this direction should enable us to understand better the hydrological change under global warming and the past climate variability of the silk road over long temporal and large spatial scales.

GCMs and MDGs: can climate science reduce poverty?

NASA Astrophysics Data System (ADS)

Thomson, M. C.; Connor, S. J.

2004-12-01

Sub-Saharan Africa, the birthplace of humankind, is seen by many, both as the least developed region of the world, and the region where the processes of globalization and sustainable development are most difficult to set in motion. Sub-Saharan African countries invariably appear en masse at the bottom of the annual UNDP Human Development Report rankings with development indicators such as life expectancy and basic nutrition levels in decline. The poorer communities are most vulnerable to adverse impacts of climate fluctuations and seen as the least able to cope with current climate variability. Sub-Saharan Africa has a population of approximately 625 million, with more than two thirds of the people dependant on rain-fed agriculture. The vast majority of the population lack access to clean water and sanitation and sub-Saharan Africa currently bears the highest burden of infectious diseases such as HIV-AIDS, TB and Malaria to be found anywhere in the world. With almost half of the region's population living on less than US$1 per day, sub-Saharan Africa accounts for one quarter of the world's poor. The rural poor are often considered to have no voice and therefore form a very weak political constituency. International development targets such as the recently articulated UN Millennium Development Goals are seen as one means of giving voice to this large but disenfranchised population. Improved management of climate sensitive sectors is essential to achieving a number of the MDgs: Poverty-Hunger, Disease, Water and sanitation. Climate information is also essential to measuring that achievement, as climate often acts as a confounder in any analysis of interventions. Here we present work on how climate science, including state of the art - multi-model ensemble seasonal climate forecasting models, are being used in support of achieving the MDGs in Africa.

Climate and the complexity of migratory phenology: sexes, migratory distance, and arrival distributions

NASA Astrophysics Data System (ADS)

Macmynowski, Dena P.; Root, Terry L.

2007-05-01

The intra- and inter-season complexity of bird migration has received limited attention in climatic change research. Our phenological analysis of 22 species collected in Chicago, USA, (1979 2002) evaluates the relationship between multi-scalar climate variables and differences (1) in arrival timing between sexes, (2) in arrival distributions among species, and (3) between spring and fall migration. The early migratory period for earliest arriving species (i.e., short-distance migrants) and earliest arriving individuals of a species (i.e., males) most frequently correlate with climate variables. Compared to long-distance migrant species, four times as many short-distance migrants correlate with spring temperature, while 8 of 11 (73%) of long-distance migrant species’ arrival is correlated with the North Atlantic Oscillation (NAO). While migratory phenology has been correlated with NAO in Europe, we believe that this is the first documentation of a significant association in North America. Geographically proximate conditions apparently influence migratory timing for short-distance migrants while continental-scale climate (e.g., NAO) seemingly influences the phenology of Neotropical migrants. The preponderance of climate correlations is with the early migratory period, not the median of arrival, suggesting that early spring conditions constrain the onset or rate of migration for some species. The seasonal arrival distribution provides considerable information about migratory passage beyond what is apparent from statistical analyses of phenology. A relationship between climate and fall phenology is not detected at this location. Analysis of the within-season complexity of migration, including multiple metrics of arrival, is essential to detect species’ responses to changing climate as well as evaluate the underlying biological mechanisms.

Optimization of Water Resources and Agricultural Activities for Economic Benefit in Colorado

NASA Astrophysics Data System (ADS)

LIM, J.; Lall, U.

2017-12-01

The limited water resources available for irrigation are a key constraint for the important agricultural sector of Colorado's economy. As climate change and groundwater depletion reshape these resources, it is essential to understand the economic potential of water resources under different agricultural production practices. This study uses a linear programming optimization at the county spatial scale and annual temporal scales to study the optimal allocation of water withdrawal and crop choices. The model, AWASH, reflects streamflow constraints between different extraction points, six field crops, and a distinct irrigation decision for maize and wheat. The optimized decision variables, under different environmental, social, economic, and physical constraints, provide long-term solutions for ground and surface water distribution and for land use decisions so that the state can generate the maximum net revenue. Colorado, one of the largest agricultural producers, is tested as a case study and the sensitivity on water price and on climate variability is explored.

Developmental phenotypic plasticity helps bridge stochastic weather events associated with climate change.

PubMed

Burggren, Warren

2018-05-10

The slow, inexorable rise in annual average global temperatures and acidification of the oceans are often advanced as consequences of global change. However, many environmental changes, especially those involving weather (as opposed to climate), are often stochastic, variable and extreme, particularly in temperate terrestrial or freshwater habitats. Moreover, few studies of animal and plant phenotypic plasticity employ realistic (i.e. short-term, stochastic) environmental change in their protocols. Here, I posit that the frequently abrupt environmental changes (days, weeks, months) accompanying much longer-term general climate change (e.g. global warming over decades or centuries) require consideration of the true nature of environmental change (as opposed to statistical means) coupled with an expansion of focus to consider developmental phenotypic plasticity. Such plasticity can be in multiple forms - obligatory/facultative, beneficial/deleterious - depending upon the degree and rate of environmental variability at specific points in organismal development. Essentially, adult phenotypic plasticity, as important as it is, will be irrelevant if developing offspring lack sufficient plasticity to create modified phenotypes necessary for survival. © 2018. Published by The Company of Biologists Ltd.

Near-surface wind speed statistical distribution: comparison between ECMWF System 4 and ERA-Interim

NASA Astrophysics Data System (ADS)

Marcos, Raül; Gonzalez-Reviriego, Nube; Torralba, Verónica; Cortesi, Nicola; Young, Doo; Doblas-Reyes, Francisco J.

2017-04-01

In the framework of seasonal forecast verification, knowing whether the characteristics of the climatological wind speed distribution, simulated by the forecasting systems, are similar to the observed ones is essential to guide the subsequent process of bias adjustment. To bring some light about this topic, this work assesses the properties of the statistical distributions of 10m wind speed from both ERA-Interim reanalysis and seasonal forecasts of ECMWF system 4. The 10m wind speed distribution has been characterized in terms of the four main moments of the probability distribution (mean, standard deviation, skewness and kurtosis) together with the coefficient of variation and goodness of fit Shapiro-Wilks test, allowing the identification of regions with higher wind variability and non-Gaussian behaviour at monthly time-scales. Also, the comparison of the predicted and observed 10m wind speed distributions has been measured considering both inter-annual and intra-seasonal variability. Such a comparison is important in both climate research and climate services communities because it provides useful climate information for decision-making processes and wind industry applications.

Climatic vulnerability of the world’s freshwater and marine fishes

NASA Astrophysics Data System (ADS)

Comte, Lise; Olden, Julian D.

2017-10-01

Climate change is a mounting threat to biological diversity, compromising ecosystem structure and function, and undermining the delivery of essential services worldwide. As the magnitude and speed of climate change accelerates, greater understanding of the taxonomy and geography of climatic vulnerability is critical to guide effective conservation action. However, many uncertainties remain regarding the degree and variability of climatic risk within entire clades and across vast ecosystem boundaries. Here we integrate physiological estimates of thermal sensitivity for 2,960 ray-finned fishes with future climatic exposure, and demonstrate that global patterns of vulnerability differ substantially between freshwater and marine realms. Our results suggest that climatic vulnerability for freshwater faunas will be predominantly determined by elevated levels of climatic exposure predicted for the Northern Hemisphere, whereas marine faunas in the tropics will be the most at risk, reflecting their higher intrinsic sensitivity. Spatial overlap between areas of high physiological risk and high human impacts, together with evidence of low past rates of evolution in upper thermal tolerance, highlights the urgency of global conservation actions and policy initiatives if harmful climate effects on the world’s fishes are to be mitigated in the future.

Collaboration pathway(s) using new tools for optimizing `operational' climate monitoring from space

NASA Astrophysics Data System (ADS)

Helmuth, Douglas B.; Selva, Daniel; Dwyer, Morgan M.

2015-09-01

Consistently collecting the earth's climate signatures remains a priority for world governments and international scientific organizations. Architecting a long term solution requires transforming scientific missions into an optimized robust `operational' constellation that addresses the collective needs of policy makers, scientific communities and global academic users for trusted data. The application of new tools offers pathways for global architecture collaboration. Recent rule-based expert system (RBES) optimization modeling of the intended NPOESS architecture becomes a surrogate for global operational climate monitoring architecture(s). These rulebased systems tools provide valuable insight for global climate architectures, by comparison/evaluation of alternatives and the sheer range of trade space explored. Optimization of climate monitoring architecture(s) for a partial list of ECV (essential climate variables) is explored and described in detail with dialogue on appropriate rule-based valuations. These optimization tool(s) suggest global collaboration advantages and elicit responses from the audience and climate science community. This paper will focus on recent research exploring joint requirement implications of the high profile NPOESS architecture and extends the research and tools to optimization for a climate centric case study. This reflects work from SPIE RS Conferences 2013 and 2014, abridged for simplification30, 32. First, the heavily securitized NPOESS architecture; inspired the recent research question - was Complexity (as a cost/risk factor) overlooked when considering the benefits of aggregating different missions into a single platform. Now years later a complete reversal; should agencies considering Disaggregation as the answer. We'll discuss what some academic research suggests. Second, using the GCOS requirements of earth climate observations via ECV (essential climate variables) many collected from space-based sensors; and accepting their definitions of global coverages intended to ensure the needs of major global and international organizations (UNFCCC and IPCC) are met as a core objective. Consider how new optimization tools like rule-based engines (RBES) offer alternative methods of evaluating collaborative architectures and constellations? What would the trade space of optimized operational climate monitoring architectures of ECV look like? Third, using the RBES tool kit (2014) demonstrate with application to a climate centric rule-based decision engine - optimizing architectural trades of earth observation satellite systems, allowing comparison(s) to existing architectures and gaining insights for global collaborative architectures. How difficult is it to pull together an optimized climate case study - utilizing for example 12 climate based instruments on multiple existing platforms and nominal handful of orbits; for best cost and performance benefits against the collection requirements of representative set of ECV. How much effort and resources would an organization expect to invest to realize these analysis and utility benefits?

1,000 Years of Climatic Variability in the Upper Colorado River Basin, USA

NASA Astrophysics Data System (ADS)

Tingstad, A. H.; MacDonald, G. M.

2008-12-01

The Upper Colorado River Basin (UCRB) is an essential water resource region in the United States. Seven western U.S. states, including water-hungry California, depend on water originating in the UCRB to support rising populations, agriculture, and infrastructure. Predictions that drought and depletion of water resources will intensify in the next several decades due to human-induced climate warming makes it essential that the natural patterns and causes of drought in the UCRB are understood. In particular, droughts that occurred during the Medieval Period (~ A.D. 900-1200) are of interest because temperatures are known to have been elevated during this time. We present a new 1,000-year tree-ring reconstruction for part of the UCRB using Pinus edulis (two-needle Pinyon) samples from northeastern Utah. We evaluate variability in the summer (JJA) and annual Palmer Drought Severity Index (PDSI) for the Uinta Mountains region, and use wavelet and other analyses to determine the importance of the El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) in determining the timing and duration of droughts in the region. We conclude that while intense droughts did occur during the Medieval Period and throughout the record, water shortages may not be spatially and temporally uniform throughout the UCRB and the western U.S.

Revealing, Reducing, and Representing Uncertainties in New Hydrologic Projections for Climate-changed Futures

NASA Astrophysics Data System (ADS)

Arnold, Jeffrey; Clark, Martyn; Gutmann, Ethan; Wood, Andy; Nijssen, Bart; Rasmussen, Roy

2016-04-01

The United States Army Corps of Engineers (USACE) has had primary responsibility for multi-purpose water resource operations on most of the major river systems in the U.S. for more than 200 years. In that time, the USACE projects and programs making up those operations have proved mostly robust against the range of natural climate variability encountered over their operating life spans. However, in some watersheds and for some variables, climate change now is known to be shifting the hydroclimatic baseline around which that natural variability occurs and changing the range of that variability as well. This makes historical stationarity an inappropriate basis for assessing continued project operations under climate-changed futures. That means new hydroclimatic projections are required at multiple scales to inform decisions about specific threats and impacts, and for possible adaptation responses to limit water-resource vulnerabilities and enhance operational resilience. However, projections of possible future hydroclimatologies have myriad complex uncertainties that require explicit guidance for interpreting and using them to inform those decisions about climate vulnerabilities and resilience. Moreover, many of these uncertainties overlap and interact. Recent work, for example, has shown the importance of assessing the uncertainties from multiple sources including: global model structure [Meehl et al., 2005; Knutti and Sedlacek, 2013]; internal climate variability [Deser et al., 2012; Kay et al., 2014]; climate downscaling methods [Gutmann et al., 2012; Mearns et al., 2013]; and hydrologic models [Addor et al., 2014; Vano et al., 2014; Mendoza et al., 2015]. Revealing, reducing, and representing these uncertainties is essential for defining the plausible quantitative climate change narratives required to inform water-resource decision-making. And to be useful, such quantitative narratives, or storylines, of climate change threats and hydrologic impacts must sample from the full range of uncertainties associated with all parts of the simulation chain, from global climate models with simulations of natural climate variability, through regional climate downscaling, and on to modeling of affected hydrologic processes and downstream water resources impacts. This talk will present part of the work underway now both to reveal and reduce some important uncertainties and to develop explicit guidance for future generation of quantitative hydroclimatic storylines. Topics will include: 1- model structural and parameter-set limitations of some methods widely used to quantify climate impacts to hydrologic processes [Gutmann et al., 2014; Newman et al., 2015]; 2- development and evaluation of new, spatially consistent, U.S. national-scale climate downscaling and hydrologic simulation capabilities directly relevant at the multiple scales of water-resource decision-making [Newman et al., 2015; Mizukami et al., 2015; Gutmann et al., 2016]; and 3- development and evaluation of advanced streamflow forecasting methods to reduce and represent integrated uncertainties in a tractable way [Wood et al., 2014; Wood et al., 2015]. A key focus will be areas where climatologic and hydrologic science is currently under-developed to inform decisions - or is perhaps wrongly scaled or misapplied in practice - indicating the need for additional fundamental science and interpretation.

Divergence in Forest-Type Response to Climate and Weather: Evidence for Regional Links Between Forest-Type Evenness and Net Primary Productivity

USGS Publications Warehouse

Bradford, J.B.

2011-01-01

Climate change is altering long-term climatic conditions and increasing the magnitude of weather fluctuations. Assessing the consequences of these changes for terrestrial ecosystems requires understanding how different vegetation types respond to climate and weather. This study examined 20 years of regional-scale remotely sensed net primary productivity (NPP) in forests of the northern Lake States to identify how the relationship between NPP and climate or weather differ among forest types, and if NPP patterns are influenced by landscape-scale evenness of forest-type abundance. These results underscore the positive relationship between temperature and NPP. Importantly, these results indicate significant differences among broadly defined forest types in response to both climate and weather. Essentially all weather variables that were strongly related to annual NPP displayed significant differences among forest types, suggesting complementarity in response to environmental fluctuations. In addition, this study found that forest-type evenness (within 8 ?? 8 km2 areas) is positively related to long-term NPP mean and negatively related to NPP variability, suggesting that NPP in pixels with greater forest-type evenness is both higher and more stable through time. This is landscape- to subcontinental-scale evidence of a relationship between primary productivity and one measure of biological diversity. These results imply that anthropogenic or natural processes that influence the proportional abundance of forest types within landscapes may influence long-term productivity patterns. ?? 2011 Springer Science+Business Media, LLC (outside the USA).

Sensitivity of proxies on non-linear interactions in the climate system

PubMed Central

Schultz, Johannes A.; Beck, Christoph; Menz, Gunter; Neuwirth, Burkhard; Ohlwein, Christian; Philipp, Andreas

2015-01-01

Recent climate change is affecting the earth system to an unprecedented extent and intensity and has the potential to cause severe ecological and socioeconomic consequences. To understand natural and anthropogenic induced processes, feedbacks, trends, and dynamics in the climate system, it is also essential to consider longer timescales. In this context, annually resolved tree-ring data are often used to reconstruct past temperature or precipitation variability as well as atmospheric or oceanic indices such as the North Atlantic Oscillation (NAO) or the Atlantic Multidecadal Oscillation (AMO). The aim of this study is to assess weather-type sensitivity across the Northern Atlantic region based on two tree-ring width networks. Our results indicate that nonstationarities in superordinate space and time scales of the climate system (here synoptic- to global scale, NAO, AMO) can affect the climate sensitivity of tree-rings in subordinate levels of the system (here meso- to synoptic scale, weather-types). This scale bias effect has the capability to impact even large multiproxy networks and the ability of these networks to provide information about past climate conditions. To avoid scale biases in climate reconstructions, interdependencies between the different scales in the climate system must be considered, especially internal ocean/atmosphere dynamics. PMID:26686001

Influence of Environmental Factors on Essential Oil Variability in Origanum compactum Benth. Growing Wild in Morocco.

PubMed

Aboukhalid, Kaoutar; Al Faiz, Chaouki; Douaik, Ahmed; Bakha, Mohamed; Kursa, Karolina; Agacka-Mołdoch, Monika; Machon, Nathalie; Tomi, Félix; Lamiri, Abdeslam

2017-09-01

The present study aimed to evaluate the influence of environmental factors on essential oils (EOs) composition of Origanum compactum populations sampled all over the distribution area of the species in Morocco, and to determine the extent of the chemical profiles throughout the geographical distribution of the species. The chemical compositions were submitted to canonical correlation analysis and canonical discriminant analysis that indicated a significant relationship between oil components and some environmental factors. According to their chemical composition and edapho-climatic characteristics, two major groups of populations were differentiated. The first group was composed of samples growing in regions with humid climate, clayey, sandy, and alkaline soils. These samples showed high thymol, α-terpineol, linalool, and carvacryl methyl oxide content. The second group consisted of plants belonging to semi-arid climate, and growing at high altitudes and silty soils. These samples were characterized by high carvacrol, α-thujene, α-terpinene, and myrcene content. However, populations exposed to sub-humid climate, appeared less homogeneous and belong mainly either to the first or second group. A significant correlation between some edaphic factors (pH, K 2 O content, soil texture) and the EOs yield of O. compactum plants was evidenced. In spite of the correlation obtained for the oil composition with edapho-climatic factors and the variance explained by the environmental data set, the observed EO diversity might be also genetically determined. © 2017 Wiley-VHCA AG, Zurich, Switzerland.

Evaluation of mean climate in a chemistry-climate model simulation

NASA Astrophysics Data System (ADS)

Hong, S.; Park, H.; Wie, J.; Park, R.; Lee, S.; Moon, B. K.

2017-12-01

Incorporation of the interactive chemistry is essential for understanding chemistry-climate interactions and feedback processes in climate models. Here we assess a newly developed chemistry-climate model (GRIMs-Chem), which is based on the Global/Regional Integrated Model system (GRIMs) including the aerosol direct effect as well as stratospheric linearized ozone chemistry (LINOZ). We conducted GRIMs-Chem with observed sea surface temperature during the period of 1979-2010, and compared the simulation results with observations and also with CMIP models. To measure the relative performance of our model, we define the quantitative performance metric using the Taylor diagram. This metric allow us to assess overall features in simulating multiple variables. Overall, our model better reproduce the zonal mean spatial pattern of temperature, horizontal wind, vertical motion, and relative humidity relative to other models. However, the model did not produce good simulations at upper troposphere (200 hPa). It is currently unclear which model processes are responsible for this. AcknowledgementsThis research was supported by the Korea Ministry of Environment (MOE) as "Climate Change Correspondence Program."

Geographic distribution of phlebotomine sandfly species (Diptera: Psychodidae) in Central-West Brazil.

PubMed

Almeida, Paulo Silva de; Andrade, Andrey José de; Sciamarelli, Alan; Raizer, Josué; Menegatti, Jaqueline Aparecida; Hermes, Sandra Cristina Negreli Moreira; Carvalho, Maria do Socorro Laurentino de; Gurgel-Gonçalves, Rodrigo

2015-06-01

This study updates the geographic distributions of phlebotomine species in Central-West Brazil and analyses the climatic factors associated with their occurrence. The data were obtained from the entomology services of the state departments of health in Central-West Brazil, scientific collections and a literature review of articles from 1962-2014. Ecological niche models were produced for sandfly species with more than 20 occurrences using the Maxent algorithm and eight climate variables. In all, 2,803 phlebotomine records for 127 species were analysed. Nyssomyia whitmani, Evandromyia lenti and Lutzomyia longipalpis were the species with the greatest number of records and were present in all the biomes in Central-West Brazil. The models, which were produced for 34 species, indicated that the Cerrado areas in the central and western regions of Central-West Brazil were climatically more suitable to sandflies. The variables with the greatest influence on the models were the temperature in the coldest months and the temperature seasonality. The results show that phlebotomine species in Central-West Brazil have different geographical distribution patterns and that climate conditions in essentially the entire region favour the occurrence of at least one Leishmania vector species, highlighting the need to maintain or intensify vector control and surveillance strategies.

Geographic distribution of phlebotomine sandfly species (Diptera: Psychodidae) in Central-West Brazil

PubMed Central

de Almeida, Paulo Silva; de Andrade, Andrey José; Sciamarelli, Alan; Raizer, Josué; Menegatti, Jaqueline Aparecida; Hermes, Sandra Cristina Negreli Moreira; de Carvalho, Maria do Socorro Laurentino; Gurgel-Gonçalves, Rodrigo

2015-01-01

This study updates the geographic distributions of phlebotomine species in Central-West Brazil and analyses the climatic factors associated with their occurrence. The data were obtained from the entomology services of the state departments of health in Central-West Brazil, scientific collections and a literature review of articles from 1962-2014. Ecological niche models were produced for sandfly species with more than 20 occurrences using the Maxent algorithm and eight climate variables. In all, 2,803 phlebotomine records for 127 species were analysed. Nyssomyia whitmani, Evandromyia lenti and Lutzomyia longipalpis were the species with the greatest number of records and were present in all the biomes in Central-West Brazil. The models, which were produced for 34 species, indicated that the Cerrado areas in the central and western regions of Central-West Brazil were climatically more suitable to sandflies. The variables with the greatest influence on the models were the temperature in the coldest months and the temperature seasonality. The results show that phlebotomine species in Central-West Brazil have different geographical distribution patterns and that climate conditions in essentially the entire region favour the occurrence of at least one Leishmania vector species, highlighting the need to maintain or intensify vector control and surveillance strategies. PMID:26018450

On the reliability of seasonal climate forecasts

PubMed Central

Weisheimer, A.; Palmer, T. N.

2014-01-01

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time. PMID:24789559

Large deviation probabilities for correlated Gaussian stochastic processes and daily temperature anomalies

NASA Astrophysics Data System (ADS)

Massah, Mozhdeh; Kantz, Holger

2016-04-01

As we have one and only one earth and no replicas, climate characteristics are usually computed as time averages from a single time series. For understanding climate variability, it is essential to understand how close a single time average will typically be to an ensemble average. To answer this question, we study large deviation probabilities (LDP) of stochastic processes and characterize them by their dependence on the time window. In contrast to iid variables for which there exists an analytical expression for the rate function, the correlated variables such as auto-regressive (short memory) and auto-regressive fractionally integrated moving average (long memory) processes, have not an analytical LDP. We study LDP for these processes, in order to see how correlation affects this probability in comparison to iid data. Although short range correlations lead to a simple correction of sample size, long range correlations lead to a sub-exponential decay of LDP and hence to a very slow convergence of time averages. This effect is demonstrated for a 120 year long time series of daily temperature anomalies measured in Potsdam (Germany).

Ecohydrologic processes and soil thickness feedbacks control limestone-weathering rates in a karst landscape

DOE PAGES

Dong, Xiaoli; Cohen, Matthew J.; Martin, Jonathan B.; ...

2018-05-18

Here, chemical weathering of bedrock plays an essential role in the formation and evolution of Earth's critical zone. Over geologic time, the negative feedback between temperature and chemical weathering rates contributes to the regulation of Earth climate. The challenge of understanding weathering rates and the resulting evolution of critical zone structures lies in complicated interactions and feedbacks among environmental variables, local ecohydrologic processes, and soil thickness, the relative importance of which remains unresolved. We investigate these interactions using a reactive-transport kinetics model, focusing on a low-relief, wetland-dominated karst landscape (Big Cypress National Preserve, South Florida, USA) as a case study.more » Across a broad range of environmental variables, model simulations highlight primary controls of climate and soil biological respiration, where soil thickness both supplies and limits transport of biologically derived acidity. Consequently, the weathering rate maximum occurs at intermediate soil thickness. The value of the maximum weathering rate and the precise soil thickness at which it occurs depend on several environmental variables, including precipitation regime, soil inundation, vegetation characteristics, and rate of groundwater drainage. Simulations for environmental conditions specific to Big Cypress suggest that wetland depressions in this landscape began to form around beginning of the Holocene with gradual dissolution of limestone bedrock and attendant soil development, highlighting large influence of age-varying soil thickness on weathering rates and consequent landscape development. While climatic variables are often considered most important for chemical weathering, our results indicate that soil thickness and biotic activity are equally important. Weathering rates reflect complex interactions among soil thickness, climate, and local hydrologic and biotic processes, which jointly shape the supply and delivery of chemical reactants, and the resulting trajectories of critical zone and karst landscape development.« less

Ecohydrologic processes and soil thickness feedbacks control limestone-weathering rates in a karst landscape

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dong, Xiaoli; Cohen, Matthew J.; Martin, Jonathan B.

Here, chemical weathering of bedrock plays an essential role in the formation and evolution of Earth's critical zone. Over geologic time, the negative feedback between temperature and chemical weathering rates contributes to the regulation of Earth climate. The challenge of understanding weathering rates and the resulting evolution of critical zone structures lies in complicated interactions and feedbacks among environmental variables, local ecohydrologic processes, and soil thickness, the relative importance of which remains unresolved. We investigate these interactions using a reactive-transport kinetics model, focusing on a low-relief, wetland-dominated karst landscape (Big Cypress National Preserve, South Florida, USA) as a case study.more » Across a broad range of environmental variables, model simulations highlight primary controls of climate and soil biological respiration, where soil thickness both supplies and limits transport of biologically derived acidity. Consequently, the weathering rate maximum occurs at intermediate soil thickness. The value of the maximum weathering rate and the precise soil thickness at which it occurs depend on several environmental variables, including precipitation regime, soil inundation, vegetation characteristics, and rate of groundwater drainage. Simulations for environmental conditions specific to Big Cypress suggest that wetland depressions in this landscape began to form around beginning of the Holocene with gradual dissolution of limestone bedrock and attendant soil development, highlighting large influence of age-varying soil thickness on weathering rates and consequent landscape development. While climatic variables are often considered most important for chemical weathering, our results indicate that soil thickness and biotic activity are equally important. Weathering rates reflect complex interactions among soil thickness, climate, and local hydrologic and biotic processes, which jointly shape the supply and delivery of chemical reactants, and the resulting trajectories of critical zone and karst landscape development.« less

Human activities and climate variability drive fast-paced change across the world's estuarine-coastal ecosystems

USGS Publications Warehouse

Cloern, James E.; Abreu, Paulo C.; Carstensen, Jacob; Chauvaud, Laurent; Elmgren, Ragnar; Grall, Jacques; Greening, Holly; Johansson, John O.R.; Kahru, Mati; Sherwood, Edward T.; Xu, Jie; Yin, Kedong

2016-01-01

Time series of environmental measurements are essential for detecting, measuring and understanding changes in the Earth system and its biological communities. Observational series have accumulated over the past 2–5 decades from measurements across the world's estuaries, bays, lagoons, inland seas and shelf waters influenced by runoff. We synthesize information contained in these time series to develop a global view of changes occurring in marine systems influenced by connectivity to land. Our review is organized around four themes: (i) human activities as drivers of change; (ii) variability of the climate system as a driver of change; (iii) successes, disappointments and challenges of managing change at the sea-land interface; and (iv) discoveries made from observations over time. Multidecadal time series reveal that many of the world's estuarine–coastal ecosystems are in a continuing state of change, and the pace of change is faster than we could have imagined a decade ago. Some have been transformed into novel ecosystems with habitats, biogeochemistry and biological communities outside the natural range of variability. Change takes many forms including linear and nonlinear trends, abrupt state changes and oscillations. The challenge of managing change is daunting in the coastal zone where diverse human pressures are concentrated and intersect with different responses to climate variability over land and over ocean basins. The pace of change in estuarine–coastal ecosystems will likely accelerate as the human population and economies continue to grow and as global climate change accelerates. Wise stewardship of the resources upon which we depend is critically dependent upon a continuing flow of information from observations to measure, understand and anticipate future changes along the world's coastlines.

Long-term forecasting of meteorological time series using Nonlinear Canonical Correlation Analysis (NLCCA)

NASA Astrophysics Data System (ADS)

Woldesellasse, H. T.; Marpu, P. R.; Ouarda, T.

2016-12-01

Wind is one of the crucial renewable energy sources which is expected to bring solutions to the challenges of clean energy and the global issue of climate change. A number of linear and nonlinear multivariate techniques has been used to predict the stochastic character of wind speed. A wind forecast with good accuracy has a positive impact on the reduction of electricity system cost and is essential for the effective grid management. Over the past years, few studies have been done on the assessment of teleconnections and its possible effects on the long-term wind speed variability in the UAE region. In this study Nonlinear Canonical Correlation Analysis (NLCCA) method is applied to study the relationship between global climate oscillation indices and meteorological variables, with a major emphasis on wind speed and wind direction, of Abu Dhabi, UAE. The wind dataset was obtained from six ground stations. The first mode of NLCCA is capable of capturing the nonlinear mode of the climate indices at different seasons, showing the symmetry between the warm states and the cool states. The strength of the nonlinear canonical correlation between the two sets of variables varies with the lead/lag time. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE) and Mean absolute error (MAE). The results indicated that NLCCA models provide more accurate information about the nonlinear intrinsic behaviour of the dataset of variables than linear CCA model in terms of the correlation and root mean square error. Key words: Nonlinear Canonical Correlation Analysis (NLCCA), Canonical Correlation Analysis, Neural Network, Climate Indices, wind speed, wind direction

Agriculture in West Africa in the Twenty-First Century: Climate Change and Impacts Scenarios, and Potential for Adaptation

PubMed Central

Sultan, Benjamin; Gaetani, Marco

2016-01-01

West Africa is known to be particularly vulnerable to climate change due to high climate variability, high reliance on rain-fed agriculture, and limited economic and institutional capacity to respond to climate variability and change. In this context, better knowledge of how climate will change in West Africa and how such changes will impact crop productivity is crucial to inform policies that may counteract the adverse effects. This review paper provides a comprehensive overview of climate change impacts on agriculture in West Africa based on the recent scientific literature. West Africa is nowadays experiencing a rapid climate change, characterized by a widespread warming, a recovery of the monsoonal precipitation, and an increase in the occurrence of climate extremes. The observed climate tendencies are also projected to continue in the twenty-first century under moderate and high emission scenarios, although large uncertainties still affect simulations of the future West African climate, especially regarding the summer precipitation. However, despite diverging future projections of the monsoonal rainfall, which is essential for rain-fed agriculture, a robust evidence of yield loss in West Africa emerges. This yield loss is mainly driven by increased mean temperature while potential wetter or drier conditions as well as elevated CO2 concentrations can modulate this effect. Potential for adaptation is illustrated for major crops in West Africa through a selection of studies based on process-based crop models to adjust cropping systems (change in varieties, sowing dates and density, irrigation, fertilizer management) to future climate. Results of the cited studies are crop and region specific and no clear conclusions can be made regarding the most effective adaptation options. Further efforts are needed to improve modeling of the monsoon system and to better quantify the uncertainty in its changes under a warmer climate, in the response of the crops to such changes and in the potential for adaptation. PMID:27625660

Agriculture in West Africa in the Twenty-First Century: Climate Change and Impacts Scenarios, and Potential for Adaptation.

PubMed

Sultan, Benjamin; Gaetani, Marco

2016-01-01

West Africa is known to be particularly vulnerable to climate change due to high climate variability, high reliance on rain-fed agriculture, and limited economic and institutional capacity to respond to climate variability and change. In this context, better knowledge of how climate will change in West Africa and how such changes will impact crop productivity is crucial to inform policies that may counteract the adverse effects. This review paper provides a comprehensive overview of climate change impacts on agriculture in West Africa based on the recent scientific literature. West Africa is nowadays experiencing a rapid climate change, characterized by a widespread warming, a recovery of the monsoonal precipitation, and an increase in the occurrence of climate extremes. The observed climate tendencies are also projected to continue in the twenty-first century under moderate and high emission scenarios, although large uncertainties still affect simulations of the future West African climate, especially regarding the summer precipitation. However, despite diverging future projections of the monsoonal rainfall, which is essential for rain-fed agriculture, a robust evidence of yield loss in West Africa emerges. This yield loss is mainly driven by increased mean temperature while potential wetter or drier conditions as well as elevated CO2 concentrations can modulate this effect. Potential for adaptation is illustrated for major crops in West Africa through a selection of studies based on process-based crop models to adjust cropping systems (change in varieties, sowing dates and density, irrigation, fertilizer management) to future climate. Results of the cited studies are crop and region specific and no clear conclusions can be made regarding the most effective adaptation options. Further efforts are needed to improve modeling of the monsoon system and to better quantify the uncertainty in its changes under a warmer climate, in the response of the crops to such changes and in the potential for adaptation.

Climate Research and Seasonal Forecasting for West Africans: Perceptions, Dissemination, and Use?.

NASA Astrophysics Data System (ADS)

Tarhule, Aondover; Lamb, Peter J.

2003-12-01

Beginning in response to the disastrous drought of 1968 73, considerable research and monitoring have focused on the characteristics, causes, predictability, and impacts of West African Soudano Sahel (10° 18°N) rainfall variability and drought. While these efforts have generated substantial information on a range of these topics, very little is known of the extent to which communities, activities at risk, and policy makers are aware of, have access to, or use such information. This situation has prevailed despite Glantz&;s provocative BAMS paper on the use and value of seasonal forecasts for the Sahel more than a quarter century ago. We now provide a systematic reevaluation of these issues based on questionnaire responses of 566 participants (in 13 communities) and 26 organizations in Burkina Faso, Mali, Niger, and Nigeria. The results reveal that rural inhabitants have limited access to climate information, with nongovernmental organizations (NGOs) being the most important source. Moreover, the pathways for information flow are generally weakly connected and informal. As a result, utilization of the results of climate research is very low to nonexistent, even by organizations responsible for managing the effects of climate variability. Similarly, few people have access to seasonal climate forecasts, although the vast majority expressed a willingness to use such information when it becomes available. Those respondents with access expressed great enthusiasm and satisfaction with seasonal forecasts. The results suggest that inhabitants of the Soudano Sahel savanna are keen for changes that improve their ability to cope with climate variability, but the lack of information on alternative courses of action is a major constraint. Our study, thus, essentially leaves unchanged both Glantz&;s negative “tentative conclusion” and more positive “preliminary assessment” of 25 years ago. Specifically, while many of the infrastructural deficiencies and socioeconomic impediments remain, the great yearning for climate information by Soudano Sahalians suggests that the time is finally ripe for fostering increased use. Therefore, a simple model for improved dissemination of climate research and seasonal climate forecast information is proposed. The tragedy is that a quarter century has passed since Glantz&;s clarion call.

Deglacial History of the Ecuadorian Andes and Implication for Climate Variations: Preliminary Results

NASA Astrophysics Data System (ADS)

Hall, M.; Rinterknecht, V. R.; Schaefer, J. M.; Seager, R.; Greene, A.

2004-12-01

Paleoclimate reconstructions are essential for evaluating the future evolution of natural climate variability and for determining climate sensitivity to external forcing. Reconstructing climate conditions from the Last Glacial Maximum (LGM) to the Holocene represents a unique opportunity to understand climate variability from full glacial conditions to modern warm conditions. The primary goal of our project, is to verify if the changes in temperature and precipitation driving the glacier event in the tropics during the well-documented Little Ice Age (LIA), may also account for the glaciations related to the LGM and the late glacial period. This inter-disciplinary project brings together specialists in glacial geology, surface exposure dating, and climate modeling. Our first trip to Ecuador took us to the Papallacta Valley at the rim of the Potrerillos Plateau. We developed detailed maps of the snowline lowering in the valley and took samples in well-exposed sections for radiocarbon dating. We used our maps and the age constraints on the deglacial history of the Papallacta Valley to estimate the possible combinations of changes in climate parameters related to reconstructed snowline variations. This local study represents the first step in a broader project that will cover most of the Ecuadorian Andes. We will also provide direct dating (3He, 10Be, and 36Cl) of the moraine sequences deposited during the retreat of the glaciers during the late Pleistocene. By the time of the project completion we want to evaluate the nature of the driving forces underlying the LGM and the late glacial event in view of the relatively well understood mechanisms behind the termination of the LIA, and we want to compare the produced data to mid- and high- latitude areas in order to evaluate the regional footprint of dimension and timing of glacier response to climate change.

Projected response of an endangered marine turtle population to climate change

NASA Astrophysics Data System (ADS)

Saba, Vincent S.; Stock, Charles A.; Spotila, James R.; Paladino, Frank V.; Tomillo, Pilar Santidrián

2012-11-01

Assessing the potential impacts of climate change on individual species and populations is essential for the stewardship of ecosystems and biodiversity. Critically endangered leatherback turtles in the eastern Pacific Ocean are excellent candidates for such an assessment because their sensitivity to contemporary climate variability has been substantially studied. If incidental fisheries mortality is eliminated, this population still faces the challenge of recovery in a rapidly changing climate. Here we combined an Earth system model, climate model projections assessed by the Intergovernmental Panel on Climate Change and a population dynamics model to estimate a 7% per decade decline in the Costa Rica nesting population over the twenty-first century. Whereas changes in ocean conditions had a small effect on the population, the ~2.5°C warming of the nesting beach was the primary driver of the decline through reduced hatching success and hatchling emergence rate. Hatchling sex ratio did not substantially change. Adjusting nesting phenology or changing nesting sites may not entirely prevent the decline, but could offset the decline rate. However, if future observations show a long-term decline in hatching success and emergence rate, anthropogenic climate mitigation of nests (for example, shading, irrigation) may be able to preserve the nesting population.

Changing Conditions in the Arctic: An Analysis of 45 years of Tropospheric Ozone Measurements at Barrow Observatory

NASA Astrophysics Data System (ADS)

McClure-Begley, A.; Petropavlovskikh, I. V.; Crepinsek, S.; Jefferson, A.; Emmons, L. K.; Oltmans, S. J.

2017-12-01

In order to understand the impact of climate on local bio-systems, understanding the changes to the atmospheric composition and processes in the Arctic boundary layer and free troposphere is imperative. In the Arctic, many conditions influence tropospheric ozone variability such as: seasonal halogen caused depletion events, long range transport of pollutants from mid-northern latitudes, compounds released from wildfires, and different meteorological conditions. The Barrow station in Utqiagvik, Alaska has collected continuous measurements of ground-level ozone since 1973. This unique long-term time series allows for analysis of the influence of a rapidly changing climate on ozone conditions in this region. Specifically, this study analyzes the frequency of enhanced ozone episodes over time and provides in depth analysis of periods of positive deviations from the expected conditions. To discern the contribution of different pollutant sources to observed ozone variability, co-located measurements of aerosols, carbon monoxide, and meteorological conditions are used. In addition, the NCAR Mozart-4/MOPITT Chemical Forecast model and NOAA Hysplit back-trajectory analysis provide information on transport patterns to the Arctic and confirmation of the emission sources that influenced the observed conditions. These anthropogenic influences on ozone variability in and below the boundary layer are essential for developing an understanding of the interaction of climate change and the bio-systems in the Arctic.

Assessing concentration uncertainty estimates from passive microwave sea ice products

NASA Astrophysics Data System (ADS)

Meier, W.; Brucker, L.; Miller, J. A.

2017-12-01

Sea ice concentration is an essential climate variable and passive microwave derived estimates of concentration are one of the longest satellite-derived climate records. However, until recently uncertainty estimates were not provided. Numerous validation studies provided insight into general error characteristics, but the studies have found that concentration error varied greatly depending on sea ice conditions. Thus, an uncertainty estimate from each observation is desired, particularly for initialization, assimilation, and validation of models. Here we investigate three sea ice products that include an uncertainty for each concentration estimate: the NASA Team 2 algorithm product, the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF) product, and the NOAA/NSIDC Climate Data Record (CDR) product. Each product estimates uncertainty with a completely different approach. The NASA Team 2 product derives uncertainty internally from the algorithm method itself. The OSI-SAF uses atmospheric reanalysis fields and a radiative transfer model. The CDR uses spatial variability from two algorithms. Each approach has merits and limitations. Here we evaluate the uncertainty estimates by comparing the passive microwave concentration products with fields derived from the NOAA VIIRS sensor. The results show that the relationship between the product uncertainty estimates and the concentration error (relative to VIIRS) is complex. This may be due to the sea ice conditions, the uncertainty methods, as well as the spatial and temporal variability of the passive microwave and VIIRS products.

Societal resilience to hydroclimatic change in the Roman World

NASA Astrophysics Data System (ADS)

Dermody, Brian; van Beek, Rens; Bierkens, Marc; Dekker, Stefan

2016-04-01

The Romans were masters of water resource management. They employed sophisticated irrigation techniques alongside a highly integrated food redistribution system that provided stable food supplies under the variable hydroclimatic regime within the Roman World. However, a number of paleoclimate studies have demonstrated hydroclimatic changes during the Roman Period that exceeded the amplitude and persistence of normal climate variability. In particular, there was a shift from warmer and more stable hydroclimatic conditions in the Roman Warm Period (c.250 BC - 250 AD) to cooler and more variable conditions in Late Roman Period (after c.250 AD). In this study we use a socio-hydrological model of the Roman world to explore the impact of hydroclimatic changes between the Roman Warm Period and Late Roman Period on the Roman food production and redistribution system. We calculate crop yields based on temperature and water resource availability using PC Raster Global Water Balance model (PCR-GLOBWB). PCR-GLOBWB is forced with reanalysis climate fields reflecting reconstructions of Roman Warm Period to the Late Roman climate patterns. Cropland areas and settlement patterns are derived from a database of 14,700 Roman settlement sites and crop suitability maps. We simulate food redistribution using a multi-agent food redistribution network with link weights based on Orbis: The Stanford Geospatial Network of the Roman World. Our analysis indicates a reduction in crop yields during the Late Roman Period compared with the Roman Warm Period owing to cooler temperatures. In addition, our simulations indicate that increased hydroclimatic variability decreased the stability of yields in the Late Roman period. Crop yields in the Western Empire are simulated to have been impacted most by the change in climate owing to cooler average temperatures and greater hydroclimatic variability compared with the Eastern part of the Empire. The food redistribution network was essential to buffer against lower and less stable yields in the Late Roman Period. However, the Late Roman Period coincided with a breakdown in the food redistribution network, making the Western Roman Empire particularly vulnerable to changing climate conditions. Our analysis demonstrates a number of important processes that have general implications for water resource management in food production and redistribution systems.

Climate change not to blame for late Quaternary megafauna extinctions in Australia

PubMed Central

Saltré, Frédérik; Rodríguez-Rey, Marta; Brook, Barry W.; Johnson, Christopher N; Turney, Chris S. M.; Alroy, John; Cooper, Alan; Beeton, Nicholas; Bird, Michael I.; Fordham, Damien A.; Gillespie, Richard; Herrando-Pérez, Salvador; Jacobs, Zenobia; Miller, Gifford H.; Nogués-Bravo, David; Prideaux, Gavin J.; Roberts, Richard G.; Bradshaw, Corey J. A.

2016-01-01

Late Quaternary megafauna extinctions impoverished mammalian diversity worldwide. The causes of these extinctions in Australia are most controversial but essential to resolve, because this continent-wide event presaged similar losses that occurred thousands of years later on other continents. Here we apply a rigorous metadata analysis and new ensemble-hindcasting approach to 659 Australian megafauna fossil ages. When coupled with analysis of several high-resolution climate records, we show that megafaunal extinctions were broadly synchronous among genera and independent of climate aridity and variability in Australia over the last 120,000 years. Our results reject climate change as the primary driver of megafauna extinctions in the world's most controversial context, and instead estimate that the megafauna disappeared Australia-wide ∼13,500 years after human arrival, with shorter periods of coexistence in some regions. This is the first comprehensive approach to incorporate uncertainty in fossil ages, extinction timing and climatology, to quantify mechanisms of prehistorical extinctions. PMID:26821754

Joint Research Centre Copernicus Climate Change Service (C3S) Fitness-for-Purpose (F4P) Platform

NASA Astrophysics Data System (ADS)

Gobron, N.; Adams, J. S.; Cappucci, F.; Lanconelli, C.; Mota, B.; Melin, F.

2016-08-01

This paper presents the concept and first results of the Copernicus Climate Change Service Fitness-for-Purpose (C3S F4P) project. The main goal aims at evaluating the efficiency and overall performance of the service, mainly with regard to users information needs and high level requirements. This project will also assess the fitness- for-purpose of the C3S with a specific emphasis on the needs of European Union (EU) Policies and translate these recommendations into programmatic and technical requirements. The C3S Climate Data Records (CDS) include various Essential Climate Variables (ECVs) that are derived from space sensors, including from Copernicus Sentinels sensors. One module of the F4P platform focuses on the benchmarking of data sets and algorithms, in addition to radiative transfer models used towards understanding potential discrepancies between CDS records. Methods and preliminary results of the benchmark platform are presented in this contribution.

Elevational differences in developmental plasticity determine phenological responses of grasshoppers to recent climate warming.

PubMed

Buckley, Lauren B; Nufio, César R; Kirk, Evan M; Kingsolver, Joel G

2015-06-22

Annual species may increase reproduction by increasing adult body size through extended development, but risk being unable to complete development in seasonally limited environments. Synthetic reviews indicate that most, but not all, species have responded to recent climate warming by advancing the seasonal timing of adult emergence or reproduction. Here, we show that 50 years of climate change have delayed development in high-elevation, season-limited grasshopper populations, but advanced development in populations at lower elevations. Developmental delays are most pronounced for early-season species, which might benefit most from delaying development when released from seasonal time constraints. Rearing experiments confirm that population, elevation and temperature interact to determine development time. Population differences in developmental plasticity may account for variability in phenological shifts among adults. An integrated consideration of the full life cycle that considers local adaptation and plasticity may be essential for understanding and predicting responses to climate change. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Changing Amazon biomass and the role of atmospheric CO2 concentration, climate, and land use

NASA Astrophysics Data System (ADS)

Almeida Castanho, Andrea D.; Galbraith, David; Zhang, Ke; Coe, Michael T.; Costa, Marcos H.; Moorcroft, Paul

2016-01-01

The Amazon tropical evergreen forest is an important component of the global carbon budget. Its forest floristic composition, structure, and function are sensitive to changes in climate, atmospheric composition, and land use. In this study biomass and productivity simulated by three dynamic global vegetation models (Integrated Biosphere Simulator, Ecosystem Demography Biosphere Model, and Joint UK Land Environment Simulator) for the period 1970-2008 are compared with observations from forest plots (Rede Amazónica de Inventarios Forestales). The spatial variability in biomass and productivity simulated by the DGVMs is low in comparison to the field observations in part because of poor representation of the heterogeneity of vegetation traits within the models. We find that over the last four decades the CO2 fertilization effect dominates a long-term increase in simulated biomass in undisturbed Amazonian forests, while land use change in the south and southeastern Amazonia dominates a reduction in Amazon aboveground biomass, of similar magnitude to the CO2 biomass gain. Climate extremes exert a strong effect on the observed biomass on short time scales, but the models are incapable of reproducing the observed impacts of extreme drought on forest biomass. We find that future improvements in the accuracy of DGVM predictions will require improved representation of four key elements: (1) spatially variable plant traits, (2) soil and nutrients mediated processes, (3) extreme event mortality, and (4) sensitivity to climatic variability. Finally, continued long-term observations and ecosystem-scale experiments (e.g. Free-Air CO2 Enrichment experiments) are essential for a better understanding of the changing dynamics of tropical forests.

The GHG-CCI Project to Deliver the Essential Climate Variable Greenhouse Gases: Current status

NASA Astrophysics Data System (ADS)

Buchwitz, M.; Boesch, H.; Reuter, M.

2012-04-01

The GHG-CCI project (http://www.esa-ghg-cci.org) is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs). The goal of GHG-CCI is to deliver global satellite-derived data sets of the two most important anthropogenic greenhouse gases (GHGs) carbon dioxide (CO2) and methane (CH4) suitable to obtain information on regional CO2 and CH4 surface sources and sinks as needed for better climate prediction. The GHG-CCI core ECV data products are column-averaged mole fractions of CO2 and CH4, XCO2 and XCH4, retrieved from SCIAMACHY on ENVISAT and TANSO on GOSAT. Other satellite instruments will be used to provide constraints in upper layers such as IASI, MIPAS, and ACE-FTS. Which of the advanced algorithms, which are under development, will be the best for a given data product still needs to be determined. For each of the 4 GHG-CCI core data products - XCO2 and XCH4 from SCIAMACHY and GOSAT - several algorithms are bing further developed and the corresponding data products are inter-compared to identify which data product is the most appropriate. This includes comparisons with corresponding data products generated elsewhere, most notably with the operational data products of GOSAT generated at NIES and the NASA/ACOS GOSAT XCO2 product. This activity, the so-called "Round Robin exercise", will be performed in the first two years of this project. At the end of the 2 year Round Robin phase (end of August 2012) a decision will be made which of the algorithms performs best. The selected algorithms will be used to generate the first version of the ECV GHG. In the last six months of this 3 year project the resulting data products will be validated and made available to all interested users. In the presentation and overview about this project will be given focussing on the latest results.

Hydrologic refugia, plants, and climate change.

PubMed

McLaughlin, Blair C; Ackerly, David D; Klos, P Zion; Natali, Jennifer; Dawson, Todd E; Thompson, Sally E

2017-08-01

Climate, physical landscapes, and biota interact to generate heterogeneous hydrologic conditions in space and over time, which are reflected in spatial patterns of species distributions. As these species distributions respond to rapid climate change, microrefugia may support local species persistence in the face of deteriorating climatic suitability. Recent focus on temperature as a determinant of microrefugia insufficiently accounts for the importance of hydrologic processes and changing water availability with changing climate. Where water scarcity is a major limitation now or under future climates, hydrologic microrefugia are likely to prove essential for species persistence, particularly for sessile species and plants. Zones of high relative water availability - mesic microenvironments - are generated by a wide array of hydrologic processes, and may be loosely coupled to climatic processes and therefore buffered from climate change. Here, we review the mechanisms that generate mesic microenvironments and their likely robustness in the face of climate change. We argue that mesic microenvironments will act as species-specific refugia only if the nature and space/time variability in water availability are compatible with the ecological requirements of a target species. We illustrate this argument with case studies drawn from California oak woodland ecosystems. We posit that identification of hydrologic refugia could form a cornerstone of climate-cognizant conservation strategies, but that this would require improved understanding of climate change effects on key hydrologic processes, including frequently cryptic processes such as groundwater flow. © 2017 John Wiley & Sons Ltd.

Assessments of Future Maize Yield Potential Changes in the Korean Peninsula Using Multiple Crop Models

NASA Astrophysics Data System (ADS)

Kim, S. H.; Lim, C. H.; Kim, J.; Lee, W. K.; Kafatos, M.

2016-12-01

The Korean Peninsula has unique agricultural environment due to the differences of political and socio-economical system between Republic of Korea (SK, hereafter) and Democratic Peoples' Republic of Korea (NK, hereafter). NK has been suffering lack of food supplies caused by natural disasters, land degradation and political failure. The neighboring developed country SK has better agricultural system but very low food self-sufficiency rate. Maize is an important crop in both countries since it is staple food for NK and SK is No. 2 maize importing country in the world after Japan. Therefore, evaluating maize yield potential (Yp) in the two distinct regions is essential to assess food security under climate change and variability. In this study, we utilized multiple process-based crop models, having ability of regional scale assessment, to evaluate maize Yp and assess the model uncertainties -EPIC, GEPIC, DSSAT, and APSIM model that has capability of regional scale expansion (apsimRegions). First we evaluated each crop model for 3 years from 2012 to 2014 using reanalysis data (RDAPS; Regional Data Assimilation and Prediction System produced by Korea Meteorological Agency) and observed yield data. Each model performances were compared over the different regions in the Korean Peninsula having different local climate characteristics. To quantify of the major influence of at each climate variables, we also conducted sensitivity test using 20 years of climatology in historical period from 1981 to 2000. Lastly, the multi-crop model ensemble analysis was performed for future period from 2031 to 2050. The required weather variables projected for mid-century were employed from COordinated Regional climate Downscaling EXperiment (CORDEX) East Asia. The high-resolution climate data were obtained from multiple regional climate models (RCM) driven by multiple climate scenarios projected from multiple global climate models (GCMs) in conjunction with multiple greenhouse gas concentration pathways. The results indicate that the projected Yp in the Korean peninsula is significantly changed comparing to the historical period and proper adaptation strategies such as optimized planting dates can considerably alleviate Yp decrease.

Transient Earth system responses to cumulative carbon dioxide emissions: linearities, uncertainties, and probabilities in an observation-constrained model ensemble

NASA Astrophysics Data System (ADS)

Steinacher, M.; Joos, F.

2016-02-01

Information on the relationship between cumulative fossil CO2 emissions and multiple climate targets is essential to design emission mitigation and climate adaptation strategies. In this study, the transient response of a climate or environmental variable per trillion tonnes of CO2 emissions, termed TRE, is quantified for a set of impact-relevant climate variables and from a large set of multi-forcing scenarios extended to year 2300 towards stabilization. An ˜ 1000-member ensemble of the Bern3D-LPJ carbon-climate model is applied and model outcomes are constrained by 26 physical and biogeochemical observational data sets in a Bayesian, Monte Carlo-type framework. Uncertainties in TRE estimates include both scenario uncertainty and model response uncertainty. Cumulative fossil emissions of 1000 Gt C result in a global mean surface air temperature change of 1.9 °C (68 % confidence interval (c.i.): 1.3 to 2.7 °C), a decrease in surface ocean pH of 0.19 (0.18 to 0.22), and a steric sea level rise of 20 cm (13 to 27 cm until 2300). Linearity between cumulative emissions and transient response is high for pH and reasonably high for surface air and sea surface temperatures, but less pronounced for changes in Atlantic meridional overturning, Southern Ocean and tropical surface water saturation with respect to biogenic structures of calcium carbonate, and carbon stocks in soils. The constrained model ensemble is also applied to determine the response to a pulse-like emission and in idealized CO2-only simulations. The transient climate response is constrained, primarily by long-term ocean heat observations, to 1.7 °C (68 % c.i.: 1.3 to 2.2 °C) and the equilibrium climate sensitivity to 2.9 °C (2.0 to 4.2 °C). This is consistent with results by CMIP5 models but inconsistent with recent studies that relied on short-term air temperature data affected by natural climate variability.

A cross-assessment of CCI-ECVs and RCSM simulations over the Mediterranean area

NASA Astrophysics Data System (ADS)

D'Errico, Miriam; Planton, Serge; Nabat, Pierre

2017-04-01

A first objective of this study, conducted in the framework of the Climate Modelling Users Group (CMUG), one of the projects of the European Space Agency Climate Change Initiative (ESA CCI) program, is a cross-assessment of simulations of a Med-CORDEX regional climate system model (CNRM-RCSM5) and a sub-set of atmosphere, marine and surface interrelated Satellite-Derived Essential Climate Variables (CCI-ECVs) (i.e. sea surface temperature, sea level, aerosols and soil moisture content) over the Mediterranean area. The consistency between the model and the CCI-ECVs is evaluated through the analysis of a climate specific event that can be observed with the CCI-ECVs, in atmospheric reanalysis and reproduced in the RCSM simulations. In this presentation we focus on the July 2006 heat wave that affected the western part of the Mediterranean continental and marine area. The application of a spectral nudging method using ERA-Interim reanalysis in our simulation allows to reproduce this event with a proper chronology. As a result we show that the consistency between the simulated model aerosol optical depth and the ECV products (being produced by the ESA Aerosol CCI project consortium) depends on the choice of the algorithm used to infer the variable from the satellite observations. In particular the heat wave main characteristics become consistent between the model and the satellite-derived observations for sea surface temperature, soil moisture and sea level. The link between the atmospheric circulation and the aerosols distribution is also investigated.

Potential of satellite-derived ecosystem functional attributes to anticipate species range shifts

NASA Astrophysics Data System (ADS)

Alcaraz-Segura, Domingo; Lomba, Angela; Sousa-Silva, Rita; Nieto-Lugilde, Diego; Alves, Paulo; Georges, Damien; Vicente, Joana R.; Honrado, João P.

2017-05-01

In a world facing rapid environmental changes, anticipating their impacts on biodiversity is of utmost relevance. Remotely-sensed Ecosystem Functional Attributes (EFAs) are promising predictors for Species Distribution Models (SDMs) by offering an early and integrative response of vegetation performance to environmental drivers. Species of high conservation concern would benefit the most from a better ability to anticipate changes in habitat suitability. Here we illustrate how yearly projections from SDMs based on EFAs could reveal short-term changes in potential habitat suitability, anticipating mid-term shifts predicted by climate-change-scenario models. We fitted two sets of SDMs for 41 plant species of conservation concern in the Iberian Peninsula: one calibrated with climate variables for baseline conditions and projected under two climate-change-scenarios (future conditions); and the other calibrated with EFAs for 2001 and projected annually from 2001 to 2013. Range shifts predicted by climate-based models for future conditions were compared to the 2001-2013 trends from EFAs-based models. Projections of EFAs-based models estimated changes (mostly contractions) in habitat suitability that anticipated, for the majority (up to 64%) of species, the mid-term shifts projected by traditional climate-change-scenario forecasting, and showed greater agreement with the business-as-usual scenario than with the sustainable-development one. This study shows how satellite-derived EFAs can be used as meaningful essential biodiversity variables in SDMs to provide early-warnings of range shifts and predictions of short-term fluctuations in suitable conditions for multiple species.

Developing an approach to effectively use super ensemble experiments for the projection of hydrological extremes under climate change

NASA Astrophysics Data System (ADS)

Watanabe, S.; Kim, H.; Utsumi, N.

2017-12-01

This study aims to develop a new approach which projects hydrology under climate change using super ensemble experiments. The use of multiple ensemble is essential for the estimation of extreme, which is a major issue in the impact assessment of climate change. Hence, the super ensemble experiments are recently conducted by some research programs. While it is necessary to use multiple ensemble, the multiple calculations of hydrological simulation for each output of ensemble simulations needs considerable calculation costs. To effectively use the super ensemble experiments, we adopt a strategy to use runoff projected by climate models directly. The general approach of hydrological projection is to conduct hydrological model simulations which include land-surface and river routing process using atmospheric boundary conditions projected by climate models as inputs. This study, on the other hand, simulates only river routing model using runoff projected by climate models. In general, the climate model output is systematically biased so that a preprocessing which corrects such bias is necessary for impact assessments. Various bias correction methods have been proposed, but, to the best of our knowledge, no method has proposed for variables other than surface meteorology. Here, we newly propose a method for utilizing the projected future runoff directly. The developed method estimates and corrects the bias based on the pseudo-observation which is a result of retrospective offline simulation. We show an application of this approach to the super ensemble experiments conducted under the program of Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI). More than 400 ensemble experiments from multiple climate models are available. The results of the validation using historical simulations by HAPPI indicates that the output of this approach can effectively reproduce retrospective runoff variability. Likewise, the bias of runoff from super ensemble climate projections is corrected, and the impact of climate change on hydrologic extremes is assessed in a cost-efficient way.

Climate-driven shifts in continental net primary production implicated as a driver of a recent abrupt increase in the land carbon sink

NASA Astrophysics Data System (ADS)

Buermann, Wolfgang; Beaulieu, Claudie; Parida, Bikash; Medvigy, David; Collatz, George J.; Sheffield, Justin; Sarmiento, Jorge L.

2016-03-01

The world's ocean and land ecosystems act as sinks for anthropogenic CO2, and over the last half century their combined sink strength grew steadily with increasing CO2 emissions. Recent analyses of the global carbon budget, however, have uncovered an abrupt, substantial ( ˜ 1 PgC yr-1) and sustained increase in the land sink in the late 1980s whose origin remains unclear. In the absence of this prominent shift in the land sink, increases in atmospheric CO2 concentrations since the late 1980s would have been ˜ 30 % larger than observed (or ˜ 12 ppm above current levels). Global data analyses are limited in regards to attributing causes to changes in the land sink because different regions are likely responding to different drivers. Here, we address this challenge by using terrestrial biosphere models constrained by observations to determine if there is independent evidence for the abrupt strengthening of the land sink. We find that net primary production significantly increased in the late 1980s (more so than heterotrophic respiration), consistent with the inferred increase in the global land sink, and that large-scale climate anomalies are responsible for this shift. We identify two key regions in which climatic constraints on plant growth have eased: northern Eurasia experienced warming, and northern Africa received increased precipitation. Whether these changes in continental climates are connected is uncertain, but North Atlantic climate variability is important. Our findings suggest that improved understanding of climate variability in the North Atlantic may be essential for more credible projections of the land sink under climate change.

Evaluating the patterns of spatiotemporal trends of root zone soil moisture in major climate regions in East Asia

NASA Astrophysics Data System (ADS)

Zohaib, Muhammad; Kim, Hyunglok; Choi, Minha

2017-08-01

Root zone soil moisture (RZSM) is a crucial variable in land-atmosphere interactions. Evaluating the spatiotemporal trends and variability patterns of RZSM are essential for discerning the anthropogenic and climate change effects on the regional and global hydrological cycles. In this study, the trends of RZSM, computed by the exponential filter from the European Space Agency's Climate Change Initiative soil moisture, were evaluated in major climate regions of East Asia from 1982 to 2014. Moreover, the trends of RZSM were compared to the trends of precipitation (P), skin temperature (Tskin), and actual evapotranspiration (AET) to investigate how they influence the RZSM trends in each climate region. Drying trends were predominant in arid and continental regions, whereas wetting trends were found in the tropical and temperate regions. The increasing trends of Tskin and AET cause drying in arid and continental regions, whereas in tropical regions, these cause wetting trends, which might be due to convective P. In temperate regions, despite decreasing P and increasing Tskin, the RZSM trend was increasing, attributed to the intensive irrigation activities in these regions. This is probably the first time to analyze the long-term trends of RZSM in different climate regions. Hence, the results of this study will improve our understanding of the regional and global hydrological cycles. Despite certain limitations, the results of this study may be useful for improving and developing climate models and predicting long-term vast scale natural disasters such as drought, dust outbreaks, floods, and heat waves.

Earth system responses to cumulative carbon emissions

NASA Astrophysics Data System (ADS)

Steinacher, M.; Joos, F.

2015-07-01

Information on the relationship between cumulative fossil carbon emissions and multiple climate targets are essential to design emission mitigation and climate adaptation strategies. In this study, the transient responses in different climate variables are quantified for a large set of multi-forcing scenarios extended to year 2300 towards stabilization and in idealized experiments using the Bern3D-LPJ carbon-climate model. The model outcomes are constrained by 26 physical and biogeochemical observational data sets in a Bayesian, Monte-Carlo type framework. Cumulative fossil emissions of 1000 Gt C result in a global mean surface air temperature change of 1.88 °C (68 % confidence interval (c.i.): 1.28 to 2.69 °C), a decrease in surface ocean pH of 0.19 (0.18 to 0.22), and in steric sea level rise of 20 cm (13 to 27 cm until 2300). Linearity between cumulative emissions and transient response is high for pH and reasonably high for surface air and sea surface temperatures, but less pronounced for changes in Atlantic Meridional Overturning, Southern Ocean and tropical surface water saturation with respect to biogenic structures of calcium carbonate, and carbon stocks in soils. The slopes of the relationships change when CO2 is stabilized. The Transient Climate Response is constrained, primarily by long-term ocean heat observations, to 1.7 °C (68 % c.i.: 1.3 to 2.2 °C) and the Equilibrium Climate Sensitivity to 2.9 °C (2.0 to 4.2 °C). This is consistent with results by CMIP5 models, but inconsistent with recent studies that relied on short-term air temperature data affected by natural climate variability.

A global reconstruction of climate-driven subdecadal water storage variability

NASA Astrophysics Data System (ADS)

Humphrey, V.; Gudmundsson, L.; Seneviratne, S. I.

2017-03-01

Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission has provided unprecedented observations of global mass redistribution caused by hydrological processes. However, there are still few sources on pre-2002 global terrestrial water storage (TWS). Classical approaches to retrieve past TWS rely on either land surface models (LSMs) or basin-scale water balance calculations. Here we propose a new approach which statistically relates anomalies in atmospheric drivers to monthly GRACE anomalies. Gridded subdecadal TWS changes and time-dependent uncertainty intervals are reconstructed for the period 1985-2015. Comparisons with model results demonstrate the performance and robustness of the derived data set, which represents a new and valuable source for studying subdecadal TWS variability, closing the ocean/land water budgets and assessing GRACE uncertainties. At midpoint between GRACE observations and LSM simulations, the statistical approach provides TWS estimates (doi:10.5905/ethz-1007-85) that are essentially derived from observations and are based on a limited number of transparent model assumptions.

Influence of weather and climate on subjective symptom intensity in atopic eczema

NASA Astrophysics Data System (ADS)

Vocks, E.; Busch, R.; Fröhlich, C.; Borelli, S.; Mayer, H.; Ring, J.

The frequent clinical observation that the course of atopic eczema, a skin disease involving a disturbed cutaneous barrier function, is influenced by climate and weather motivated us to analyse these relationships biometrically. In the Swiss high-mountain area of Davos the intensity of itching experienced by patients with atopic eczema was evaluated and compared to 15 single meteorological variables recorded daily during an entire 7-year observation period. By means of univariate analyses and multiple regressions, itch intensity was found to be correlated with some meteorological variables. A clear-cut inverse correlation exists with air temperature (coefficient of correlation: -0.235, P<0.001), but the effects of water vapour pressure, air pressure and hours of sunshine are less pronounced. The results show that itching in atopic eczema is significantly dependent on meteorological conditions. The data suggest that, in patients with atopic eczema, a certain range of thermo-hygric atmospheric conditions with a balance of heat and water loss on the skin surface is essential for the skin to feel comfortable.

Climate variation explains a third of global crop yield variability

PubMed Central

Ray, Deepak K.; Gerber, James S.; MacDonald, Graham K.; West, Paul C.

2015-01-01

Many studies have examined the role of mean climate change in agriculture, but an understanding of the influence of inter-annual climate variations on crop yields in different regions remains elusive. We use detailed crop statistics time series for ~13,500 political units to examine how recent climate variability led to variations in maize, rice, wheat and soybean crop yields worldwide. While some areas show no significant influence of climate variability, in substantial areas of the global breadbaskets, >60% of the yield variability can be explained by climate variability. Globally, climate variability accounts for roughly a third (~32–39%) of the observed yield variability. Our study uniquely illustrates spatial patterns in the relationship between climate variability and crop yield variability, highlighting where variations in temperature, precipitation or their interaction explain yield variability. We discuss key drivers for the observed variations to target further research and policy interventions geared towards buffering future crop production from climate variability. PMID:25609225

Tool kit development to refine and visualize essential climate data and information for marine protected areas

NASA Astrophysics Data System (ADS)

Cecil, L.; Stachniewicz, J.; Shein, K. A.; Ansari, S.; Jarvis, C.

2013-05-01

Marine ecosystem responses to climate variability and change such as changing water temperature, water chemistry (e.g., pH, salinity), water level, or storminess may result in adverse impacts including mass mortality, loss of habitat, increased disease susceptibility, and trophic cascade feedbacks. Unfortunately, while marine ecosystem resource managers are aware of these threats, they often lack sufficient expertise with identifying, accessing and using the many large and complex climate data products that would inform ecosystem-scale climate impact assessments. NOAA's National Climatic Data Center (NCDC) has been working with the Gulf of the Farallones National Marine Sanctuary Ocean Climate Center to enhance and expand the functionality of NCDC's Weather and Climate Toolkit (WCT) to begin to address this limitation. The WCT is a freely available, Java-based user interface (http://www.ncdc.noaa.gov/oa/wct/) designed to access, analyze, and display a variety of NCDC's georeferenced climate data products (e.g., satellite data, radar, reanalysis datasets, in-situ observations). However, the WCT requires the user to have already identified a data set of interest and gained access to it. This can limit its utility by users who are not knowledgeable about which data sets are relevant to their needs and where those data sets can be found. The Integrated Marine Protected Area Climate Tools (IMPACT) prototype modification to the WCT addresses those requirements through an iterative process between climate scientists and resource managers. The WCT-IMPACT prototype couples a user query approach with a quasi-expert system that determines, retrieves, and loads the appropriate data products for visualization and analysis by the user. Relevant data products are identified based on the environmental variables in which ecosystem managers have indicated an importance to their ecosystems. To improve response time, the user, through the WCT-IMPACT interface, crops (or subsets) the larger gridded data products, such as NOAA's satellite Climate Data Records to the geographic boundaries of each included marine protected area (MPA). These clipped data sets are processed to produce MPA-specific analytics (e.g., files for averages, extremes, peaks over threshold, etc). Once a specific MPA has been selected, the associated data may be visualized, analyzed, and exported to other formats (e.g., netCDF, KML) from within the tool. The WCT-IMPACT tool kit will provide marine ecosystem managers with the capacity to answer such questions as what was the climate like during periods of optimal ecological health, or have climate conditions changed equally across an ecosystem's domain? The WCT-IMPACT extension is being developed specifically to address the needs of marine ecosystem managers to have access to relevant climate data and information for developing ecosystem-scale climate assessments, while retaining the ability for a WCT user to identify and access the full suite of georeferenced climate data provided by NCDC. In this tool kit development scheme, the need to coordinate with the resource managers is paramount and end user participation in an iterative process with the climate scientists is essential.

The Interplay of Internal and Forced Modes of Hadley Cell Expansion: Lessons from the Global Warming Hiatus

NASA Astrophysics Data System (ADS)

Amaya, D. J.; Siler, N.; Xie, S. P.; Miller, A. J.

2017-12-01

The poleward branches of the Hadley Cells show a robust shift poleward shift during the satellite era, leading to concerns over the possible encroachment of the globe's subtropical dry zones into currently temperate climates. The extent to which this trend is caused by anthropogenic forcing versus internal variability remains the subject of considerable debate. In this study, we us a joint EOF method to identify two distinct modes of Hadley Cell variability: (i) an anthropogenically-forced mode, which we identify using a 20-member simulation of the historical climate, and (ii) an internal mode, which identify using a 1000-year pre-industrial control simulation with a global climate model. The forced mode is found to be closely related to the TOA radiative imbalance and exhibits a long-term trend since 1860, while the internal mode is found to be essentially indistinguishable from the El Niño Southern Oscillation (ENSO). Together these two modes explain an average of 70% of the interannual variability seen in model "edge indices" over the historical period. Since 1980, the superposition of forced and internal modes has resulted in a period of accelerated Hadley Cell expansion and decelerated global warming (i.e., the "hiatus"). A comparison of the change in these modes since 1980 indicates that by 2013 the signal has emerged above the noise of internal variability in the Southern Hemisphere (SH), but not in the Northern Hemisphere (NH), with the latter also exhibiting strong zonal asymmetry, particularly in the North Atlantic. Our results highlight the important interplay of internal and forced modes of Hadley Cell width change and improve our understanding of the interannual variability and long-term trend seen in observations.

A network-based approach for semi-quantitative knowledge mining and its application to yield variability

NASA Astrophysics Data System (ADS)

Schauberger, Bernhard; Rolinski, Susanne; Müller, Christoph

2016-12-01

Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. A systematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.

Complexity in Climatic Controls on Plant Species Distribution: Satellite Data Reveal Unique Climate for Giant Sequoia in the California Sierra Nevada

NASA Astrophysics Data System (ADS)

Waller, Eric Kindseth

A better understanding of the environmental controls on current plant species distribution is essential if the impacts of such diverse challenges as invasive species, changing fire regimes, and global climate change are to be predicted and important diversity conserved. Climate, soil, hydrology, various biotic factors fire, history, and chance can all play a role, but disentangling these factors is a daunting task. Increasingly sophisticated statistical models relying on existing distributions and mapped climatic variables, among others, have been developed to try to answer these questions. Any failure to explain pattern with existing mapped climatic variables is often taken as a referendum on climate as a whole, rather than on the limitations of the particular maps or models. Every location has a unique and constantly changing climate so that any distribution could be explained by some aspect of climate. Chapter 1 of this dissertation reviews some of the major flaws in species distribution modeling and addresses concerns that climate may therefore not be predictive of, or even relevant to, species distributions. Despite problems with climate-based models, climate and climate-derived variables still have substantial merit for explaining species distribution patterns. Additional generation of relevant climate variables and improvements in other climate and climate-derived variables are still needed to demonstrate this more effectively. Satellite data have a long history of being used for vegetation mapping and even species distribution mapping. They have great potential for being used for additional climatic information, and for improved mapping of other climate and climate-derived variables. Improving the characterization of cloud cover frequency with satellite data is one way in which the mapping of important climate and climate-derived variables can be improved. An important input to water balance models, solar radiation maps could be vastly improved with a better mapping of spatial and temporal patterns in cloud cover. Chapter 2 of this dissertation describes the generation of custom daily cloud cover maps from Advanced Very High Resolution Radiometer (AVHRR) satellite data from 1981-1999 at ~5 km resolution and Moderate Resolution Imagine Spectroradiomter (MODIS) satellite reflectance data at ~500 meter resolution for much of the western U.S., from 2000 to 2012. Intensive comparisons of reflectance spectra from a variety of cloud and snow-covered scenes from the southwestern United States allowed the generation of new rules for the classification of clouds and snow in both the AVHRR and MODIS data. The resulting products avoid many of the problems that plague other cloud mapping efforts, such as the tendency for snow cover and bright desert soils to be mapped as cloud. This consistency in classification across cover types is critically important for any distribution modeling of a plant species that might be dependent on cloud cover. In Chapter 3, monthly cloud frequencies derived from the daily classifications were used directly in species distribution models for giant sequoia and were found to be the strongest predictors of giant sequoia distribution. A high frequency of cloud cover, especially in the spring, differentiated the climate of the west slope of the southern Sierra Nevada, where giant sequoia are prolific, from central and northern parts of the range, where the tree is rare and generally absent. Other mapped cloud products, contaminated by confusion with high elevation snow, would likely not have found this important result. The result illustrates the importance of accuracy in mapping as well as the importance of previously overlooked aspects of climate for species distribution modeling. But it also raises new questions about why the clouds form where they do and whether they might be associated with other aspects of climate important to giant sequoia distribution. What are the exact climatic mechanisms governing the distribution? Detailed aspects of the local climate warranted more investigation. Chapter 4 investigates the climate associated with the frequent cloud formation over the western slopes of the southern Sierra Nevada: the "sequoia belt". This region is climatically distinct in a number of ways, all of which could be factors in influencing the distribution of giant sequoia and other species. Satellite and micrometeorological flux tower data reveal characteristics of the sequoia belt that were not evident with surface climate measurements and maps derived from them. Results have implications for species distributions everywhere, but especially in rugged mountains, where climates are complex and poorly mapped. Chapter 5 summarizes some of the main conclusions from the work and suggests directions for related future research. (Abstract shortened by UMI.).

Farmers' Perceptions of Climate Variability and Factors Influencing Adaptation: Evidence from Anhui and Jiangsu, China

NASA Astrophysics Data System (ADS)

Kibue, Grace Wanjiru; Liu, Xiaoyu; Zheng, Jufeng; zhang, Xuhui; Pan, Genxing; Li, Lianqing; Han, Xiaojun

2016-05-01

Impacts of climate variability and climate change are on the rise in China posing great threat to agriculture and rural livelihoods. Consequently, China is undertaking research to find solutions of confronting climate change and variability. However, most studies of climate change and variability in China largely fail to address farmers' perceptions of climate variability and adaptation. Yet, without an understanding of farmers' perceptions, strategies are unlikely to be effective. We conducted questionnaire surveys of farmers in two farming regions, Yifeng, Jiangsu and Qinxi, Anhui achieving 280 and 293 responses, respectively. Additionally, we used climatological data to corroborate the farmers' perceptions of climate variability. We found that farmers' were aware of climate variability such that were consistent with climate records. However, perceived impacts of climate variability differed between the two regions and were influenced by farmers' characteristics. In addition, the vast majorities of farmers were yet to make adjustments in their farming practices as a result of numerous challenges. These challenges included socioeconomic and socio-cultural barriers. Results of logit modeling showed that farmers are more likely to adapt to climate variability if contact with extension services, frequency of seeking information, household heads' education, and climate variability perceptions are improved. These results suggest the need for policy makers to understand farmers' perceptions of climate variability and change in order to formulate policies that foster adaptation, and ultimately protect China's agricultural assets.

Farmers' Perceptions of Climate Variability and Factors Influencing Adaptation: Evidence from Anhui and Jiangsu, China.

PubMed

Kibue, Grace Wanjiru; Liu, Xiaoyu; Zheng, Jufeng; Zhang, Xuhui; Pan, Genxing; Li, Lianqing; Han, Xiaojun

2016-05-01

Impacts of climate variability and climate change are on the rise in China posing great threat to agriculture and rural livelihoods. Consequently, China is undertaking research to find solutions of confronting climate change and variability. However, most studies of climate change and variability in China largely fail to address farmers' perceptions of climate variability and adaptation. Yet, without an understanding of farmers' perceptions, strategies are unlikely to be effective. We conducted questionnaire surveys of farmers in two farming regions, Yifeng, Jiangsu and Qinxi, Anhui achieving 280 and 293 responses, respectively. Additionally, we used climatological data to corroborate the farmers' perceptions of climate variability. We found that farmers' were aware of climate variability such that were consistent with climate records. However, perceived impacts of climate variability differed between the two regions and were influenced by farmers' characteristics. In addition, the vast majorities of farmers were yet to make adjustments in their farming practices as a result of numerous challenges. These challenges included socioeconomic and socio-cultural barriers. Results of logit modeling showed that farmers are more likely to adapt to climate variability if contact with extension services, frequency of seeking information, household heads' education, and climate variability perceptions are improved. These results suggest the need for policy makers to understand farmers' perceptions of climate variability and change in order to formulate policies that foster adaptation, and ultimately protect China's agricultural assets.

The GCOS Reference Upper-Air Network (GRUAN)

NASA Astrophysics Data System (ADS)

Vömel, H.; Berger, F. H.; Immler, F. J.; Seidel, D.; Thorne, P.

2009-04-01

While the global upper-air observing network has provided useful observations for operational weather forecasting for decades, its measurements lack the accuracy and long-term continuity needed for understanding climate change. Consequently, the scientific community faces uncertainty on such key issues as the trends of temperature in the upper troposphere and stratosphere or the variability and trends of stratospheric water vapour. To address these shortcomings, and to ensure that future climate records will be more useful than the records to date, the Global Climate Observing System (GCOS) program initiated the GCOS Reference Upper Air Network (GRUAN). GRUAN will be a network of about 30-40 observatories with a representative sampling of geographic regions and surface types. These stations will provide upper-air reference observations of the essential climate variables, i.e. temperature, geopotential, humidity, wind, radiation and cloud properties using specialized radiosondes and complementary remote sensing profiling instrumentation. Long-term stability, quality assurance / quality control, and a detailed assessment of measurement uncertainties will be the key aspects of GRUAN observations. The network will not be globally complete but will serve to constrain and adjust data from more spatially comprehensive global observing systems including satellites and the current radiosonde networks. This paper outlines the scientific rationale for GRUAN, its role in the Global Earth Observation System of Systems, network requirements and likely instrumentation, management structure, current status and future plans.

Climate Observations from Space

NASA Astrophysics Data System (ADS)

Briggs, Stephen

2016-07-01

The latest Global Climate Observing System (GCOS) Status Report on global climate observations, delivered to the UNFCCC COP21 in November 2016, showed how satellite data are critical for observations relating to climate. Of the 50 Essential Climate Variables (ECVs) identified by GCOS as necessary for understanding climate change, about half are derived only from satellite data while half of the remainder have a significant input from satellites. Hence data from Earth observing satellite systems are now a fundamental requirement for understanding the climate system and for managing the consequences of climate change. Following the Paris Agreement of COP21 this need is only greater. Not only will satellites have to continue to provide data for modelling and predicting climate change but also for a much wider range of actions relating to climate. These include better information on loss and damage, resilience, improved adaptation to change, and on mitigation including information on greenhouse gas emissions. In addition there is an emerging need for indicators of the risks associated with future climate change which need to be better quantified, allowing policy makers both to understand what decisions need to be taken, and to see the consequences of their actions. The presentation will set out some of the ways in which satellite data are important in all aspects of understanding, managing and predicting climate change and how they may be used to support future decisions by those responsible for policy related to managing climate change and its consequences.

High-resolution IP25-based reconstruction of sea-ice variability in the western North Pacific and Bering Sea during the past 18,000 years

NASA Astrophysics Data System (ADS)

Méheust, Marie; Stein, Ruediger; Fahl, Kirsten; Max, Lars; Riethdorf, Jan-Rainer

2016-04-01

Due to its strong influence on heat and moisture exchange between the ocean and the atmosphere, sea ice is an essential component of the global climate system. In the context of its alarming decrease in terms of concentration, thickness and duration, understanding the processes controlling sea-ice variability and reconstructing paleo-sea-ice extent in polar regions have become of great interest for the scientific community. In this study, for the first time, IP25, a recently developed biomarker sea-ice proxy, was used for a high-resolution reconstruction of the sea-ice extent and its variability in the western North Pacific and western Bering Sea during the past 18,000 years. To identify mechanisms controlling the sea-ice variability, IP25 data were associated with published sea-surface temperature as well as diatom and biogenic opal data. The results indicate that a seasonal sea-ice cover existed during cold periods (Heinrich Stadial 1 and Younger Dryas), whereas during warmer intervals (Bølling-Allerød and Holocene) reduced sea ice or ice-free conditions prevailed in the study area. The variability in sea-ice extent seems to be linked to climate anomalies and sea-level changes controlling the oceanographic circulation between the subarctic Pacific and the Bering Sea, especially the Alaskan Stream injection though the Aleutian passes.

Informing Adaptation Decisions: What Do We Need to Know and What Do We Need to Do?

NASA Astrophysics Data System (ADS)

Pulwarty, R. S.; Webb, R. S.

2014-12-01

The demand for improved climate knowledge and information is well documented. As noted in the IPCC Reports (SREX, AR5) and other assessments, this demand has increased pressure for better information to support planning under changing rates of extremes event occurrence. This demand has focused on mechanisms used to respond to past variability and change, including, integrated resource management (watersheds, coasts), infrastructure design, information systems, technological optimization, financial risk management, and behavioral and institutional change. Climate inputs range from static site design statistics (return periods) to dynamic, emergent thresholds and transitions preceded by steep response curves and punctuated equilibria. Tradeoffs are evident in the use of risk-based anticipatory strategies vs. resilience measures. In such settings, annual decision calendars for operational requirements can confound adaptation expectations. Key knowledge assessment questions include: (1) How predictable are potential impacts of events in the context of other stressors, (2) how is action to anticipate such impacts informed, and (3) How often should criteria for "robustness" be reconsidered? To illustrate, we will discuss the climate information needs and uses for two areas of concern for both short and long-term risks (i) climate and disaster risk financing, and (ii) watershed management. The presentation will focus on the climate information needed for (1) improved monitoring, modeling and methods for understanding and analyzing exposure risks, (2) generating risk profiles, (3) developing information systems and scenarios for critical thresholds across climate time and space scales, (4) embedding annual decision calendars in the context of longer-term risk management, (5) gaming experiments to show the net benefits of new information. We will conclude with a discussion of the essential climate variables needed to implement services-delivery and development efforts such as the Global Framework for Climate Services and the Pilot Program on Climate Resilience.

Holocene climate variability in the western Mediterranean through a multiproxy analysis from Padul peat bog (Sierra Nevada, Spain)

NASA Astrophysics Data System (ADS)

Ramos-Román, María J.; Jiménez-Moreno, Gonzalo; Camuera, Jon; García-Alix, Antonio; Anderson, R. Scott; Jiménez-Espejo, Francisco J.; Sachse, Dirk

2017-04-01

The Iberian Peninsula, located in the Mediterranean area, is an interesting location for paleoclimate studies due to its geographic situation between arid and humid climates. Sediments from peat bogs and lakes from Sierra Nevada, in southeastern Iberian Peninsula, have been very informative in terms of how vegetation and wetland environments were impacted by Holocene climate change. These studies are essential if we want to understand the past climate change in the area, which is the key to identify the possible environmental response of the Sierra Nevada ecosystems to future climate scenarios. Padul basin, located in the southwest of the Sierra Nevada mountain range, contains a ca. 100 m-thick peat bog sedimentary sequence that was deposited during the past 1 Ma making this area interesting for paleoenvironmental and paleoclimatic reconstructions. A new 43 m-long sedimentary record has recently been retrieved from the Padul peat bog. In this study we have developed a multiproxy analysis of the Holocene part of the Padul-15-05 core including pollen analysis, XRF-core scanner, magnetic susceptibility and organic geochemistry, supported by an age control based on AMS radiocarbon dates, providing with information about vegetation and climate variability during the past 9.9 cal ka BP. This multiproxy reconstruction of the Padul-15-05 evidences the Mediterranean as a sensitive area with respect to global-scale climate system, showing relevant climate episodes such as the ca. 8, 7.5, 6.5 and 5.5 cal ka BP events during the early and middle Holocene. The trend to aridification to the late Holocene is interrupted by more arid and humid periods as the Iberian Roman Humid Period (from ca. 3 to 1.6 cal ka BP), the Dark Ages (from ca. 1.5 to 1.1 cal ka BP), the Medieval Climate Anomaly (from ca. 1.1 to 1.3 cal ka BP) and the Little Ice Age period (from ca. 500 to 100 cal yr BP).

A plant’s perspective of extremes: Terrestrial plant responses to changing climatic variability

PubMed Central

Reyer, C.; Leuzinger, S.; Rammig, A.; Wolf, A.; Bartholomeus, R. P.; Bonfante, A.; de Lorenzi, F.; Dury, M.; Gloning, P.; Abou Jaoudé, R.; Klein, T.; Kuster, T. M.; Martins, M.; Niedrist, G.; Riccardi, M.; Wohlfahrt, G.; de Angelis, P.; de Dato, G.; François, L.; Menzel, A.; Pereira, M.

2013-01-01

We review observational, experimental and model results on how plants respond to extreme climatic conditions induced by changing climatic variability. Distinguishing between impacts of changing mean climatic conditions and changing climatic variability on terrestrial ecosystems is generally underrated in current studies. The goals of our review are thus (1) to identify plant processes that are vulnerable to changes in the variability of climatic variables rather than to changes in their mean, and (2) to depict/evaluate available study designs to quantify responses of plants to changing climatic variability. We find that phenology is largely affected by changing mean climate but also that impacts of climatic variability are much less studied but potentially damaging. We note that plant water relations seem to be very vulnerable to extremes driven by changes in temperature and precipitation and that heatwaves and flooding have stronger impacts on physiological processes than changing mean climate. Moreover, interacting phenological and physiological processes are likely to further complicate plant responses to changing climatic variability. Phenological and physiological processes and their interactions culminate in even more sophisticated responses to changing mean climate and climatic variability at the species and community level. Generally, observational studies are well suited to study plant responses to changing mean climate, but less suitable to gain a mechanistic understanding of plant responses to climatic variability. Experiments seem best suited to simulate extreme events. In models, temporal resolution and model structure are crucial to capture plant responses to changing climatic variability. We highlight that a combination of experimental, observational and /or modeling studies have the potential to overcome important caveats of the respective individual approaches. PMID:23504722

Arctic sea ice trends, variability and implications for seasonal ice forecasting

PubMed Central

Serreze, Mark C.; Stroeve, Julienne

2015-01-01

September Arctic sea ice extent over the period of satellite observations has a strong downward trend, accompanied by pronounced interannual variability with a detrended 1 year lag autocorrelation of essentially zero. We argue that through a combination of thinning and associated processes related to a warming climate (a stronger albedo feedback, a longer melt season, the lack of especially cold winters) the downward trend itself is steepening. The lack of autocorrelation manifests both the inherent large variability in summer atmospheric circulation patterns and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend. These findings have implications for seasonal ice forecasting. In particular, while advances in observing sea ice thickness and assimilating thickness into coupled forecast systems have improved forecast skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability. PMID:26032315

Predicting population survival under future climate change: density dependence, drought and extraction in an insular bighorn sheep.

PubMed

Colchero, Fernando; Medellin, Rodrigo A; Clark, James S; Lee, Raymond; Katul, Gabriel G

2009-05-01

1. Our understanding of the interplay between density dependence, climatic perturbations, and conservation practices on the dynamics of small populations is still limited. This can result in uninformed strategies that put endangered populations at risk. Moreover, the data available for a large number of populations in such circumstances are sparse and mined with missing data. Under the current climate change scenarios, it is essential to develop appropriate inferential methods that can make use of such data sets. 2. We studied a population of desert bighorn sheep introduced to Tiburon Island, Mexico in 1975 and subjected to irregular extractions for the last 10 years. The unique attributes of this population are absence of predation and disease, thereby permitting us to explore the combined effect of density dependence, environmental variability and extraction in a 'controlled setting.' Using a combination of nonlinear discrete models with long-term field data, we constructed three basic Bayesian state space models with increasing density dependence (DD), and the same three models with the addition of summer drought effects. 3. We subsequently used Monte Carlo simulations to evaluate the combined effect of drought, DD, and increasing extractions on the probability of population survival under two climate change scenarios (based on the Intergovernmental Panel on Climate Change predictions): (i) increase in drought variability; and (ii) increase in mean drought severity. 4. The population grew from 16 individuals introduced in 1975 to close to 700 by 1993. Our results show that the population's growth was dominated by DD, with drought having a secondary but still relevant effect on its dynamics. 5. Our predictions suggest that under climate change scenario (i), extraction dominates the fate of the population, while for scenario (ii), an increase in mean drought affects the population's probability of survival in an equivalent magnitude as extractions. Thus, for the long-term survival of the population, our results stress that a more variable environment is less threatening than one in which the mean conditions become harsher. Current climate change scenarios and their underlying uncertainty make studies such as this one crucial for understanding the dynamics of ungulate populations and their conservation.

Climate during the Roman and early-medieval periods in North-western Europe: a review of climate reconstructions from terrestrial archives

NASA Astrophysics Data System (ADS)

Reichelmann, Dana F. C.; Gouw-Bouman, Marjolein T. I. J.; Hoek, Wim Z.; van Lanen, Rowin J.; Stouthamer, Esther; Jansma, Esther

2016-04-01

High-resolution palaeoclimate reconstructions are essential to identify possible influences of climate variability on landscape evolution and landscape-related cultural changes (e.g., shifting settlement patterns and long-distance trade relations). North-western Europe is an ideal research area for comparison between climate variability and cultural transitions given its geomorphological diversity and the significant cultural changes that took place in this region during the last two millennia (e.g., the decline of the Roman Empire and the transition to medieval kingdoms). Compared to more global climate records, such as ice cores and marine sediments, terrestrial climate proxies have the advantage of representing a relatively short response time to regional climatic change. Furthermore for this region large quantity of climate reconstructions is available covering the last millennium, whereas for the first millennium AD only few high resolution climate reconstructions are available. We compiled climate reconstructions for sites in North-western Europe from the literature and its underlying data. All these reconstructions cover the time period of AD 1 to 1000. We only selected data with an annual to decadal resolution and a minimum resolution of 50 years. This resulted in 18 climate reconstructions from different archives such as chironomids (1), pollen (4), Sphagnum cellulose (1), stalagmites (6), testate amoebae (4), and tree-rings (2). The compilation of the different temperature reconstructions shows similar trends in most of the records. Colder conditions since AD 300 for a period of approximately 400 years and warmer conditions after AD 700 become apparent. A contradicting signal is found before AD 300 with warmer conditions indicated by most of the records but not all. This is likely the result of the use of different proxies, reflecting temperatures linked to different seasons. The compilation of the different precipitation reconstructions also show similar trends. Dry periods are indicated by all records around AD 400 and 600, although precipitation records do not show the same spatial continuity as the temperature proxies. This study shows that clear climate changes occurred over North-western Europe in the period between AD 300 and 700, which are partly reflected by changes in seasonality.

Essential climatic variables estimation with satellite imagery

NASA Astrophysics Data System (ADS)

Kolotii, A.; Kussul, N.; Shelestov, A.; Lavreniuk, M. S.

2016-12-01

According to Sendai Framework for Disaster Risk Reduction 2015 - 2030 Leaf Area Index (LAI) is considered as one of essential climatic variables. This variable represents the amount of leaf material in ecosystems and controls the links between biosphere and atmosphere through various processes and enables monitoring and quantitative assessment of vegetation state. LAI has added value for such important global resources monitoring tasks as drought mapping and crop yield forecasting with use of data from different sources [1-2]. Remote sensing data from space can be used to estimate such biophysical parameter at regional and national scale. High temporal satellite imagery is usually required to capture main parameters of crop growth [3]. Sentinel-2 mission launched in 2015 be ESA is a source of high spatial and temporal resolution satellite imagery for mapping biophysical parameters. Products created with use of automated Sen2-Agri system deployed during Sen2-Agri country level demonstration project for Ukraine will be compared with our independent results of biophysical parameters mapping. References Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., & Kostetsky, O. (2015, July). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1733-1736 Kolotii, A., Kussul, N., Shelestov, A., Skakun, S., Yailymov, B., Basarab, R., ... & Ostapenko, V. (2015). Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 39-44. Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (6), 2500-2508.

Energy Literacy: A Natural and Essential Part of a Solutions-Based Approach to Climate Literacy

NASA Astrophysics Data System (ADS)

Inman, M. M.

2011-12-01

As with climate science topics, many Americans have misconceptions or gaps in understanding related to energy topics. Recent literacy efforts are geared to address these gaps in understanding. The U.S. Global Change Research Program's recently published "Energy Literacy: Essential Principles and Fundamental Concepts for Energy Education" offers a welcome complement to the Climate Literacy Essential Principles released in 2008. Research and experience suggest that education, communication and outreach about global climate change and related topics is best done using a solutions-based approach. Energy is a natural and effective topic to frame these solutions around. Used as a framework for designing curricula, Energy Literacy naturally leads to solutions-based approaches to Climate Change education. An inherently interdisciplinary topic, energy education must happen in the context of both the natural and social sciences. The Energy Literacy Essential Principles reflect this and open the door to curriculum that integrates the two.

Assessments of Maize Yield Potential in the Korean Peninsula Using Multiple Crop Models

NASA Astrophysics Data System (ADS)

Kim, S. H.; Myoung, B.; Lim, C. H.; Lee, S. G.; Lee, W. K.; Kafatos, M.

2015-12-01

The Korean Peninsular has unique agricultural environments due to the differences in the political and socio-economical systems between the Republic of Korea (SK, hereafter) and the Democratic Peoples' Republic of Korea (NK, hereafter). NK has been suffering from the lack of food supplies caused by natural disasters, land degradation and failed political system. The neighboring developed country SK has a better agricultural system but very low food self-sufficiency rate (around 1% of maize). Maize is an important crop in both countries since it is staple food for NK and SK is No. 2 maize importing country in the world after Japan. Therefore evaluating maize yield potential (Yp) in the two distinct regions is essential to assess food security under climate change and variability. In this study, we have utilized multiple process-based crop models capable of regional-scale assessments to evaluate maize Yp over the Korean Peninsula - the GIS version of EPIC model (GEPIC) and APSIM model that can be expanded to regional scales (APSIM regions). First we evaluated model performance and skill for 20 years from 1991 to 2010 using reanalysis data (Local Data Assimilation and Prediction System (LDAPS); 1.5km resolution) and observed data. Each model's performances were compared over different regions within the Korean Peninsula of different regional climate characteristics. To quantify the major influence of individual climate variables, we also conducted a sensitivity test using 20 years of climatology. Lastly, a multi-model ensemble analysis was performed to reduce crop model uncertainties. The results will provide valuable information for estimating the climate change or variability impacts on Yp over the Korean Peninsula.

A 20 year independent record of sea surface temperature for climate from Along-Track Scanning Radiometers

NASA Astrophysics Data System (ADS)

Merchant, Christopher J.; Embury, Owen; Rayner, Nick A.; Berry, David I.; Corlett, Gary K.; Lean, Katie; Veal, Karen L.; Kent, Elizabeth C.; Llewellyn-Jones, David T.; Remedios, John J.; Saunders, Roger

2012-12-01

A new record of sea surface temperature (SST) for climate applications is described. This record provides independent corroboration of global variations estimated from SST measurements made in situ. Infrared imagery from Along-Track Scanning Radiometers (ATSRs) is used to create a 20 year time series of SST at 0.1° latitude-longitude resolution, in the ATSR Reprocessing for Climate (ARC) project. A very high degree of independence of in situ measurements is achieved via physics-based techniques. Skin SST and SST estimated for 20 cm depth are provided, with grid cell uncertainty estimates. Comparison with in situ data sets establishes that ARC SSTs generally have bias of order 0.1 K or smaller. The precision of the ARC SSTs is 0.14 K during 2003 to 2009, from three-way error analysis. Over the period 1994 to 2010, ARC SSTs are stable, with better than 95% confidence, to within 0.005 K yr-1(demonstrated for tropical regions). The data set appears useful for cleanly quantifying interannual variability in SST and major SST anomalies. The ARC SST global anomaly time series is compared to the in situ-based Hadley Centre SST data set version 3 (HadSST3). Within known uncertainties in bias adjustments applied to in situ measurements, the independent ARC record and HadSST3 present the same variations in global marine temperature since 1996. Since the in situ observing system evolved significantly in its mix of measurement platforms and techniques over this period, ARC SSTs provide an important corroboration that HadSST3 accurately represents recent variability and change in this essential climate variable.

Productivity in the barents sea--response to recent climate variability.

PubMed

Dalpadado, Padmini; Arrigo, Kevin R; Hjøllo, Solfrid S; Rey, Francisco; Ingvaldsen, Randi B; Sperfeld, Erik; van Dijken, Gert L; Stige, Leif C; Olsen, Are; Ottersen, Geir

2014-01-01

The temporal and spatial dynamics of primary and secondary biomass/production in the Barents Sea since the late 1990s are examined using remote sensing data, observations and a coupled physical-biological model. Field observations of mesozooplankton biomass, and chlorophyll a data from transects (different seasons) and large-scale surveys (autumn) were used for validation of the remote sensing products and modeling results. The validation showed that satellite data are well suited to study temporal and spatial dynamics of chlorophyll a in the Barents Sea and that the model is an essential tool for secondary production estimates. Temperature, open water area, chlorophyll a, and zooplankton biomass show large interannual variations in the Barents Sea. The climatic variability is strongest in the northern and eastern parts. The moderate increase in net primary production evident in this study is likely an ecosystem response to changes in climate during the same period. Increased open water area and duration of open water season, which are related to elevated temperatures, appear to be the key drivers of the changes in annual net primary production that has occurred in the northern and eastern areas of this ecosystem. The temporal and spatial variability in zooplankton biomass appears to be controlled largely by predation pressure. In the southeastern Barents Sea, statistically significant linkages were observed between chlorophyll a and zooplankton biomass, as well as between net primary production and fish biomass, indicating bottom-up trophic interactions in this region.

Productivity in the Barents Sea - Response to Recent Climate Variability

PubMed Central

Dalpadado, Padmini; Arrigo, Kevin R.; Hjøllo, Solfrid S.; Rey, Francisco; Ingvaldsen, Randi B.; Sperfeld, Erik; van Dijken, Gert L.; Stige, Leif C.; Olsen, Are; Ottersen, Geir

2014-01-01

The temporal and spatial dynamics of primary and secondary biomass/production in the Barents Sea since the late 1990s are examined using remote sensing data, observations and a coupled physical-biological model. Field observations of mesozooplankton biomass, and chlorophyll a data from transects (different seasons) and large-scale surveys (autumn) were used for validation of the remote sensing products and modeling results. The validation showed that satellite data are well suited to study temporal and spatial dynamics of chlorophyll a in the Barents Sea and that the model is an essential tool for secondary production estimates. Temperature, open water area, chlorophyll a, and zooplankton biomass show large interannual variations in the Barents Sea. The climatic variability is strongest in the northern and eastern parts. The moderate increase in net primary production evident in this study is likely an ecosystem response to changes in climate during the same period. Increased open water area and duration of open water season, which are related to elevated temperatures, appear to be the key drivers of the changes in annual net primary production that has occurred in the northern and eastern areas of this ecosystem. The temporal and spatial variability in zooplankton biomass appears to be controlled largely by predation pressure. In the southeastern Barents Sea, statistically significant linkages were observed between chlorophyll a and zooplankton biomass, as well as between net primary production and fish biomass, indicating bottom-up trophic interactions in this region. PMID:24788513

A new CM SAF Solar Surface Radiation Climate Data Set derived from Meteosat Satellite Observations

NASA Astrophysics Data System (ADS)

Trentmann, J.; Mueller, R. W.; Pfeifroth, U.; Träger-Chatterjee, C.; Cremer, R.

2014-12-01

The incoming surface solar radiation has been defined as an essential climate variable by GCOS. It is mandatory to monitor this part of the earth's energy balance, and thus gain insights on the state and variability of the climate system. In addition, data sets of the surface solar radiation have received increased attention over the recent years as an important source of information for the planning of solar energy applications. The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) is deriving surface solar radiation from geostationary and polar-orbiting satellite instruments. While CM SAF is focusing on the generation of high-quality long-term climate data records, also operationally data is provided in short time latency within 8 weeks. Here we present SARAH (Solar Surface Radiation Dataset - Heliosat), i.e. the new CM SAF Solar Surface Radiation data set based on Meteosat satellite observations. SARAH provides instantaneous, daily- and monthly-averaged data of the effective cloud albedo (CAL), the direct normalized solar radiation (DNI) and the solar irradiance (SIS) from 1983 to 2013 for the full view of the Meteosat satellite (i.e, Europe, Africa, parts of South America, and the Atlantic ocean). The data sets are generated with a high spatial resolution of 0.05 deg allowing for detailed regional studies, and are available in netcdf-format at no cost without restrictions at www.cmsaf.eu. We provide an overview of the data sets, including a validation against reference measurements from the BSRN and GEBA surface station networks.

Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling.

PubMed

Arenas-Castro, Salvador; Gonçalves, João; Alves, Paulo; Alcaraz-Segura, Domingo; Honrado, João P

2018-01-01

Global environmental changes are rapidly affecting species' distributions and habitat suitability worldwide, requiring a continuous update of biodiversity status to support effective decisions on conservation policy and management. In this regard, satellite-derived Ecosystem Functional Attributes (EFAs) offer a more integrative and quicker evaluation of ecosystem responses to environmental drivers and changes than climate and structural or compositional landscape attributes. Thus, EFAs may hold advantages as predictors in Species Distribution Models (SDMs) and for implementing multi-scale species monitoring programs. Here we describe a modelling framework to assess the predictive ability of EFAs as Essential Biodiversity Variables (EBVs) against traditional datasets (climate, land-cover) at several scales. We test the framework with a multi-scale assessment of habitat suitability for two plant species of conservation concern, both protected under the EU Habitats Directive, differing in terms of life history, range and distribution pattern (Iris boissieri and Taxus baccata). We fitted four sets of SDMs for the two test species, calibrated with: interpolated climate variables; landscape variables; EFAs; and a combination of climate and landscape variables. EFA-based models performed very well at the several scales (AUCmedian from 0.881±0.072 to 0.983±0.125), and similarly to traditional climate-based models, individually or in combination with land-cover predictors (AUCmedian from 0.882±0.059 to 0.995±0.083). Moreover, EFA-based models identified additional suitable areas and provided valuable information on functional features of habitat suitability for both test species (narrowly vs. widely distributed), for both coarse and fine scales. Our results suggest a relatively small scale-dependence of the predictive ability of satellite-derived EFAs, supporting their use as meaningful EBVs in SDMs from regional and broader scales to more local and finer scales. Since the evaluation of species' conservation status and habitat quality should as far as possible be performed based on scalable indicators linking to meaningful processes, our framework may guide conservation managers in decision-making related to biodiversity monitoring and reporting schemes.

Climate change impact assessment on food security in Indonesia

NASA Astrophysics Data System (ADS)

Ettema, Janneke; Aldrian, Edvin; de Bie, Kees; Jetten, Victor; Mannaerts, Chris

2013-04-01

As Indonesia is the world's fourth most populous country, food security is a persistent challenge. The potential impact of future climate change on the agricultural sector needs to be addressed in order to allow early implementation of mitigation strategies. The complex island topography and local sea-land-air interactions cannot adequately be represented in large scale General Climate Models (GCMs) nor visualized by TRMM. Downscaling is needed. Using meteorological observations and a simple statistical downscaling tool, local future projections are derived from state-of-the-art, large-scale GCM scenarios, provided by the CMIP5 project. To support the agriculture sector, providing information on especially rainfall and temperature variability is essential. Agricultural production forecast is influenced by several rain and temperature factors, such as rainy and dry season onset, offset and length, but also by daily and monthly minimum and maximum temperatures and its rainfall amount. A simple and advanced crop model will be used to address the sensitivity of different crops to temperature and rainfall variability, present-day and future. As case study area, Java Island is chosen as it is fourth largest island in Indonesia but contains more than half of the nation's population and dominates it politically and economically. The objective is to identify regions at agricultural risk due to changing patterns in precipitation and temperature.

Weak climatic control of stand-scale fire history during the late holocene.

PubMed

Gavin, Daniel G; Hu, Feng Sheng; Lertzman, Kenneth; Corbett, Peter

2006-07-01

Forest fire occurrence is affected by multiple controls that operate at local to regional scales. At the spatial scale of forest stands, regional climatic controls may be obscured by local controls (e.g., stochastic ignitions, topography, and fuel loads), but the long-term role of such local controls is poorly understood. We report here stand-scale (<100 ha) fire histories of the past 5000 years based on the analysis of sediment charcoal at two lakes 11 km apart in southeastern British Columbia. The two lakes are today located in similar subalpine forests, and they likely have experienced the same late-Holocene climatic changes because of their close proximity. We evaluated two independent properties of fire history: (1) fire-interval distribution, a measure of the overall incidence of fire, and (2) fire synchroneity, a measure of the co-occurrence of fire (here, assessed at centennial to millennial time scales due to the resolution of sediment records). Fire-interval distributions differed between the sites prior to, but not after, 2500 yr before present. When the entire 5000-yr period is considered, no statistical synchrony between fire-episode dates existed between the two sites at any temporal scale, but for the last 2500 yr marginal levels of synchrony occurred at centennial scales. Each individual fire record exhibited little coherency with regional climate changes. In contrast, variations in the composite record (average of both sites) matched variations in climate evidenced by late-Holocene glacial advances. This was probably due to the increased sample size and spatial extent represented by the composite record (up to 200 ha) plus increased regional climatic variability over the last several millennia, which may have partially overridden local, non-climatic controls. We conclude that (1) over past millennia, neighboring stands with similar modern conditions may have experienced different fire intervals and asynchronous patterns in fire episodes, likely because local controls outweighed the synchronizing effect of climate; (2) the influence of climate on fire occurrence is more strongly expressed when climatic variability is relatively great; and (3) multiple records from a region are essential if climate-fire relations are to be reliably described.

Downscaling climate information for local disease mapping.

PubMed

Bernardi, M; Gommes, R; Grieser, J

2006-06-01

The study of the impacts of climate on human health requires the interdisciplinary efforts of health professionals, climatologists, biologists, and social scientists to analyze the relationships among physical, biological, ecological, and social systems. As the disease dynamics respond to variations in regional and local climate, climate variability affects every region of the world and the diseases are not necessarily limited to specific regions, so that vectors may become endemic in other regions. Climate data at local level are thus essential to evaluate the dynamics of vector-borne disease through health-climate models and most of the times the climatological databases are not adequate. Climate data at high spatial resolution can be derived by statistical downscaling using historical observations but the method is limited by the availability of historical data at local level. Since the 90s', the statistical interpolation of climate data has been an important priority of the Agrometeorology Group of the Food and Agriculture Organization of the United Nations (FAO), as they are required for agricultural planning and operational activities at the local level. Since 1995, date of the first FAO spatial interpolation software for climate data, more advanced applications have been developed such as SEDI (Satellite Enhanced Data Interpolation) for the downscaling of climate data, LOCCLIM (Local Climate Estimator) and the NEW_LOCCLIM in collaboration with the Deutscher Wetterdienst (German Weather Service) to estimate climatic conditions at locations for which no observations are available. In parallel, an important effort has been made to improve the FAO climate database including at present more than 30,000 stations worldwide and expanding the database from developing countries coverage to global coverage.

On the brink of change: plant responses to climate on the Colorado Plateau

USGS Publications Warehouse

Munson, Seth M.; Belnap, Jayne; Schelz, Charles D.; Moran, Mary; Carolin, Tara W.

2011-01-01

The intensification of aridity due to anthropogenic climate change in the southwestern U.S. is likely to have a large impact on the growth and survival of plant species that may already be vulnerable to water stress. To make accurate predictions of plant responses to climate change, it is essential to determine the long-term dynamics of plant species associated with past climate conditions. Here we show how the plant species and functional types across a wide range of environmental conditions in Colorado Plateau national parks have changed with climate variability over the last twenty years. During this time, regional mean annual temperature increased by 0.18°C per year from 1989–1995, 0.06°C per year from 1995–2003, declined by 0.14°C from 2003–2008, and there was high interannual variability in precipitation. Non-metric multidimensional scaling of plant species at long-term monitoring sites indicated five distinct plant communities. In many of the communities, canopy cover of perennial plants was sensitive to mean annual temperature occurring in the previous year, whereas canopy cover of annual plants responded to cool season precipitation. In the perennial grasslands, there was an overall decline of C3 perennial grasses, no change of C4 perennial grasses, and an increase of shrubs with increasing temperature. In the shrublands, shrubs generally showed no change or slightly increased with increasing temperature. However, certain shrub species declined where soil and physical characteristics of a site limited water availability. In the higher elevation woodlands, Juniperus osteosperma and shrub canopy cover increased with increasing temperature, while Pinus edulis at the highest elevation sites was unresponsive to interannual temperature variability. These results from well-protected national parks highlight the importance of temperature to plant responses in a water-limited region and suggest that projected increases in aridity are likely to promote grass loss and shrub expansion on the Colorado Plateau.

Homogenising time series: Beliefs, dogmas and facts

NASA Astrophysics Data System (ADS)

Domonkos, P.

2010-09-01

For obtaining reliable information about climate change and climate variability the use of high quality data series is essentially important, and one basic tool of quality improvements is the statistical homogenisation of observed time series. In the recent decades large number of homogenisation methods has been developed, but the real effects of their application on time series are still not known entirely. The ongoing COST HOME project (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. The author believes that several old theoretical rules have to be re-evaluated. Some examples of the hot questions, a) Statistically detected change-points can be accepted only with the confirmation of metadata information? b) Do semi-hierarchic algorithms for detecting multiple change-points in time series function effectively in practise? c) Is it good to limit the spatial comparison of candidate series with up to five other series in the neighbourhood? Empirical results - those from the COST benchmark, and other experiments too - show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities seem like part of the climatic variability, thus the pure application of classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality, than in raw time series. The developers and users of homogenisation methods have to bear in mind that the eventual purpose of homogenisation is not to find change-points, but to have the observed time series with statistical properties those characterise well the climate change and climate variability.

Natural and anthropogenic land cover change and its impact on the regional climate and hydrological extremes over Sanjiangyuan region

NASA Astrophysics Data System (ADS)

Ji, P.; Yuan, X.

2017-12-01

Located in the northern Tibetan Plateau, Sanjiangyuan is the headwater region of the Yellow River, Yangtze River and Mekong River. Besides climate change, natural and human-induced land cover change (e.g., Graze for Grass Project) is also influencing the regional hydro-climate and hydrological extremes significantly. To quantify their impacts, a land surface model (LSM) with consideration of soil moisture-lateral surface flow interaction and quasi-three-dimensional subsurface flow, is used to conduct long-term high resolution simulations driven by China Meteorological Administration Land Data Assimilation System forcing data and different land cover scenarios. In particular, the role of surface and subsurface lateral flows is also analyzed by comparing with typical one-dimensional models. Lateral flows help to simulate soil moisture variability caused by topography at hyper-resolution (e.g., 100m), which is also essential for simulating hydrological extremes including soil moisture dryness/wetness and high/low flows. The LSM will also be coupled with a regional climate model to simulate the effect of natural and anthropogenic land cover change on regional climate, with particular focus on the land-atmosphere coupling at different resolutions with different configurations in modeling land surface hydrology.

A Review of Recent Changes in Southern Ocean Sea Ice, Their Drivers and Forcings

NASA Technical Reports Server (NTRS)

Hobbs, William R.; Massom, Rob; Stammerjohn, Sharon; Reid, Phillip; Williams, Guy; Meier, Walter

2016-01-01

Over the past 37years, satellite records show an increase in Antarctic sea ice cover that is most pronounced in the period of sea ice growth. This trend is dominated by increased sea ice coverage in the western Ross Sea, and is mitigated by a strong decrease in the Bellingshausen and Amundsen seas. The trends in sea ice areal coverage are accompanied by related trends in yearly duration. These changes have implications for ecosystems, as well as global and regional climate. In this review, we summarize the researchto date on observing these trends, identifying their drivers, and assessing the role of anthropogenic climate change. Whilst the atmosphere is thought to be the primary driver, the ocean is also essential in explaining the seasonality of the trend patterns. Detecting an anthropogenic signal in Antarctic sea ice is particularly challenging for a number of reasons: the expected response is small compared to the very high natural variability of the system; the observational record is relatively short; and the ability of global coupled climate models to faithfully represent the complex Antarctic climate system is in doubt.

Positive Low Cloud and Dust Feedbacks Amplify Tropical North Atlantic Multidecadal Variability

NASA Technical Reports Server (NTRS)

Yuan, Tianle; Oraiopoulos, Lazaros; Zelinka, Mark; Yu, Hongbin; Norris, Joel R.; Chin, Mian; Platnick, Steven; Meyer, Kerry

2016-01-01

The Atlantic Multidecadal Oscillation (AMO) is characterized by a horseshoe pattern of sea surface temperature (SST) anomalies and has a wide range of climatic impacts. While the tropical arm of AMO is responsible for many of these impacts, it is either too weak or completely absent in many climate model simulations. Here we show, using both observational and model evidence, that the radiative effect of positive low cloud and dust feedbacks is strong enough to generate the tropical arm of AMO, with the low cloud feedback more dominant. The feedbacks can be understood in a consistent dynamical framework: weakened tropical trade wind speed in response to a warm middle latitude SST anomaly reduces dust loading and low cloud fraction over the tropical Atlantic, which warms the tropical North Atlantic SST. Together they contribute to appearance of the tropical arm of AMO. Most current climate models miss both the critical wind speed response and two positive feedbacks though realistic simulations of them may be essential for many climatic studies related to the AMO.

Reassessing regime shifts in the North Pacific: incremental climate change and commercial fishing are necessary for explaining decadal-scale biological variability.

PubMed

Litzow, Michael A; Mueter, Franz J; Hobday, Alistair J

2014-01-01

In areas of the North Pacific that are largely free of overfishing, climate regime shifts - abrupt changes in modes of low-frequency climate variability - are seen as the dominant drivers of decadal-scale ecological variability. We assessed the ability of leading modes of climate variability [Pacific Decadal Oscillation (PDO), North Pacific Gyre Oscillation (NPGO), Arctic Oscillation (AO), Pacific-North American Pattern (PNA), North Pacific Index (NPI), El Niño-Southern Oscillation (ENSO)] to explain decadal-scale (1965-2008) patterns of climatic and biological variability across two North Pacific ecosystems (Gulf of Alaska and Bering Sea). Our response variables were the first principle component (PC1) of four regional climate parameters [sea surface temperature (SST), sea level pressure (SLP), freshwater input, ice cover], and PCs 1-2 of 36 biological time series [production or abundance for populations of salmon (Oncorhynchus spp.), groundfish, herring (Clupea pallasii), shrimp, and jellyfish]. We found that the climate modes alone could not explain ecological variability in the study region. Both linear models (for climate PC1) and generalized additive models (for biology PC1-2) invoking only the climate modes produced residuals with significant temporal trends, indicating that the models failed to capture coherent patterns of ecological variability. However, when the residual climate trend and a time series of commercial fishery catches were used as additional candidate variables, resulting models of biology PC1-2 satisfied assumptions of independent residuals and out-performed models constructed from the climate modes alone in terms of predictive power. As measured by effect size and Akaike weights, the residual climate trend was the most important variable for explaining biology PC1 variability, and commercial catch the most important variable for biology PC2. Patterns of climate sensitivity and exploitation history for taxa strongly associated with biology PC1-2 suggest plausible mechanistic explanations for these modeling results. Our findings suggest that, even in the absence of overfishing and in areas strongly influenced by internal climate variability, climate regime shift effects can only be understood in the context of other ecosystem perturbations. © 2013 John Wiley & Sons Ltd.

Intercomparison of Downscaling Methods on Hydrological Impact for Earth System Model of NE United States

NASA Astrophysics Data System (ADS)

Yang, P.; Fekete, B. M.; Rosenzweig, B.; Lengyel, F.; Vorosmarty, C. J.

2012-12-01

Atmospheric dynamics are essential inputs to Regional-scale Earth System Models (RESMs). Variables including surface air temperature, total precipitation, solar radiation, wind speed and humidity must be downscaled from coarse-resolution, global General Circulation Models (GCMs) to the high temporal and spatial resolution required for regional modeling. However, this downscaling procedure can be challenging due to the need to correct for bias from the GCM and to capture the spatiotemporal heterogeneity of the regional dynamics. In this study, the results obtained using several downscaling techniques and observational datasets were compared for a RESM of the Northeast Corridor of the United States. Previous efforts have enhanced GCM model outputs through bias correction using novel techniques. For example, the Climate Impact Research at Potsdam Institute developed a series of bias-corrected GCMs towards the next generation climate change scenarios (Schiermeier, 2012; Moss et al., 2010). Techniques to better represent the heterogeneity of climate variables have also been improved using statistical approaches (Maurer, 2008; Abatzoglou, 2011). For this study, four downscaling approaches to transform bias-corrected HADGEM2-ES Model output (daily at .5 x .5 degree) to the 3'*3'(longitude*latitude) daily and monthly resolution required for the Northeast RESM were compared: 1) Bilinear Interpolation, 2) Daily bias-corrected spatial downscaling (D-BCSD) with Gridded Meteorological Datasets (developed by Abazoglou 2011), 3) Monthly bias-corrected spatial disaggregation (M-BCSD) with CRU(Climate Research Unit) and 4) Dynamic Downscaling based on Weather Research and Forecast (WRF) model. Spatio-temporal analysis of the variability in precipitation was conducted over the study domain. Validation of the variables of different downscaling methods against observational datasets was carried out for assessment of the downscaled climate model outputs. The effects of using the different approaches to downscale atmospheric variables (specifically air temperature and precipitation) for use as inputs to the Water Balance Model (WBMPlus, Vorosmarty et al., 1998;Wisser et al., 2008) for simulation of daily discharge and monthly stream flow in the Northeast US for a 100-year period in the 21st century were also assessed. Statistical techniques especially monthly bias-corrected spatial disaggregation (M-BCSD) showed potential advantage among other methods for the daily discharge and monthly stream flow simulation. However, Dynamic Downscaling will provide important complements to the statistical approaches tested.

Long-term variabilities of meridional geostrophic volumn transport in North Pacific Ocean

NASA Astrophysics Data System (ADS)

Zhou, H.; Yuan, D.; Dewar, W. K.

2016-02-01

The meridional geostrophic volumn transport (MGVT) by the ocean plays a very important role in the climatic water mass and heat balance because of its large heat capacity which enables the oceans to store the large amount of radiation received in the summer and to release it in winter. Better understanding of the role of the oceans in climate variability is essential to assess the likely range of future climate fluctuations. In the last century the North Pacific Ocean experienced considerable climate variability, especially on decadal time scale. Some studies have shown that the North Pacific Ocean is the origin of North Pacific multidecadal variability (Latif and Barnett, 1994; Barnett et al., 1999). These fluctuations were associated with large anomalies in sea level, temperature, storminess and rainfall, the heat transport and other extremes are changing as well. If the MGVT of the ocean is well-determined, it can be used as a test of the validity of numerical, global climate models. In this paper, we investigate the long-term variability of the MGVT in North Pacific ocean based on 55 years long global ocean heat and salt content data (Levitus et al., 2012). Very clear inter-decadal variations can be seen in tropical , subtropical and subpolar regions of North Pacific Ocean. There are very consistent variations between the MGVT anomalies and the inter-decadal pacific oscillation (IPO) index in the tropical gyre with cold phase of IPO corresponding to negative MGVT anomalies and warm phase corresponding to positive MGVT anomalies. The subtropical gyre shows more complex variations, and the subpolar gyre shows a negative MGVT anomaly before late 1970's and a positive anomaly after that time. The geostrophic velocities of North Pacific Ocean show significantly different anomalies during the two IPO cold phases of 1955-1976 and 1999 to present, which suggests a different mechanism of the two cold phases. The long term variations of Sverdrup transport compares well with that of the MGVT in the basin of 8-10N and north of 35N, but the two compares poorly or even reversed in the middle part of the basin. A reduced gravity model is used to investigate the mechanisms of the above variations.

Global Scale Remote Sensing Monitoring of Endorheic Lake Systems

NASA Astrophysics Data System (ADS)

Scuderi, L. A.

2010-12-01

Semi-arid regions of the world contain thousands of endorheic lakes in large shallow basins. Due to their generally remote locations few are continuously monitored. Documentation of recent variability is essential to assessing how endorheic lakes respond to short-term meteorological conditions and longer-term decadal-scale climatic variability and is critical in determining future disturbance of hydrological regimes with respect to predicted warming and drying in the mid-latitudes. Short- and long-term departures from climatic averages, rapid environmental shifts and increased population pressures may result in significant fluctuations in the hydrologic budgets of these lakes and adversely impact endorheic lake/basin ecosystems. Information on flooding variability is also critical in estimating changes in P/E balances and on the production of exposed and easily deflated surfaces that may impact dust loading locally and regionally. In order to provide information on how these lakes respond we need to understand how entire systems respond hydrologically to different climatic inputs. This requires monitoring and analysis of regional to continental-scale systems. To date, this level of monitoring has not been achieved in an operational system. In order to assess the possibility of creating a global-scale lake inundation database we analyzed two contrasting lake systems in western North America (Mexico and New Mexico, USA) and China (Inner Mongolia). We asked two major questions: 1) is it possible to quickly and accurately quantify current lake inundation events in near real time using remote sensing? and, 2) is it possible to differentiate variable meteorological sources and resultant lake inundation responses using this type of database? With respect to these results we outline an automated lake monitoring approach using MODIS data and real-time processing systems that may provide future global monitoring capabilities.

Consistency of Estimated Global Water Cycle Variations Over the Satellite Era

NASA Technical Reports Server (NTRS)

Robertson, F. R.; Bosilovich, M. G.; Roberts, J. B.; Reichle, R. H.; Adler, R.; Ricciardulli, L.; Berg, W.; Huffman, G. J.

2013-01-01

Motivated by the question of whether recent indications of decadal climate variability and a possible "climate shift" may have affected the global water balance, we examine evaporation minus precipitation (E-P) variability integrated over the global oceans and global land from three points of view-remotely sensed retrievals / objective analyses over the oceans, reanalysis vertically-integrated moisture convergence (MFC) over land, and land surface models forced with observations-based precipitation, radiation and near-surface meteorology. Because monthly variations in area-averaged atmospheric moisture storage are small and the global integral of moisture convergence must approach zero, area-integrated E-P over ocean should essentially equal precipitation minus evapotranspiration (P-ET) over land (after adjusting for ocean and land areas). Our analysis reveals considerable uncertainty in the decadal variations of ocean evaporation when integrated to global scales. This is due to differences among datasets in 10m wind speed and near-surface atmospheric specific humidity (2m qa) used in bulk aerodynamic retrievals. Precipitation variations, all relying substantially on passive microwave retrievals over ocean, still have uncertainties in decadal variability, but not to the degree present with ocean evaporation estimates. Reanalysis MFC and P-ET over land from several observationally forced diagnostic and land surface models agree best on interannual variations. However, upward MFC (i.e. P-ET) reanalysis trends are likely related in part to observing system changes affecting atmospheric assimilation models. While some evidence for a low-frequency E-P maximum near 2000 is found, consistent with a recent apparent pause in sea-surface temperature (SST) rise, uncertainties in the datasets used here remain significant. Prospects for further reducing uncertainties are discussed. The results are interpreted in the context of recent climate variability (Pacific Decadal Oscillation, Atlantic Meridional Overturning), and efforts to distinguish these modes from longer-term trends.

A comparative modeling analysis of multiscale temporal variability of rainfall in Australia

NASA Astrophysics Data System (ADS)

Samuel, Jos M.; Sivapalan, Murugesu

2008-07-01

The effects of long-term natural climate variability and human-induced climate change on rainfall variability have become the focus of much concern and recent research efforts. In this paper, we present the results of a comparative analysis of observed multiscale temporal variability of rainfall in the Perth, Newcastle, and Darwin regions of Australia. This empirical and stochastic modeling analysis explores multiscale rainfall variability, i.e., ranging from short to long term, including within-storm patterns, and intra-annual, interannual, and interdecadal variabilities, using data taken from each of these regions. The analyses investigated how storm durations, interstorm periods, and average storm rainfall intensities differ for different climate states and demonstrated significant differences in this regard between the three selected regions. In Perth, the average storm intensity is stronger during La Niña years than during El Niño years, whereas in Newcastle and Darwin storm duration is longer during La Niña years. Increase of either storm duration or average storm intensity is the cause of higher average annual rainfall during La Niña years as compared to El Niño years. On the other hand, within-storm variability does not differ significantly between different ENSO states in all three locations. In the case of long-term rainfall variability, the statistical analyses indicated that in Newcastle the long-term rainfall pattern reflects the variability of the Interdecadal Pacific Oscillation (IPO) index, whereas in Perth and Darwin the long-term variability exhibits a step change in average annual rainfall (up in Darwin and down in Perth) which occurred around 1970. The step changes in Perth and Darwin and the switch in IPO states in Newcastle manifested differently in the three study regions in terms of changes in the annual number of rainy days or the average daily rainfall intensity or both. On the basis of these empirical data analyses, a stochastic rainfall time series model was developed that incorporates the entire range of multiscale variabilities observed in each region, including within-storm, intra-annual, interannual, and interdecadal variability. Such ability to characterize, model, and synthetically generate realistic time series of rainfall intensities is essential for addressing many hydrological problems, including estimation of flood and drought frequencies, pesticide risk assessment, and landslide frequencies.

Climate Change and the Snowmelt-runoff Relationship in the Upper Rio Grande Basin

NASA Astrophysics Data System (ADS)

Chavarria, S. B.; Gutzler, D. S.

2016-12-01

Drought and rising temperatures have resulted in reduced snowpack and low flows in recent years for the Rio Grande, a vital source of surface water in three southwestern states and northern Mexico. We assess monthly and seasonal changes in streamflow volume on the upper Rio Grande (URG) near its headwaters in southern Colorado for water years 1958-2015. We use gage data from the U.S. Geological Survey, naturalized streamflows from the U.S. Natural Resources Conservation Service, and observed temperature, precipitation and snowpack data in the URG. Trends in discharge and downstream gains/losses are examined together with covariations in snow water equivalent, and surface climate variables. We test the hypothesis that climate change is already affecting the streamflow volume derived from snow accumulation in ways consistent with CMIP-based model projections of 21st Century streamflow, and we attempt to separate climate-related streamflow signals from variability due to reservoir releases or diversions. Preliminary results indicate that decreasing snowpack and resulting diminution of springtime streamflow in the URG are detectable in both observed and naturalized flow data beginning in the mid to late 1980s, despite the absence of significant decrease in total flow. Correlations between warm and cold season fluctuations in streamflow and temperature or precipitation are being evaluated and will be compared to model projections. Our study will provide information that may be useful for validating hydroclimatic models and improving seasonal water supply outlooks, essential tools for water management.

Continuation of the NVAP Global Water Vapor Data Sets for Pathfinder Science Analysis

NASA Technical Reports Server (NTRS)

VonderHaar, Thomas H.; Engelen, Richard J.; Forsythe, John M.; Randel, David L.; Ruston, Benjamin C.; Woo, Shannon; Dodge, James (Technical Monitor)

2001-01-01

This annual report covers August 2000 - August 2001 under NASA contract NASW-0032, entitled "Continuation of the NVAP (NASA's Water Vapor Project) Global Water Vapor Data Sets for Pathfinder Science Analysis". NASA has created a list of Earth Science Research Questions which are outlined by Asrar, et al. Particularly relevant to NVAP are the following questions: (a) How are global precipitation, evaporation, and the cycling of water changing? (b) What trends in atmospheric constituents and solar radiation are driving global climate? (c) How well can long-term climatic trends be assessed or predicted? Water vapor is a key greenhouse gas, and an understanding of its behavior is essential in global climate studies. Therefore, NVAP plays a key role in addressing the above climate questions by creating a long-term global water vapor dataset and by updating the dataset with recent advances in satellite instrumentation. The NVAP dataset produced from 1988-1998 has found wide use in the scientific community. Studies of interannual variability are particularly important. A recent paper by Simpson, et al. that examined the NVAP dataset in detail has shown that its relative accuracy is sufficient for the variability studies that contribute toward meeting NASA's goals. In the past year, we have made steady progress towards continuing production of this high-quality dataset as well as performing our own investigations of the data. This report summarizes the past year's work on production of the NVAP dataset and presents results of analyses we have performed in the past year.

Modeling climate effects on hip fracture rate by the multivariate GARCH model in Montreal region, Canada.

PubMed

Modarres, Reza; Ouarda, Taha B M J; Vanasse, Alain; Orzanco, Maria Gabriela; Gosselin, Pierre

2014-07-01

Changes in extreme meteorological variables and the demographic shift towards an older population have made it important to investigate the association of climate variables and hip fracture by advanced methods in order to determine the climate variables that most affect hip fracture incidence. The nonlinear autoregressive moving average with exogenous variable-generalized autoregressive conditional heteroscedasticity (ARMAX-GARCH) and multivariate GARCH (MGARCH) time series approaches were applied to investigate the nonlinear association between hip fracture rate in female and male patients aged 40-74 and 75+ years and climate variables in the period of 1993-2004, in Montreal, Canada. The models describe 50-56% of daily variation in hip fracture rate and identify snow depth, air temperature, day length and air pressure as the influencing variables on the time-varying mean and variance of the hip fracture rate. The conditional covariance between climate variables and hip fracture rate is increasing exponentially, showing that the effect of climate variables on hip fracture rate is most acute when rates are high and climate conditions are at their worst. In Montreal, climate variables, particularly snow depth and air temperature, appear to be important predictors of hip fracture incidence. The association of climate variables and hip fracture does not seem to change linearly with time, but increases exponentially under harsh climate conditions. The results of this study can be used to provide an adaptive climate-related public health program and ti guide allocation of services for avoiding hip fracture risk.

Modeling climate effects on hip fracture rate by the multivariate GARCH model in Montreal region, Canada

NASA Astrophysics Data System (ADS)

Modarres, Reza; Ouarda, Taha B. M. J.; Vanasse, Alain; Orzanco, Maria Gabriela; Gosselin, Pierre

2014-07-01

Changes in extreme meteorological variables and the demographic shift towards an older population have made it important to investigate the association of climate variables and hip fracture by advanced methods in order to determine the climate variables that most affect hip fracture incidence. The nonlinear autoregressive moving average with exogenous variable-generalized autoregressive conditional heteroscedasticity (ARMA X-GARCH) and multivariate GARCH (MGARCH) time series approaches were applied to investigate the nonlinear association between hip fracture rate in female and male patients aged 40-74 and 75+ years and climate variables in the period of 1993-2004, in Montreal, Canada. The models describe 50-56 % of daily variation in hip fracture rate and identify snow depth, air temperature, day length and air pressure as the influencing variables on the time-varying mean and variance of the hip fracture rate. The conditional covariance between climate variables and hip fracture rate is increasing exponentially, showing that the effect of climate variables on hip fracture rate is most acute when rates are high and climate conditions are at their worst. In Montreal, climate variables, particularly snow depth and air temperature, appear to be important predictors of hip fracture incidence. The association of climate variables and hip fracture does not seem to change linearly with time, but increases exponentially under harsh climate conditions. The results of this study can be used to provide an adaptive climate-related public health program and ti guide allocation of services for avoiding hip fracture risk.

Phenological Responses to ENSO in the Global Oceans

NASA Astrophysics Data System (ADS)

Racault, M.-F.; Sathyendranath, S.; Menon, N.; Platt, T.

2017-01-01

Phenology relates to the study of timing of periodic events in the life cycle of plants or animals as influenced by environmental conditions and climatic forcing. Phenological metrics provide information essential to quantify variations in the life cycle of these organisms. The metrics also allow us to estimate the speed at which living organisms respond to environmental changes. At the surface of the oceans, microscopic plant cells, so-called phytoplankton, grow and sometimes form blooms, with concentrations reaching up to 100 million cells per litre and extending over many square kilometres. These blooms can have a huge collective impact on ocean colour, because they contain chlorophyll and other auxiliary pigments, making them visible from space. Phytoplankton populations have a high turnover rate and can respond within hours to days to environmental perturbations. This makes them ideal indicators to study the first-level biological response to environmental changes. In the Earth's climate system, the El Niño-Southern Oscillation (ENSO) dominates large-scale inter-annual variations in environmental conditions. It serves as a natural experiment to study and understand how phytoplankton in the ocean (and hence the organisms at higher trophic levels) respond to climate variability. Here, the ENSO influence on phytoplankton is estimated through variations in chlorophyll concentration, primary production and timings of initiation, peak, termination and duration of the growing period. The phenological variabilities are used to characterise phytoplankton responses to changes in some physical variables: sea surface temperature, sea surface height and wind. It is reported that in oceanic regions experiencing high annual variations in the solar cycle, such as in high latitudes, the influence of ENSO may be readily measured using annual mean anomalies of physical variables. In contrast, in oceanic regions where ENSO modulates a climate system characterised by a seasonal reversal of the wind forcing, such as the monsoon system in the Indian Ocean, phenology-based mean anomalies of physical variables help refine evaluation of the mechanisms driving the biological responses and provide a more comprehensive understanding of the integrated processes.

A simple object-oriented and open-source model for scientific and policy analyses of the global climate system – Hector v1.0

DOE PAGES

Hartin, Corinne A.; Patel, Pralit L.; Schwarber, Adria; ...

2015-04-01

Simple climate models play an integral role in the policy and scientific communities. They are used for climate mitigation scenarios within integrated assessment models, complex climate model emulation, and uncertainty analyses. Here we describe Hector v1.0, an open source, object-oriented, simple global climate carbon-cycle model. This model runs essentially instantaneously while still representing the most critical global-scale earth system processes. Hector has a three-part main carbon cycle: a one-pool atmosphere, land, and ocean. The model's terrestrial carbon cycle includes primary production and respiration fluxes, accommodating arbitrary geographic divisions into, e.g., ecological biomes or political units. Hector actively solves the inorganicmore » carbon system in the surface ocean, directly calculating air–sea fluxes of carbon and ocean pH. Hector reproduces the global historical trends of atmospheric [CO 2], radiative forcing, and surface temperatures. The model simulates all four Representative Concentration Pathways (RCPs) with equivalent rates of change of key variables over time compared to current observations, MAGICC (a well-known simple climate model), and models from the 5th Coupled Model Intercomparison Project. Hector's flexibility, open-source nature, and modular design will facilitate a broad range of research in various areas.« less

Decreased winter severity increases viability of a montane frog population

PubMed Central

McCaffery, Rebecca M.; Maxell, Bryce A.

2010-01-01

Many proximate causes of global amphibian declines have been well documented, but the role that climate change has played and will play in this crisis remains ambiguous for many species. Breeding phenology and disease outbreaks have been associated with warming temperatures, but, to date, few studies have evaluated effects of climate change on individual vital rates and subsequent population dynamics of amphibians. We evaluated relationships among local climate variables, annual survival and fecundity, and population growth rates from a 9-year demographic study of Columbia spotted frogs (Rana luteiventris) in the Bitterroot Mountains of Montana. We documented an increase in survival and breeding probability as severity of winter decreased. Therefore, a warming climate with less severe winters is likely to promote population viability in this montane frog population. More generally, amphibians and other ectotherms inhabiting alpine or boreal habitats at or near their thermal ecological limits may benefit from the milder winters provided by a warming climate as long as suitable habitats remain intact. A more thorough understanding of how climate change is expected to benefit or harm amphibian populations at different latitudes and elevations is essential for determining the best strategies to conserve viable populations and allow for gene flow and shifts in geographic range. PMID:20421473

Assessment of Human Health Vulnerability to Climate Variability and Change in Cuba

PubMed Central

Bultó, Paulo Lázaro Ortíz; Rodríguez, Antonio Pérez; Valencia, Alina Rivero; Vega, Nicolás León; Gonzalez, Manuel Díaz; Carrera, Alina Pérez

2006-01-01

In this study we assessed the potential effects of climate variability and change on population health in Cuba. We describe the climate of Cuba as well as the patterns of climate-sensitive diseases of primary concern, particularly dengue fever. Analyses of the associations between climatic anomalies and disease patterns highlight current vulnerability to climate variability. We describe current adaptations, including the application of climate predictions to prevent disease outbreaks. Finally, we present the potential economic costs associated with future impacts due to climate change. The tools used in this study can be useful in the development of appropriate and effective adaptation options to address the increased climate variability associated with climate change. PMID:17185289

Climate variability drives population cycling and synchrony

Treesearch

Lars Y. Pomara; Benjamin Zuckerberg

2017-01-01

Aim There is mounting concern that climate change will lead to the collapse of cyclic population dynamics, yet the influence of climate variability on population cycling remains poorly understood. We hypothesized that variability in survival and fecundity, driven by climate variability at different points in the life cycle, scales up from...

Local Populations of Arabidopsis thaliana Show Clear Relationship between Photoperiodic Sensitivity of Flowering Time and Altitude

PubMed Central

Lewandowska-Sabat, Anna M.; Fjellheim, Siri; Olsen, Jorunn E.; Rognli, Odd A.

2017-01-01

Adaptation of plants to local conditions that vary substantially within their geographic range is essential for seasonal timing of flowering, a major determinant of plant reproductive success. This study investigates photoperiodic responses in natural populations of Arabidopsis thaliana from high northern latitudes and their significance for local adaptation. Thirty lineages from ten local A. thaliana populations, representing different locations across an altitudinal gradient (2–850 m a.s.l.) in Norway, were grown under uniform controlled conditions, and used to screen for responses to five different photoperiods. We studied relationships between variation in photoperiodic sensitivity of flowering time, altitude, and climatic factors associated with the sites of origin. We found that variation in response to photoperiod is significantly correlated with altitude and climatic variables associated with the sites of origin of the populations. Populations originating from lower altitudes showed stronger photoperiodic sensitivity than populations from higher altitudes. Our results indicate that the altitudinal climatic gradient generates clinal variation in adaptive traits in A. thaliana. PMID:28659966

Effects of ice storm on forest ecosystem of southern China in 2008 Shaoqiang Wang1, Lei Zhou1, Weimin Ju2, Kun Huang1 1Key Lab of Ecosystem Network Observation and Modeling, Institute of Geographical Sciences and Natural Resources Research, Beijing, 10010

NASA Astrophysics Data System (ADS)

Wang, Shaoqiang

2014-05-01

Evidence is mounting that an increase in extreme climate events has begun to occur worldwide during the recent decades, which affect biosphere function and biodiversity. Ecosystems returned to its original structures and functions to maintain its sustainability, which was closely dependent on ecosystem resilience. Understanding the resilience and recovery capacity of ecosystem to extreme climate events is essential to predicting future ecosystem responses to climate change. Given the overwhelming importance of this region in the overall carbon cycle of forest ecosystems in China, south China suffered a destructive ice storm in 2008. In this study, we used the number of freezing day and a process-based model (Boreal Ecosystem Productivity Simulator, BEPS) to characterize the spatial distribution of ice storm region in southeastern China and explore the impacts on carbon cycle of forest ecosystem over the past decade. The ecosystem variables, i.e. Net primary productivity (NPP), Evapotranspiration (ET), and Water use efficiency (WUE, the ratio of NPP to ET) from the outputs of BEPS models were used to detect the resistance and resilience of forest ecosystem in southern China. The pattern of ice storm-induced forest productivity widespread decline was closely related to the number of freezing day during the ice storm period. The NPP of forest area suffered heavy ice storm returned to normal status after five months with high temperature and ample moisture, indicated a high resilience of subtropical forest in China. The long-term changes of forest WUE remain stable, behaving an inherent sensitivity of ecosystem to extreme climate events. In addition, ground visits suggested that the recovery of forest productivity was attributed to rapid growth of understory. Understanding the variability and recovery threshold of ecosystem following extreme climate events help us to better simulate and predict the variability of ecosystem structure and function under current and future climate change.

Sources and Impacts of Modeled and Observed Low-Frequency Climate Variability

NASA Astrophysics Data System (ADS)

Parsons, Luke Alexander

Here we analyze climate variability using instrumental, paleoclimate (proxy), and the latest climate model data to understand more about the sources and impacts of low-frequency climate variability. Understanding the drivers of climate variability at interannual to century timescales is important for studies of climate change, including analyses of detection and attribution of climate change impacts. Additionally, correctly modeling the sources and impacts of variability is key to the simulation of abrupt change (Alley et al., 2003) and extended drought (Seager et al., 2005; Pelletier and Turcotte, 1997; Ault et al., 2014). In Appendix A, we employ an Earth system model (GFDL-ESM2M) simulation to study the impacts of a weakening of the Atlantic meridional overturning circulation (AMOC) on the climate of the American Tropics. The AMOC drives some degree of local and global internal low-frequency climate variability (Manabe and Stouffer, 1995; Thornalley et al., 2009) and helps control the position of the tropical rainfall belt (Zhang and Delworth, 2005). We find that a major weakening of the AMOC can cause large-scale temperature, precipitation, and carbon storage changes in Central and South America. Our results suggest that possible future changes in AMOC strength alone will not be sufficient to drive a large-scale dieback of the Amazonian forest, but this key natural ecosystem is sensitive to dry-season length and timing of rainfall (Parsons et al., 2014). In Appendix B, we compare a paleoclimate record of precipitation variability in the Peruvian Amazon to climate model precipitation variability. The paleoclimate (Lake Limon) record indicates that precipitation variability in western Amazonia is 'red' (i.e., increasing variability with timescale). By contrast, most state-of-the-art climate models indicate precipitation variability in this region is nearly 'white' (i.e., equally variability across timescales). This paleo-model disagreement in the overall structure of the variance spectrum has important consequences for the probability of multi-year drought. Our lake record suggests there is a significant background threat of multi-year, and even decade-length, drought in western Amazonia, whereas climate model simulations indicate most droughts likely last no longer than one to three years. These findings suggest climate models may underestimate the future risk of extended drought in this important region. In Appendix C, we expand our analysis of climate variability beyond South America. We use observations, well-constrained tropical paleoclimate, and Earth system model data to examine the overall shape of the climate spectrum across interannual to century frequencies. We find a general agreement among observations and models that temperature variability increases with timescale across most of the globe outside the tropics. However, as compared to paleoclimate records, climate models generate too little low-frequency variability in the tropics (e.g., Laepple and Huybers, 2014). When we compare the shape of the simulated climate spectrum to the spectrum of a simple autoregressive process, we find much of the modeled surface temperature variability in the tropics could be explained by ocean smoothing of weather noise. Importantly, modeled precipitation tends to be similar to white noise across much of the globe. By contrast, paleoclimate records of various types from around the globe indicate that both temperature and precipitation variability should experience much more low-frequency variability than a simple autoregressive or white-noise process. In summary, state-of-the-art climate models generate some degree of dynamically driven low-frequency climate variability, especially at high latitudes. However, the latest climate models, observations, and paleoclimate data provide us with drastically different pictures of the background climate system and its associated risks. This research has important consequences for improving how we simulate climate extremes as we enter a warmer (and often drier) world in the coming centuries; if climate models underestimate low-frequency variability, we will underestimate the risk of future abrupt change and extreme events, such as megadroughts.

Interactions of Mean Climate Change and Climate Variability on Food Security Extremes

NASA Technical Reports Server (NTRS)

Ruane, Alexander C.; McDermid, Sonali; Mavromatis, Theodoros; Hudson, Nicholas; Morales, Monica; Simmons, John; Prabodha, Agalawatte; Ahmad, Ashfaq; Ahmad, Shakeel; Ahuja, Laj R.

2015-01-01

Recognizing that climate change will affect agricultural systems both through mean changes and through shifts in climate variability and associated extreme events, we present preliminary analyses of climate impacts from a network of 1137 crop modeling sites contributed to the AgMIP Coordinated Climate-Crop Modeling Project (C3MP). At each site sensitivity tests were run according to a common protocol, which enables the fitting of crop model emulators across a range of carbon dioxide, temperature, and water (CTW) changes. C3MP can elucidate several aspects of these changes and quantify crop responses across a wide diversity of farming systems. Here we test the hypothesis that climate change and variability interact in three main ways. First, mean climate changes can affect yields across an entire time period. Second, extreme events (when they do occur) may be more sensitive to climate changes than a year with normal climate. Third, mean climate changes can alter the likelihood of climate extremes, leading to more frequent seasons with anomalies outside of the expected conditions for which management was designed. In this way, shifts in climate variability can result in an increase or reduction of mean yield, as extreme climate events tend to have lower yield than years with normal climate.C3MP maize simulations across 126 farms reveal a clear indication and quantification (as response functions) of mean climate impacts on mean yield and clearly show that mean climate changes will directly affect the variability of yield. Yield reductions from increased climate variability are not as clear as crop models tend to be less sensitive to dangers on the cool and wet extremes of climate variability, likely underestimating losses from water-logging, floods, and frosts.

Quantifying Chemical Ozone Loss in the Arctic Stratosphere with GEOS-STRATCHEM Data Assimilation System

NASA Technical Reports Server (NTRS)

Wargan, K.; Nielsen, J. E.

2017-01-01

A faithful representation of polar stratospheric chemistry in models and its connection with dynamical variability is essential for our understanding of the evolution of the ozone layer in a changing climate and during the projected continuing decline of ozone depleting substances in the atmosphere. We use a new configuration of the Goddard Earth Observing System Data Assimilation System with a stratospheric chemistry model to study ozone depletion in the Arctic polar stratosphere during the exceptionally cold (in the stratosphere) winters 2015/2016 and 2010/2011.

Identifying the fingerprints of the anthropogenic component of land use/land cover changes on regional climate of the USA high plains

NASA Astrophysics Data System (ADS)

Mutiibwa, D.; Irmak, S.

2011-12-01

The majority of recent climate change studies have largely focused on detection and attribution of anthropogenic forcings of greenhouse gases, aerosols, stratospheric and tropospheric ozone. However, there is growing evidence that land cover/land use (LULC) change can significantly impact atmospheric processes from local to regional weather and climate variability. Human activities such as conversion of natural ecosystem to croplands and urban-centers, deforestation and afforestation impact biophysical properties of the land surfaces including albedo, energy balance, moisture-holding capacity of soil, and surface roughness. Alterations in these properties affect the heat and moisture exchanges between the land surface and atmospheric boundary layer, and ultimately impact the climate system. The challenge is to demonstrate that LULC changes produce a signal that can be discerned from natural climate noise. In this study, we attempt to detect the signature of anthropogenic forcing of LULC change on climate on regional scale. The signal projector investigated for detecting the signature of LULC changes on regional climate of the High Plains of the USA is the Normalized Difference Vegetation Index (NDVI). NDVI is an indicator that captures short and long-term geographical distribution of vegetation surfaces. The study develops an enhanced signal processing procedure to maximize the signal to noise ratio by introducing a pre-filtering technique of ARMA processes on the investigated climate and signal variables, before applying the optimal fingerprinting technique to detect the signals of LULC changes on observed climate, temperature, in the High Plains. The intent is to filter out as much noise as possible while still retaining the essential features of the signal by making use of the known characteristics of the noise and the anticipated signal. The study discusses the approach of identifying and suppressing the autocorrelation in optimal fingerprint analysis by applying linear transformation of ARMA processes to the analysis variables. With the assumption that natural climate variability is a near stationary process, the pre-filters are developed to generate stationary residuals. The High Plains region although impacted by droughts over the last three decades has had an increase in agricultural lands, both irrigated and non-irrigated. The study shows that for the most part of the High Plains region there is significant influence of evaporative cooling on regional climate during the summer months. As the vegetation coverage increases coupled with increased in irrigation application, the regional daytime surface energy in summer is increasingly redistributed into latent heat flux which increases the effect of evaporative cooling on summer temperatures. We included the anthropogenic forcing of CO2 on regional climate with the main purpose of surpassing the radiative heating effect of greenhouse gases from natural climate noise, to enhance the LULC signal-to-noise ratio. The warming signal due to greenhouse gas forcing is observed to be weakest in the central part of the High Plains. The results showed that the CO2 signal in the region was weak or is being surpassed by the evaporative cooling effect.

Space-time dependence between energy sources and climate related energy production

NASA Astrophysics Data System (ADS)

Engeland, Kolbjorn; Borga, Marco; Creutin, Jean-Dominique; Ramos, Maria-Helena; Tøfte, Lena; Warland, Geir

2014-05-01

The European Renewable Energy Directive adopted in 2009 focuses on achieving a 20% share of renewable energy in the EU overall energy mix by 2020. A major part of renewable energy production is related to climate, called "climate related energy" (CRE) production. CRE production systems (wind, solar, and hydropower) are characterized by a large degree of intermittency and variability on both short and long time scales due to the natural variability of climate variables. The main strategies to handle the variability of CRE production include energy-storage, -transport, -diversity and -information (smart grids). The three first strategies aim to smooth out the intermittency and variability of CRE production in time and space whereas the last strategy aims to provide a more optimal interaction between energy production and demand, i.e. to smooth out the residual load (the difference between demand and production). In order to increase the CRE share in the electricity system, it is essential to understand the space-time co-variability between the weather variables and CRE production under both current and future climates. This study presents a review of the literature that searches to tackle these problems. It reveals that the majority of studies deals with either a single CRE source or with the combination of two CREs, mostly wind and solar. This may be due to the fact that the most advanced countries in terms of wind equipment have also very little hydropower potential (Denmark, Ireland or UK, for instance). Hydropower is characterized by both a large storage capacity and flexibility in electricity production, and has therefore a large potential for both balancing and storing energy from wind- and solar-power. Several studies look at how to better connect regions with large share of hydropower (e.g., Scandinavia and the Alps) to regions with high shares of wind- and solar-power (e.g., green battery North-Sea net). Considering time scales, various studies consider wind and solar power production and their co-fluctuation at small time scales. The multi-scale nature of the variability is less studied, i.e., the potential adverse or favorable co-fluctuation at intermediate time scales involving water scarcity or abundance, is less present in the literature.Our review points out that it could be especially interesting to promote research on how the pronounced large-scale fluctuations in inflow to hydropower (intra-annual run-off) and smaller scale fluctuations in wind- and solar-power interact in an energy system. There is a need to better represent the profound difference between wind-, solar- and hydro-energy sources. On the one hand, they are all directly linked to the 2-D horizontal dynamics of meteorology. On the other hand, the branching structure of hydrological systems transforms this variability and governs the complex combination of natural inflows and reservoir storage.Finally, we note that the CRE production is, in addition to weather, also influenced by the energy system and market, i.e., the energy transport and demand across scales as well as changes of market regulation. The CRE production system lies thus in this nexus between climate, energy systems and market regulations. The work presented is part of the FP7 project COMPLEX (Knowledge based climate mitigation systems for a low carbon economy; http://www.complex.ac.uk)

Pteropods and climate off the Antarctic Peninsula

NASA Astrophysics Data System (ADS)

Loeb, Valerie J.; Santora, Jarrod A.

2013-09-01

Shelled (thecosome) and naked (gymnosome) pteropods are regular, at times abundant, members of Southern Ocean zooplankton assemblages. Regionally, shelled species can play a major role in food webs and carbon cycling. Because of their aragonite shells thecosome pteropods may be vulnerable to the impacts of ocean acidification; without shells they cannot survive and their demise would have major implications for food webs and carbon cycling in the Southern Ocean. Additionally, pteropod species in the southwest Atlantic sector of the Southern Ocean inhabit a region of rapid warming and climate change, the impacts of which are predicted to be observed as poleward distribution shifts. Here we provide baseline information on intraseasonal, interannual and longer scale variability of pteropod populations off the Antarctic Peninsula between 1994 and 2009. Concentrations of the 4 dominant taxa, Limacina helicina antarctica f. antarctica, Clio pyramidata f. sulcata, Spongiobranchaea australis and Clione limacina antarctica, are similar to those monitored during the 1928-1935 Discovery Investigations and reflect generally low values but with episodic interannual abundance peaks that, except for C. pyr. sulcata, are related to basin-scale climate forcing associated with the El Niño-Southern Oscillation (ENSO) climate mode. Significant abundance increases of L. helicina and S. australis after 1998 were associated with a climate regime shift that initiated a period dominated by cool La Niña conditions and increased nearshore influence of the Antarctic Circumpolar Current (ACC). This background information is essential to assess potential future changes in pteropod species distribution and abundance associated with ocean warming and acidification. construct maps of pteropod spatial frequency and mean abundance to assess their oceanographic associations; quantify pteropod abundance anomalies for comparing intraseasonal and interannual variability relative to m-3 environmental variables and climate modes; investigate the presence of long-term trends and/or cycles of peak abundance of the pteropod species in this region as have been described for krill and salps (Loeb et al., 2009, 2010; Loeb and Santora, 2012). We then examine interannual and longer-term variability of pteropod species abundance with respect to possible effects of the El Niño-Southern Oscillation (ENSO) and Southern Annular Mode (SAM) on population size, advection into and retention within the survey area. In doing so we highlight the importance of having sufficient spatial and temporal sampling coverage, as well as appropriate net mesh size, to establish statistically significant abundance changes associated with climate modes and long-term warming.

Assessing the Agricultural Vulnerability for India under Changing Climate

NASA Astrophysics Data System (ADS)

Sharma, Tarul; Vardhan Murari, Harsha; Karmakar, Subhankar; Ghosh, Subimal; Singh, Jitendra

2016-04-01

Global climate change has proven to show majorly negative impacts for the far future. These negative impacts adversely affect almost all the fields including agriculture, water resources, tourism, and marine ecosystem. Among these, the effects on agriculture are considered to be of prime importance since its regional impacts can directly affect the global food security. Under such lines, it becomes essential to understand how climate change directs agricultural production for a region along with its vulnerability. In India, rice and wheat are considered as major staple diet and hence understanding its production loss/gain due to regional vulnerability to climate change becomes necessary. Here, an attempt has been made to understand the agricultural vulnerability for rice and wheat, considering yield as a function of temperature and precipitation during growing period. In order to accomplish this objective, the ratio of actual to potential evapo-transpiration has been considered which serves as a reliable indicator; with more this ratio towards unity, less vulnerable will be the region. The current objective needs an integration of climatic, hydrological and agricultural parameters; that can be achieved by simulating a climate data driven hydrologic (Variable Infiltration Capacity, VIC) model and a crop (Decision Support System for Agrotechnology Transfer, DSSAT) model. The proposed framework is an attempt to derive a crop vulnerability map that can facilitate in strategizing adaption practices which can reduce the adverse impacts of climate change in future.

Impacts of climate change under CMIP5 RCP scenarios on the streamflow in the Dinder River and ecosystem habitats in Dinder National Park, Sudan

NASA Astrophysics Data System (ADS)

Basheer, A. K.; Lu, H.; Omer, A.; Ali, A. B.; Abdelgader, A. M. S.

2015-10-01

The fate of seasonal rivers ecosystem habitats under climate change essentially depends on the changes in annual recharge, which related to alterations in precipitation and evaporation over the river basin. Therefore the change in climate conditions is expected to significantly affect hydrological and ecological components, particularly in fragmented ecosystems. This study aims to assess the impacts of climate change on the streamflow in Dinder River Basin (DRB), and infer its relative possible effects on the Dinder National Park (DNP) ecosystem habitats in the Sudan. Two global circulation models (GCMs) from Coupled Model Intercomparison Project Phase 5 and two statistical downscaling approaches combined with hydrological model (SWAT) were used to project the climate change conditions over the study periods 2020s, 2050s and 2080s. The results indicated that the climate over the DRB will become warmer and wetter under the most scenarios. The projected precipitation variability mainly depends on the selected GCM and downscaling approach. Moreover, the projected streamflow was more sensitive to rainfall and temperature variation, and will likely increase in this century. In contrast to drought periods during (1960s, 1970s and 1980s), the predicted climate change is likely to affect ecosystems in DNP positively and promote the ecological restoration of the flora and fauna habitats'.

Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium - Part 1: Theory

NASA Astrophysics Data System (ADS)

Sundberg, R.; Moberg, A.; Hind, A.

2012-08-01

A statistical framework for comparing the output of ensemble simulations from global climate models with networks of climate proxy and instrumental records has been developed, focusing on near-surface temperatures for the last millennium. This framework includes the formulation of a joint statistical model for proxy data, instrumental data and simulation data, which is used to optimize a quadratic distance measure for ranking climate model simulations. An essential underlying assumption is that the simulations and the proxy/instrumental series have a shared component of variability that is due to temporal changes in external forcing, such as volcanic aerosol load, solar irradiance or greenhouse gas concentrations. Two statistical tests have been formulated. Firstly, a preliminary test establishes whether a significant temporal correlation exists between instrumental/proxy and simulation data. Secondly, the distance measure is expressed in the form of a test statistic of whether a forced simulation is closer to the instrumental/proxy series than unforced simulations. The proposed framework allows any number of proxy locations to be used jointly, with different seasons, record lengths and statistical precision. The goal is to objectively rank several competing climate model simulations (e.g. with alternative model parameterizations or alternative forcing histories) by means of their goodness of fit to the unobservable true past climate variations, as estimated from noisy proxy data and instrumental observations.

Satellite-based climate data records of surface solar radiation from the CM SAF

NASA Astrophysics Data System (ADS)

Trentmann, Jörg; Cremer, Roswitha; Kothe, Steffen; Müller, Richard; Pfeifroth, Uwe

2017-04-01

The incoming surface solar radiation has been defined as an essential climate variable by GCOS. Long term monitoring of this part of the earth's energy budget is required to gain insights on the state and variability of the climate system. In addition, climate data sets of surface solar radiation have received increased attention over the recent years as an important source of information for solar energy assessments, for crop modeling, and for the validation of climate and weather models. The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) is deriving climate data records (CDRs) from geostationary and polar-orbiting satellite instruments. Within the CM SAF these CDRs are accompanied by operational data at a short time latency to be used for climate monitoring. All data from the CM SAF is freely available via www.cmsaf.eu. Here we present the regional and the global climate data records of surface solar radiation from the CM SAF. The regional climate data record SARAH (Surface Solar Radiation Dataset - Heliosat, doi: 10.5676/EUM_SAF_CM/SARAH/V002) is based on observations from the series of Meteosat satellites. SARAH provides 30-min, daily- and monthly-averaged data of the effective cloud albedo, the solar irradiance (incl. spectral information), the direct solar radiation (horizontal and normal), and the sunshine duration from 1983 to 2015 for the full view of the Meteosat satellite (i.e, Europe, Africa, parts of South America, and the Atlantic ocean). The data sets are generated with a high spatial resolution of 0.05° allowing for detailed regional studies. The global climate data record CLARA (CM SAF Clouds, Albedo and Radiation dataset from AVHRR data, doi: 10.5676/EUM_SAF_CM/CLARA_AVHRR/V002) is based on observations from the series of AVHRR satellite instruments. CLARA provides daily- and monthly-averaged global data of the solar irradiance (SIS) from 1982 to 2015 with a spatial resolution of 0.25°. In addition to the solar surface radiation also the longwave surface radiation as well as surface albedo and numerous cloud properties are provided in CLARA. Here we provide an overview of the climate data records of the surface solar radiation and present the results of the quality assessment of both climate data records against available surface reference observations, e.g., from the BSRN and the GEBA data archive.

Regional sea level variability in a high-resolution global coupled climate model

NASA Astrophysics Data System (ADS)

Palko, D.; Kirtman, B. P.

2016-12-01

The prediction of trends at regional scales is essential in order to adapt to and prepare for the effects of climate change. However, GCMs are unable to make reliable predictions at regional scales. The prediction of local sea level trends is particularly critical. The main goal of this research is to utilize high-resolution (HR) (0.1° resolution in the ocean) coupled model runs of CCSM4 to analyze regional sea surface height (SSH) trends. Unlike typical, lower resolution (1.0°) GCM runs these HR runs resolve features in the ocean, like the Gulf Stream, which may have a large effect on regional sea level. We characterize the variability of regional SSH along the Atlantic coast of the US using tide gauge observations along with fixed radiative forcing runs of CCSM4 and HR interactive ensemble runs. The interactive ensemble couples an ensemble mean atmosphere with a single ocean realization. This coupling results in a 30% decrease in the strength of the Atlantic meridional overturning circulation; therefore, the HR interactive ensemble is analogous to a HR hosing experiment. By characterizing the variability in these high-resolution GCM runs and observations we seek to understand what processes influence coastal SSH along the Eastern Coast of the United States and better predict future SLR.

Climate Variability over India and Bangladesh from the Perturbed UK Met Office Hadley Model: Impacts on Flow and Nutrient Fluxes in the Ganges Delta System

NASA Astrophysics Data System (ADS)

Whitehead, P. G.; Caesar, J.; Crossman, J.; Barbour, E.; Ledesma, J.; Futter, M. N.

2015-12-01

A semi-distributed flow and water quality model (INCA- Integrated Catchments Model) has been set up for the whole of the Ganges- Brahmaputra- Meghna (GBM) River system in India and Bangladesh. These massive rivers transport large fluxes of water and nutrients into the Bay of Bengal via the GBM Delta system in Bangladesh. Future climate change will impact these fluxes with changing rainfall, temperature, evapotranspiration and soil moisture deficits being altered in the catchment systems. In this study the INCA model has been used to assess potential impacts of climate change using the UK Met Office Hadley Centre GCM model linked to a regionally coupled model of South East Asia, covering India and Bangladesh. The Hadley Centre model has been pururbed by varying the parameters in the model to generate 17 realisations of future climates. Some of these reflect expected change but others capture the more extreme potential behaviour of future climate conditions. The 17 realisations have been used to drive the INCA Flow and Nitrogen model inorder to generate downstream times series of hydrology and nitrate- nitrogen. The variability of the climates on these fluxes are investigated and and their likley impact on the Bay of Begal Delta considered. Results indicate a slight shift in the monsoon season with increased wet season flows and increased temperatures which alter nutrient fluxes. Societal Importance to Stakeholders The GBM Delta supports one of the most densely populated regions of people living in poverty, who rely on ecosystem services provided by the Delta for survival. These ecosystem services are dependent upon fluxes of water and nutrients. Freshwater for urban, agriculture, and aquaculture requirements are essential to livelihoods. Nutrient loads stimulate estuarine ecosystems, supporting fishing stocks, which contribute significantly the economy of Bangladesh. Thus the societal importance of upstream climate driven change change in Bangladesh are very significant to many stakeholders in Bangladesh at the local, regional and national levels.

Predicting future US water yield and ecosystem productivity by linking an ecohydrological model to WRF dynamically downscaled climate projections

NASA Astrophysics Data System (ADS)

Sun, S.; Sun, G.; Cohen, E.; McNulty, S. G.; Caldwell, P.; Duan, K.; Zhang, Y.

2015-12-01

Quantifying the potential impacts of climate change on water yield and ecosystem productivity (i.e., carbon balances) is essential to developing sound watershed restoration plans, and climate change adaptation and mitigation strategies. This study links an ecohydrological model (Water Supply and Stress Index, WaSSI) with WRF (Weather Research and Forecasting Model) dynamically downscaled climate projections of the HadCM3 model under the IPCC SRES A2 emission scenario. We evaluated the future (2031-2060) changes in evapotranspiration (ET), water yield (Q) and gross primary productivity (GPP) from the baseline period of 1979-2007 across the 82 773 watersheds (12 digit Hydrologic Unit Code level) in the conterminous US (CONUS), and evaluated the future annual and monthly changes of hydrology and ecosystem productivity for the 18 Water Resource Regions (WRRs) or 2-digit HUCs. Across the CONUS, the future multi-year means show increases in annual precipitation (P) of 45 mm yr-1 (6 %), 1.8 °C increase in temperature (T), 37 mm yr-1 (7 %) increase in ET, 9 mm yr-1 (3 %) increase in Q, and 106 g C m-2 yr-1 (9 %) increase in GPP. Response to climate change was highly variable across the 82, 773 watersheds, but in general, the majority would see consistent increases in all variables evaluated. Over half of the 82 773 watersheds, mostly found in the northeast and the southern part of the southwest would have an increase in annual Q (>100 mm yr-1 or 20 %). This study provides an integrated method and example for comprehensive assessment of the potential impacts of climate change on watershed water balances and ecosystem productivity at high spatial and temporal resolutions. Results will be useful for policy-makers and land managers in formulating appropriate watershed-specific strategies for sustaining water and carbon sources in the face of climate change.

Patterns of Tree Species Diversity in Relation to Climatic Factors on the Sierra Madre Occidental, Mexico

PubMed Central

Silva-Flores, Ramón; Pérez-Verdín, Gustavo; Wehenkel, Christian

2014-01-01

Biological diversity can be defined as variability among living organisms from all sources, including terrestrial organisms, marine and other aquatic ecosystems, and the ecological complexes which they are part of. This includes diversity within species, between species, and of ecosystems. Numerous diversity indices combine richness and evenness in a single expression, and several climate-based explanations have been proposed to explain broad-scale diversity patterns. However, climate-based water-energy dynamics appears to be an essential factor that determines patterns of diversity. The Mexican Sierra Madre Occidental occupies an area of about 29 million hectares and is located between the Neotropical and Holarctic ecozones. It shelters a high diversity of flora, including 24 different species of Pinus (ca. 22% on the whole), 54 species of Quercus (ca. 9–14%), 7 species of Arbutus (ca. 50%) and many other trees species. The objectives of this study were to model how tree species diversity is related to climatic and geographic factors and stand density and to test the Metabolic Theory, Productivity-Diversity Hypothesis, Physiological Tolerance Hypothesis, Mid-Domain Effect, and the Water-Energy Dynamic Theory on the Sierra Madre Occidental, Durango. The results supported the Productivity-Diversity Hypothesis, Physiological Tolerance Hypothesis and Water-Energy Dynamic Theory, but not the Mid-Domain Effect or Metabolic Theory. The annual aridity index was the variable most closely related to the diversity indices analyzed. Contemporary climate was found to have moderate to strong effects on the minimum, median and maximum tree species diversity. Because water-energy dynamics provided a satisfactory explanation for the patterns of minimum, median and maximum diversity, an understanding of this factor is critical to future biodiversity research. Quantile regression of the data showed that the three diversity parameters of tree species are generally higher in cold, humid temperate climates than in dry, hot climates. PMID:25127455

Patterns of tree species diversity in relation to climatic factors on the Sierra Madre Occidental, Mexico.

PubMed

Silva-Flores, Ramón; Pérez-Verdín, Gustavo; Wehenkel, Christian

2014-01-01

Biological diversity can be defined as variability among living organisms from all sources, including terrestrial organisms, marine and other aquatic ecosystems, and the ecological complexes which they are part of. This includes diversity within species, between species, and of ecosystems. Numerous diversity indices combine richness and evenness in a single expression, and several climate-based explanations have been proposed to explain broad-scale diversity patterns. However, climate-based water-energy dynamics appears to be an essential factor that determines patterns of diversity. The Mexican Sierra Madre Occidental occupies an area of about 29 million hectares and is located between the Neotropical and Holarctic ecozones. It shelters a high diversity of flora, including 24 different species of Pinus (ca. 22% on the whole), 54 species of Quercus (ca. 9-14%), 7 species of Arbutus (ca. 50%) and many other trees species. The objectives of this study were to model how tree species diversity is related to climatic and geographic factors and stand density and to test the Metabolic Theory, Productivity-Diversity Hypothesis, Physiological Tolerance Hypothesis, Mid-Domain Effect, and the Water-Energy Dynamic Theory on the Sierra Madre Occidental, Durango. The results supported the Productivity-Diversity Hypothesis, Physiological Tolerance Hypothesis and Water-Energy Dynamic Theory, but not the Mid-Domain Effect or Metabolic Theory. The annual aridity index was the variable most closely related to the diversity indices analyzed. Contemporary climate was found to have moderate to strong effects on the minimum, median and maximum tree species diversity. Because water-energy dynamics provided a satisfactory explanation for the patterns of minimum, median and maximum diversity, an understanding of this factor is critical to future biodiversity research. Quantile regression of the data showed that the three diversity parameters of tree species are generally higher in cold, humid temperate climates than in dry, hot climates.

Climate downscaling over South America for 1971-2000: application in SMAP rainfall-runoff model for Grande River Basin

NASA Astrophysics Data System (ADS)

da Silva, Felipe das Neves Roque; Alves, José Luis Drummond; Cataldi, Marcio

2018-03-01

This paper aims to validate inflow simulations concerning the present-day climate at Água Vermelha Hydroelectric Plant (AVHP—located on the Grande River Basin) based on the Soil Moisture Accounting Procedure (SMAP) hydrological model. In order to provide rainfall data to the SMAP model, the RegCM regional climate model was also used working with boundary conditions from the MIROC model. Initially, present-day climate simulation performed by RegCM model was analyzed. It was found that, in terms of rainfall, the model was able to simulate the main patterns observed over South America. A bias correction technique was also used and it was essential to reduce mistakes related to rainfall simulation. Comparison between rainfall simulations from RegCM and MIROC showed improvements when the dynamical downscaling was performed. Then, SMAP, a rainfall-runoff hydrological model, was used to simulate inflows at Água Vermelha Hydroelectric Plant. After calibration with observed rainfall, SMAP simulations were evaluated in two different periods from the one used in calibration. During calibration, SMAP captures the inflow variability observed at AVHP. During validation periods, the hydrological model obtained better results and statistics with observed rainfall. However, in spite of some discrepancies, the use of simulated rainfall without bias correction captured the interannual flow variability. However, the use of bias removal in the simulated rainfall performed by RegCM brought significant improvements to the simulation of natural inflows performed by SMAP. Not only the curve of simulated inflow became more similar to the observed inflow, but also the statistics improved their values. Improvements were also noticed in the inflow simulation when the rainfall was provided by the regional climate model compared to the global model. In general, results obtained so far prove that there was an added value in rainfall when regional climate model was compared to global climate model and that data from regional models must be bias-corrected so as to improve their results.

Climate Impact of Solar Variability

NASA Technical Reports Server (NTRS)

Schatten, Kenneth H. (Editor); Arking, Albert (Editor)

1990-01-01

The conference on The Climate Impact of Solar Variability, was held at Goddard Space Flight Center from April 24 to 27, 1990. In recent years they developed a renewed interest in the potential effects of increasing greenhouse gases on climate. Carbon dioxide, methane, nitrous oxide, and the chlorofluorocarbons have been increasing at rates that could significantly change climate. There is considerable uncertainty over the magnitude of this anthropogenic change. The climate system is very complex, with feedback processes that are not fully understood. Moreover, there are two sources of natural climate variability (volcanic aerosols and solar variability) added to the anthropogenic changes which may confuse our interpretation of the observed temperature record. Thus, if we could understand the climatic impact of the natural variability, it would aid our interpretation and understanding of man-made climate changes.

Climate variability and vulnerability to climate change: a review

PubMed Central

Thornton, Philip K; Ericksen, Polly J; Herrero, Mario; Challinor, Andrew J

2014-01-01

The focus of the great majority of climate change impact studies is on changes in mean climate. In terms of climate model output, these changes are more robust than changes in climate variability. By concentrating on changes in climate means, the full impacts of climate change on biological and human systems are probably being seriously underestimated. Here, we briefly review the possible impacts of changes in climate variability and the frequency of extreme events on biological and food systems, with a focus on the developing world. We present new analysis that tentatively links increases in climate variability with increasing food insecurity in the future. We consider the ways in which people deal with climate variability and extremes and how they may adapt in the future. Key knowledge and data gaps are highlighted. These include the timing and interactions of different climatic stresses on plant growth and development, particularly at higher temperatures, and the impacts on crops, livestock and farming systems of changes in climate variability and extreme events on pest-weed-disease complexes. We highlight the need to reframe research questions in such a way that they can provide decision makers throughout the food system with actionable answers, and the need for investment in climate and environmental monitoring. Improved understanding of the full range of impacts of climate change on biological and food systems is a critical step in being able to address effectively the effects of climate variability and extreme events on human vulnerability and food security, particularly in agriculturally based developing countries facing the challenge of having to feed rapidly growing populations in the coming decades. PMID:24668802

Comparison of Mediterranean Pistacia lentiscus genotypes by random amplified polymorphic DNA, chemical, and morphological analyses.

PubMed

Barazani, Oz; Dudai, Nativ; Golan-Goldhirsh, Avi

2003-08-01

Characterization of the genetic variability of Mediterranean Pistacia lentiscus genotypes by RAPD, composition of essential oils, and morphology is presented. High polymorphism in morphological parameters was found among accessions, with no significant differences in relation to geographical origin, or to gender. GC-MS analysis of leaves extracted by t-butyl methyl ether, showed 12 monoterpenes, seven sesquiterpenes, and one linear nonterpenic compound. Cluster analysis divided the accessions into two main groups according to the relative content of the major compounds, with no relation to their geographical origin. In contrast, a dendrogram based on RAPD analysis gave two main clusters according to their geographical origins. Low correlation was found between genetic and essential oil content matrices. High morphological and chemical variability on one hand, and genotypic polymorphism on the other, provide ecological advantages that might explain the distribution of Pistacia lentiscus over a wide range of habitats. The plants under study were grown together in the same climatic and environmental conditions, thus pointing to the plausible genetic basis of the observed phenotypic differences.

Climate-driven vital rates do not always mean climate-driven population.

PubMed

Tavecchia, Giacomo; Tenan, Simone; Pradel, Roger; Igual, José-Manuel; Genovart, Meritxell; Oro, Daniel

2016-12-01

Current climatic changes have increased the need to forecast population responses to climate variability. A common approach to address this question is through models that project current population state using the functional relationship between demographic rates and climatic variables. We argue that this approach can lead to erroneous conclusions when interpopulation dispersal is not considered. We found that immigration can release the population from climate-driven trajectories even when local vital rates are climate dependent. We illustrated this using individual-based data on a trans-equatorial migratory seabird, the Scopoli's shearwater Calonectris diomedea, in which the variation of vital rates has been associated with large-scale climatic indices. We compared the population annual growth rate λ i , estimated using local climate-driven parameters with ρ i , a population growth rate directly estimated from individual information and that accounts for immigration. While λ i varied as a function of climatic variables, reflecting the climate-dependent parameters, ρ i did not, indicating that dispersal decouples the relationship between population growth and climate variables from that between climatic variables and vital rates. Our results suggest caution when assessing demographic effects of climatic variability especially in open populations for very mobile organisms such as fish, marine mammals, bats, or birds. When a population model cannot be validated or it is not detailed enough, ignoring immigration might lead to misleading climate-driven projections. © 2016 John Wiley & Sons Ltd.

An experimental test of the role of environmental temperature variability on ectotherm molecular, physiological and life-history traits: implications for global warming.

PubMed

Folguera, Guillermo; Bastías, Daniel A; Caers, Jelle; Rojas, José M; Piulachs, Maria-Dolors; Bellés, Xavier; Bozinovic, Francisco

2011-07-01

Global climate change is one of the greatest threats to biodiversity; one of the most important effects is the increase in the mean earth surface temperature. However, another but poorly studied main characteristic of global change appears to be an increase in temperature variability. Most of the current analyses of global change have focused on mean values, paying less attention to the role of the fluctuations of environmental variables. We experimentally tested the effects of environmental temperature variability on characteristics associated to the fitness (body mass balance, growth rate, and survival), metabolic rate (VCO(2)) and molecular traits (heat shock protein expression, Hsp70), in an ectotherm, the terrestrial woodlouse Porcellio laevis. Our general hypotheses are that higher values of thermal amplitude may directly affect life-history traits, increasing metabolic cost and stress responses. At first, results supported our hypotheses showing a diversity of responses among characters to the experimental thermal treatments. We emphasize that knowledge about the cellular and physiological mechanisms by which animals cope with environmental changes is essential to understand the impact of mean climatic change and variability. Also, we consider that the studies that only incorporate only mean temperatures to predict the life-history, ecological and evolutionary impact of global temperature changes present important problems to predict the diversity of responses of the organism. This is because the analysis ignores the complexity and details of the molecular and physiological processes by which animals cope with environmental variability, as well as the life-history and demographic consequences of such variability. Copyright © 2011 Elsevier Inc. All rights reserved.

Ensembles modeling approach to study Climate Change impacts on Wheat

NASA Astrophysics Data System (ADS)

Ahmed, Mukhtar; Claudio, Stöckle O.; Nelson, Roger; Higgins, Stewart

2017-04-01

Simulations of crop yield under climate variability are subject to uncertainties, and quantification of such uncertainties is essential for effective use of projected results in adaptation and mitigation strategies. In this study we evaluated the uncertainties related to crop-climate models using five crop growth simulation models (CropSyst, APSIM, DSSAT, STICS and EPIC) and 14 general circulation models (GCMs) for 2 representative concentration pathways (RCP) of atmospheric CO2 (4.5 and 8.5 W m-2) in the Pacific Northwest (PNW), USA. The aim was to assess how different process-based crop models could be used accurately for estimation of winter wheat growth, development and yield. Firstly, all models were calibrated for high rainfall, medium rainfall, low rainfall and irrigated sites in the PNW using 1979-2010 as the baseline period. Response variables were related to farm management and soil properties, and included crop phenology, leaf area index (LAI), biomass and grain yield of winter wheat. All five models were run from 2000 to 2100 using the 14 GCMs and 2 RCPs to evaluate the effect of future climate (rainfall, temperature and CO2) on winter wheat phenology, LAI, biomass, grain yield and harvest index. Simulated time to flowering and maturity was reduced in all models except EPIC with some level of uncertainty. All models generally predicted an increase in biomass and grain yield under elevated CO2 but this effect was more prominent under rainfed conditions than irrigation. However, there was uncertainty in the simulation of crop phenology, biomass and grain yield under 14 GCMs during three prediction periods (2030, 2050 and 2070). We concluded that to improve accuracy and consistency in simulating wheat growth dynamics and yield under a changing climate, a multimodel ensemble approach should be used.

MALIBU: A High Spatial Resolution Multi-Angle Imaging Unmanned Airborne System to Validate Satellite-derived BRDF/Albedo Products

NASA Astrophysics Data System (ADS)

Wang, Z.; Roman, M. O.; Pahlevan, N.; Stachura, M.; McCorkel, J.; Bland, G.; Schaaf, C.

2016-12-01

Albedo is a key climate forcing variable that governs the absorption of incoming solar radiation and its ultimate transfer to the atmosphere. Albedo contributes significant uncertainties in the simulation of climate changes; and as such, it is defined by the Global Climate Observing System (GCOS) as a terrestrial essential climate variable (ECV) required by global and regional climate and biogeochemical models. NASA's Goddard Space Flight Center's Multi AngLe Imaging Bidirectional Reflectance Distribution Function small-UAS (MALIBU) is part of a series of pathfinder missions to develop enhanced multi-angular remote sensing techniques using small Unmanned Aircraft Systems (sUAS). The MALIBU instrument package includes two multispectral imagers oriented at two different viewing geometries (i.e., port and starboard sides) capture vegetation optical properties and structural characteristics. This is achieved by analyzing the surface reflectance anisotropy signal (i.e., BRDF shape) obtained from the combination of surface reflectance from different view-illumination angles and spectral channels. Satellite measures of surface albedo from MODIS, VIIRS, and Landsat have been evaluated by comparison with spatially representative albedometer data from sparsely distributed flux towers at fixed heights. However, the mismatch between the footprint of ground measurements and the satellite footprint challenges efforts at validation, especially for heterogeneous landscapes. The BRDF (Bidirectional Reflectance Distribution Function) models of surface anisotropy have only been evaluated with airborne BRDF data over a very few locations. The MALIBU platform that acquires extremely high resolution sub-meter measures of surface anisotropy and surface albedo, can thus serve as an important source of reference data to enable global land product validation efforts, and resolve the errors and uncertainties in the various existing products generated by NASA and its national and international partners.

Temporal and spatial variability of groundwater recharge on Jeju Island, Korea

USGS Publications Warehouse

Mair, Alan; Hagedorn, Benjamin; Tillery, Suzanne; El-Kadi, Aly I.; Westenbroek, Stephen M.; Ha, Kyoochul; Koh, Gi-Won

2013-01-01

Estimates of groundwater recharge spatial and temporal variability are essential inputs to groundwater flow models that are used to test groundwater availability under different management and climate conditions. In this study, a soil water balance analysis was conducted to estimate groundwater recharge on the island of Jeju, Korea, for baseline, drought, and climate-land use change scenarios. The Soil Water Balance (SWB) computer code was used to compute groundwater recharge and other water balance components at a daily time step using a 100 m grid cell size for an 18-year baseline scenario (1992–2009). A 10-year drought scenario was selected from historical precipitation trends (1961–2009), while the climate-land use change scenario was developed using late 21st century climate projections and a change in urban land use. Mean annual recharge under the baseline, drought, and climate-land use scenarios was estimated at 884, 591, and 788 mm, respectively. Under the baseline scenario, mean annual recharge was within the range of previous estimates (825–959 mm) and only slightly lower than the mean of 902 mm. As a fraction of mean annual rainfall, mean annual recharge was computed as only 42% and less than previous estimates of 44–48%. The maximum historical reported annual pumping rate of 241 × 106 m3 equates to 15% of baseline recharge, which is within the range of 14–16% computed from earlier studies. The model does not include a mechanism to account for additional sources of groundwater recharge, such as fog drip, irrigation, and artificial recharge, and may also overestimate evapotranspiration losses. Consequently, the results presented in this study represent a conservative estimate of total recharge.

Variable Trends in High Peak Flow Generation Across the Swedish Sub-Arctic

NASA Astrophysics Data System (ADS)

Matti, B.; Dahlke, H. E.; Lyon, S. W.

2015-12-01

There is growing concern about increased frequency and severity of floods and droughts globally in recent years. Improving knowledge on the complexity of hydrological systems and their interactions with climate is essential to be able to determine drivers of these extreme events and to predict changes in these drivers under altered climate conditions. This is particularly true in cold regions such as the Swedish Sub-Arctic where independent shifts in both precipitation and temperature can have significant influence on extremes. This study explores changes in the magnitude and timing of the annual maximum daily flows in 18 Swedish sub-arctic catchments. The Mann-Kendall trend test was used to estimate changes in selected hydrological signatures. Further, a flood frequency analysis was conducted by fitting a Gumbel (Extreme Value type I) distribution whereby selected flood percentiles were tested for stationarity using a generalized least squares regression approach. Our results showed that hydrological systems in cold climates have complex, heterogeneous interactions with climate. Shifts from a snowmelt-dominated to a rainfall-dominated flow regime were evident with all significant trends pointing towards (1) lower flood magnitudes in the spring flood; (2) earlier flood occurrence; (3) earlier snowmelt onset; and (4) decreasing mean summer flows. Decreasing trends in flood magnitude and mean summer flows suggest permafrost thawing and are in agreement with the increasing trends in annual minimum flows. Trends in the selected flood percentiles showed an increase in extreme events over the entire period of record, while trends were variable under shorter periods. A thorough uncertainty analysis emphasized that the applied trend test is highly sensitive to the period of record considered. As such, no clear overall regional pattern could be determined suggesting that how catchments are responding to changes in climatic drivers is strongly influenced by their physical characteristics.

Sources of variability of evapotranspiration in California

USGS Publications Warehouse

Hidalgo, H.G.; Cayan, D.R.; Dettinger, M.D.

2005-01-01

The variability (1990-2002) of potential evapotranspiration estimates (ETo) and related meteorological variables from a set of stations from the California Irrigation Management System (CIMIS) is studied. Data from the National Climatic Data Center (NCDC) and from the Department of Energy from 1950 to 2001 were used to validate the results. The objective is to determine the characteristics of climatological ETo and to identify factors controlling its variability (including associated atmospheric circulations). Daily ETo anomalies are strongly correlated with net radiation (Rn) anomalies, relative humidity (RH), and cloud cover, and less with average daily temperature (Tavg). The highest intraseasonal variability of ETo daily anomalies occurs during the spring, mainly caused by anomalies below the high ETo seasonal values during cloudy days. A characteristic circulation pattern is associated with anomalies of ETo and its driving meteorological inputs, Rn, RH, and Tavg, at daily to seasonal time scales. This circulation pattern is dominated by 700-hPa geopotential height (Z700) anomalies over a region off the west coast of North America, approximately between 32?? and 44?? latitude, referred to as the California Pressure Anomaly (CPA). High cloudiness and lower than normal ETo are associated with the lowheight (pressure) phase of the CPA pattern. Higher than normal ETo anomalies are associated with clear skies maintained through anomalously high Z700 anomalies offshore of the North American coast. Spring CPA, cloudiness, maximum temperature (Tmax), pan evaporation (Epan), and ETo conditions have not trended significantly or consistently during the second half of the twentieth century in California. Because it is not known how cloud cover and humidity will respond to climate change, the response of ETo in California to increased greenhouse-gas concentrations is essentially unknown; however, to retain the levels of ETo in the current climate, a decline of Rn by about 6% would be required to compensate for a warming of +3??C. ?? 2005 American Meteorological Society.

How resilient are ecosystems in adapting to climate variability

NASA Astrophysics Data System (ADS)

Savenije, Hubert H. G.

2015-04-01

The conclusion often drawn in the media is that ecosystems may perish as a result of climate change. Although climatic trends may indeed lead to shifts in ecosystem composition, the challenge to adjust to climatic variability - even if there is no trend - is larger, particularly in semi-arid or topical climates where climatic variability is large compared to temperate climates. How do ecosystems buffer for climatic variability? The most powerful mechanism is to invest in root zone storage capacity, so as to guarantee access to water and nutrients during period of drought. This investment comes at a cost of having less energy available to invest in growth or formation of fruits. Ecosystems are expected to create sufficient buffer to overcome critical periods of drought, but not more than is necessary to survive or reproduce. Based on this concept, a methodology has been developed to estimate ecosystem root zone storage capacity at local, regional and global scale. These estimates correspond well with estimates made by combining soil and ecosystem information, but are more accurate and more detailed. The methodology shows that ecosystems have intrinsic capacity to adjust to climatic variability and hence have a high resilience to both climatic variability and climatic trends.

THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE

PubMed Central

Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan

2015-01-01

Background: The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran’s universities. Methods: This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran’s public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For predicting the organizational climate pattern of the libraries is used from the multivariate linear regression and track diagram. Results: of the 9 variables affecting organizational climate, 5 variables of innovation, teamwork, customer service, psychological safety and deep diversity play a major role in prediction of the organizational climate of Iran’s libraries. The results also indicate that each of these variables with different coefficient have the power to predict organizational climate but the climate score of psychological safety (0.94) plays a very crucial role in predicting the organizational climate. Track diagram showed that five variables of teamwork, customer service, psychological safety, deep diversity and innovation directly effects on the organizational climate variable that contribution of the team work from this influence is more than any other variables. Conclusions: Of the indicator of the organizational climate of climateQual, the contribution of the team work from this influence is more than any other variables that reinforcement of teamwork in academic libraries can be more effective in improving the organizational climate of this type libraries. PMID:26622203

THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE.

PubMed

Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan

2015-10-01

The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran's universities. This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran's public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For predicting the organizational climate pattern of the libraries is used from the multivariate linear regression and track diagram. of the 9 variables affecting organizational climate, 5 variables of innovation, teamwork, customer service, psychological safety and deep diversity play a major role in prediction of the organizational climate of Iran's libraries. The results also indicate that each of these variables with different coefficient have the power to predict organizational climate but the climate score of psychological safety (0.94) plays a very crucial role in predicting the organizational climate. Track diagram showed that five variables of teamwork, customer service, psychological safety, deep diversity and innovation directly effects on the organizational climate variable that contribution of the team work from this influence is more than any other variables. Of the indicator of the organizational climate of climateQual, the contribution of the team work from this influence is more than any other variables that reinforcement of teamwork in academic libraries can be more effective in improving the organizational climate of this type libraries.

Potential breeding distributions of U.S. birds predicted with both short-term variability and long-term average climate data.

PubMed

Bateman, Brooke L; Pidgeon, Anna M; Radeloff, Volker C; Flather, Curtis H; VanDerWal, Jeremy; Akçakaya, H Resit; Thogmartin, Wayne E; Albright, Thomas P; Vavrus, Stephen J; Heglund, Patricia J

2016-12-01

Climate conditions, such as temperature or precipitation, averaged over several decades strongly affect species distributions, as evidenced by experimental results and a plethora of models demonstrating statistical relations between species occurrences and long-term climate averages. However, long-term averages can conceal climate changes that have occurred in recent decades and may not capture actual species occurrence well because the distributions of species, especially at the edges of their range, are typically dynamic and may respond strongly to short-term climate variability. Our goal here was to test whether bird occurrence models can be predicted by either covariates based on short-term climate variability or on long-term climate averages. We parameterized species distribution models (SDMs) based on either short-term variability or long-term average climate covariates for 320 bird species in the conterminous USA and tested whether any life-history trait-based guilds were particularly sensitive to short-term conditions. Models including short-term climate variability performed well based on their cross-validated area-under-the-curve AUC score (0.85), as did models based on long-term climate averages (0.84). Similarly, both models performed well compared to independent presence/absence data from the North American Breeding Bird Survey (independent AUC of 0.89 and 0.90, respectively). However, models based on short-term variability covariates more accurately classified true absences for most species (73% of true absences classified within the lowest quarter of environmental suitability vs. 68%). In addition, they have the advantage that they can reveal the dynamic relationship between species and their environment because they capture the spatial fluctuations of species potential breeding distributions. With this information, we can identify which species and guilds are sensitive to climate variability, identify sites of high conservation value where climate variability is low, and assess how species' potential distributions may have already shifted due recent climate change. However, long-term climate averages require less data and processing time and may be more readily available for some areas of interest. Where data on short-term climate variability are not available, long-term climate information is a sufficient predictor of species distributions in many cases. However, short-term climate variability data may provide information not captured with long-term climate data for use in SDMs. © 2016 by the Ecological Society of America.

Assessing Current State Science Teaching and Learning Standards for Ability to Achieve Climate Science Literacy

NASA Astrophysics Data System (ADS)

Busch, K. C.

2012-12-01

Even though there exists a high degree of consensus among scientists about climate change, doubt has actually increased over the last five years within the general U.S. public. In 2006, 79% of those polled agreed that there is evidence for global warming, while only 59% agreed in 2010 (Pew Research Center, 2010). The source for this doubt can be partially attributed to lack of knowledge. Formal education is one mechanism that potentially can address inadequate public understanding as school is the primary place where students - and future citizens - learn about the climate. In a joint effort, several governmental agencies, non-governmental organizations, scientists and educators have created a framework called The Essential Principles of Climate Science Literacy, detailing seven concepts that are deemed vital for individuals and communities to understand Earth's climate system (USGCRP, 2009). Can students reach climate literacy - as defined by these 7 concepts - if they are taught using a curriculum based on the current state standards? To answer this question, the K-12 state science teaching and learning standards for Texas and California - two states that heavily influence nation-wide textbook creation - were compared against the Essential Principles. The data analysis consisted of two stages, looking for: 1) direct reference to "climate" and "climate change" and 2) indirect reference to the 7 Essential Principles through axial coding. The word "climate" appears in the California K-12 science standards 4 times and in the Texas standards 7 times. The word "climate change" appears in the California and Texas standards only 3 times each. Indirect references to the 7 Essential Principles of climate science literacy were more numerous. Broadly, California covered 6 of the principles while Texas covered all 7. In looking at the 7 principles, the second one "Climate is regulated by complex interactions among component of the Earth system" was the most substantively addressed. Least covered were number 6 "Human activities are impacting the climate system" and number 7 "Climate change will have consequences for the Earth system and human lives." Most references, either direct or indirect, occurred in the high school standards for earth science, a class not required for graduation in either state. This research points to the gaps between what the 7 Essential Principles of Climate Literacy defines as essential knowledge and what students may learn in their K-12 science classes. Thus, the formal system does not seem to offer an experience which can potentially develop a more knowledgeable citizenry who will be able to make wise personal and policy decisions about climate change, falling short of the ultimate goal of achieving widespread climate literacy. Especially troubling was the sparse attention to the principles addressing the human connection to the climate - principles number 6 and 7. If climate literate citizens are to make "wise personal and policy decisions" (USGCRP, 2009), these two principles especially are vital. This research, therefore, has been valuable for identifying current shortcomings in state standards.

Water-the Nation's Fundamental Climate Issue A White Paper on the U.S. Geological Survey Role and Capabilities

USGS Publications Warehouse

Lins, Harry F.; Hirsch, Robert M.; Kiang, Julie

2010-01-01

Of all the potential threats posed by climatic variability and change, those associated with water resources are arguably the most consequential for both society and the environment (Waggoner, 1990). Climatic effects on agriculture, aquatic ecosystems, energy, and industry are strongly influenced by climatic effects on water. Thus, understanding changes in the distribution, quantity and quality of, and demand for water in response to climate variability and change is essential to planning for and adapting to future climatic conditions. A central role of the U.S. Geological Survey (USGS) with respect to climate is to document environmental changes currently underway and to develop improved capabilities to predict future changes. Indeed, a centerpiece of the USGS role is a new Climate Effects Network of monitoring sites. Measuring the climatic effects on water is an essential component of such a network (along with corresponding effects on terrestrial ecosystems). The USGS needs to be unambiguous in communicating with its customers and stakeholders, and with officials at the Department of the Interior, that although modeling future impacts of climate change is important, there is no more critical role for the USGS in climate change science than that of measuring and describing the changes that are currently underway. One of the best statements of that mission comes from a short paper by Ralph Keeling (2008) that describes the inspiration and the challenges faced by David Keeling in operating the all-important Mauna Loa Observatory over a period of more than four decades. Ralph Keeling stated: 'The only way to figure out what is happening to our planet is to measure it, and this means tracking changes decade after decade and poring over the records.' There are three key ideas that are important to the USGS in the above-mentioned sentence. First, to understand what is happening requires measurement. While models are a tool for learning and testing our understanding, they are not a substitute for observations. The second key idea is that measurement needs to be done over a period of many decades. When viewing hydrologic records over time scales of a few years to a few decades, trends commonly appear. However, when viewed in the context of many decades to centuries, these short-term trends are recognized as being part of much longer term oscillations. Thus, while we might want to initiate monitoring of important aspects of our natural resources, the data that will prove to be most useful in the next few years are those records that already have long-term continuity. USGS streamflow and groundwater level data are excellent examples of such long-term records. These measured data span many decades, follow standard protocols for collection and quality assurance, and are stored in a database that provides access to the full period of record. The third point from the Keeling quote relates to the notion of ?poring over the records.? Important trends will not generally jump off the computer screen at us. Thoughtful analyses are required to get past a number of important but confounding influences in the record, such as the role of seasonal variation, changes in water management, or influences of quasi-periodic phenomena, such as El Ni?o-Southern Oscillation (ENSO) or the Pacific Decadal Oscillation (PDO). No organization is better situated to pore over the records than the USGS because USGS scientists know the data, quality-assure the data, understand the factors that influence the data, and have the ancillary information on the watersheds within which the data are collected. To fulfill the USGS role in understanding climatic variability and change, we need to continually improve and strengthen two of our key capabilities: (1) preserving continuity of long-term water data collection and (2) analyzing and interpreting water data to determine how the Nation's water resources are changing. Understanding change in water resources

Computational data sciences for assessment and prediction of climate extremes

NASA Astrophysics Data System (ADS)

Ganguly, A. R.

2011-12-01

Climate extremes may be defined inclusively as severe weather events or large shifts in global or regional weather patterns which may be caused or exacerbated by natural climate variability or climate change. This area of research arguably represents one of the largest knowledge-gaps in climate science which is relevant for informing resource managers and policy makers. While physics-based climate models are essential in view of non-stationary and nonlinear dynamical processes, their current pace of uncertainty reduction may not be adequate for urgent stakeholder needs. The structure of the models may in some cases preclude reduction of uncertainty for critical processes at scales or for the extremes of interest. On the other hand, methods based on complex networks, extreme value statistics, machine learning, and space-time data mining, have demonstrated significant promise to improve scientific understanding and generate enhanced predictions. When combined with conceptual process understanding at multiple spatiotemporal scales and designed to handle massive data, interdisciplinary data science methods and algorithms may complement or supplement physics-based models. Specific examples from the prior literature and our ongoing work suggests how data-guided improvements may be possible, for example, in the context of ocean meteorology, climate oscillators, teleconnections, and atmospheric process understanding, which in turn can improve projections of regional climate, precipitation extremes and tropical cyclones in an useful and interpretable fashion. A community-wide effort is motivated to develop and adapt computational data science tools for translating climate model simulations to information relevant for adaptation and policy, as well as for improving our scientific understanding of climate extremes from both observed and model-simulated data.

Arctic sea ice trends, variability and implications for seasonal ice forecasting.

PubMed

Serreze, Mark C; Stroeve, Julienne

2015-07-13

September Arctic sea ice extent over the period of satellite observations has a strong downward trend, accompanied by pronounced interannual variability with a detrended 1 year lag autocorrelation of essentially zero. We argue that through a combination of thinning and associated processes related to a warming climate (a stronger albedo feedback, a longer melt season, the lack of especially cold winters) the downward trend itself is steepening. The lack of autocorrelation manifests both the inherent large variability in summer atmospheric circulation patterns and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend. These findings have implications for seasonal ice forecasting. In particular, while advances in observing sea ice thickness and assimilating thickness into coupled forecast systems have improved forecast skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

New Perspectives on the Role of Internal Variability in Regional Climate Change and Climate Model Evaluation

NASA Astrophysics Data System (ADS)

Deser, C.

2017-12-01

Natural climate variability occurs over a wide range of time and space scales as a result of processes intrinsic to the atmosphere, the ocean, and their coupled interactions. Such internally generated climate fluctuations pose significant challenges for the identification of externally forced climate signals such as those driven by volcanic eruptions or anthropogenic increases in greenhouse gases. This challenge is exacerbated for regional climate responses evaluated from short (< 50 years) data records. The limited duration of the observations also places strong constraints on how well the spatial and temporal characteristics of natural climate variability are known, especially on multi-decadal time scales. The observational constraints, in turn, pose challenges for evaluation of climate models, including their representation of internal variability and assessing the accuracy of their responses to natural and anthropogenic radiative forcings. A promising new approach to climate model assessment is the advent of large (10-100 member) "initial-condition" ensembles of climate change simulations with individual models. Such ensembles allow for accurate determination, and straightforward separation, of externally forced climate signals and internal climate variability on regional scales. The range of climate trajectories in a given model ensemble results from the fact that each simulation represents a particular sequence of internal variability superimposed upon a common forced response. This makes clear that nature's single realization is only one of many that could have unfolded. This perspective leads to a rethinking of approaches to climate model evaluation that incorporate observational uncertainty due to limited sampling of internal variability. Illustrative examples across a range of well-known climate phenomena including ENSO, volcanic eruptions, and anthropogenic climate change will be discussed.

Climate-Driven Crop Yield and Yield Variability and Climate Change Impacts on the U.S. Great Plains Agricultural Production.

PubMed

Kukal, Meetpal S; Irmak, Suat

2018-02-22

Climate variability and trends affect global crop yields and are characterized as highly dependent on location, crop type, and irrigation. U.S. Great Plains, due to its significance in national food production, evident climate variability, and extensive irrigation is an ideal region of investigation for climate impacts on food production. This paper evaluates climate impacts on maize, sorghum, and soybean yields and effect of irrigation for individual counties in this region by employing extensive crop yield and climate datasets from 1968-2013. Variability in crop yields was a quarter of the regional average yields, with a quarter of this variability explained by climate variability, and temperature and precipitation explained these in singularity or combination at different locations. Observed temperature trend was beneficial for maize yields, but detrimental for sorghum and soybean yields, whereas observed precipitation trend was beneficial for all three crops. Irrigated yields demonstrated increased robustness and an effective mitigation strategy against climate impacts than their non-irrigated counterparts by a considerable fraction. The information, data, and maps provided can serve as an assessment guide for planners, managers, and policy- and decision makers to prioritize agricultural resilience efforts and resource allocation or re-allocation in the regions that exhibit risk from climate variability.

Environmental literacy framework with a focus on climate change (ELF): a framework and resources for teaching climate change

NASA Astrophysics Data System (ADS)

Huffman, L. T.; Blythe, D.; Dahlman, L. E.; Fischbein, S.; Johnson, K.; Kontar, Y.; Rack, F. R.; Kulhanek, D. K.; Pennycook, J.; Reed, J.; Youngman, B.; Reeves, M.; Thomas, R.

2010-12-01

The challenges of communicating climate change science to non-technical audiences present a daunting task, but one that is recognized in the science community as urgent and essential. ANDRILL's (ANtarctic geological DRILLing) international network of scientists, engineers, technicians and educators work together to convey a deeper understanding of current geoscience research as well as the process of science to non-technical audiences. One roadblock for educators who recognize the need to teach climate change has been the lack of a comprehensive, integrated set of resources and activities that are related to the National Science Education Standards. Pieces of the climate change puzzle can be found in the excellent work of the groups of science and education professionals who wrote the Essential Principles of Ocean Sciences, Climate Literacy: The Essential Principles of Climate Science, Earth Science Literacy Principles: The Big Ideas and Supporting Concepts of Earth Science, and Essential Principals and Fundamental Concepts for Atmospheric Science Literacy, but teachers have precious little time to search out the climate change goals and objectives in those frameworks and then find the resources to teach them. Through NOAA funding, ANDRILL has created a new framework, The Environmental Literacy Framework with a Focus on Climate Change (ELF), drawing on the works of the aforementioned groups, and promoting an Earth Systems approach to teaching climate change through five units: Atmosphere, Biosphere, Geosphere, Hydrosphere/Cryosphere, and Energy as the driver of interactions within and between the “spheres.” Each key concept in the framework has a hands-on, inquiry activity and matching NOAA resources for teaching the objectives. In its present form, we present a ‘road map’ for teaching climate change and a set of resources intended to continue to evolve over time.

Does climate variability influence the demography of wild primates? Evidence from long-term life-history data in seven species.

PubMed

Campos, Fernando A; Morris, William F; Alberts, Susan C; Altmann, Jeanne; Brockman, Diane K; Cords, Marina; Pusey, Anne; Stoinski, Tara S; Strier, Karen B; Fedigan, Linda M

2017-11-01

Earth's rapidly changing climate creates a growing need to understand how demographic processes in natural populations are affected by climate variability, particularly among organisms threatened by extinction. Long-term, large-scale, and cross-taxon studies of vital rate variation in relation to climate variability can be particularly valuable because they can reveal environmental drivers that affect multiple species over extensive regions. Few such data exist for animals with slow life histories, particularly in the tropics, where climate variation over large-scale space is asynchronous. As our closest relatives, nonhuman primates are especially valuable as a resource to understand the roles of climate variability and climate change in human evolutionary history. Here, we provide the first comprehensive investigation of vital rate variation in relation to climate variability among wild primates. We ask whether primates are sensitive to global changes that are universal (e.g., higher temperature, large-scale climate oscillations) or whether they are more sensitive to global change effects that are local (e.g., more rain in some places), which would complicate predictions of how primates in general will respond to climate change. To address these questions, we use a database of long-term life-history data for natural populations of seven primate species that have been studied for 29-52 years to investigate associations between vital rate variation, local climate variability, and global climate oscillations. Associations between vital rates and climate variability varied among species and depended on the time windows considered, highlighting the importance of temporal scale in detection of such effects. We found strong climate signals in the fertility rates of three species. However, survival, which has a greater impact on population growth, was little affected by climate variability. Thus, we found evidence for demographic buffering of life histories, but also evidence of mechanisms by which climate change could affect the fates of wild primates. © 2017 John Wiley & Sons Ltd.

On climate prediction: how much can we expect from climate memory?

NASA Astrophysics Data System (ADS)

Yuan, Naiming; Huang, Yan; Duan, Jianping; Zhu, Congwen; Xoplaki, Elena; Luterbacher, Jürg

2018-03-01

Slowing variability in climate system is an important source of climate predictability. However, it is still challenging for current dynamical models to fully capture the variability as well as its impacts on future climate. In this study, instead of simulating the internal multi-scale oscillations in dynamical models, we discussed the effects of internal variability in terms of climate memory. By decomposing climate state x(t) at a certain time point t into memory part M(t) and non-memory part ɛ (t) , climate memory effects from the past 30 years on climate prediction are quantified. For variables with strong climate memory, high variance (over 20% ) in x(t) is explained by the memory part M(t), and the effects of climate memory are non-negligible for most climate variables, but the precipitation. Regarding of multi-steps climate prediction, a power law decay of the explained variance was found, indicating long-lasting climate memory effects. The explained variances by climate memory can remain to be higher than 10% for more than 10 time steps. Accordingly, past climate conditions can affect both short (monthly) and long-term (interannual, decadal, or even multidecadal) climate predictions. With the memory part M(t) precisely calculated from Fractional Integral Statistical Model, one only needs to focus on the non-memory part ɛ (t) , which is an important quantity that determines climate predictive skills.

Assessing climate impacts

PubMed Central

Wohl, Ellen E.; Pulwarty, Roger S.; Zhang, Jian Yun

2000-01-01

Assessing climate impacts involves identifying sources and characteristics of climate variability, and mitigating potential negative impacts of that variability. Associated research focuses on climate driving mechanisms, biosphere–hydrosphere responses and mediation, and human responses. Examples of climate impacts come from 1998 flooding in the Yangtze River Basin and hurricanes in the Caribbean and Central America. Although we have limited understanding of the fundamental driving-response interactions associated with climate variability, increasingly powerful measurement and modeling techniques make assessing climate impacts a rapidly developing frontier of science. PMID:11027321

Broad-scale lake expansion and flooding inundates essential wood bison habitat in northwestern Canada.

NASA Astrophysics Data System (ADS)

Blais, J. M.; Korosi, J.; Thienpont, J. R.; Pisaric, M. F.; Kokelj, S.; Smol, J. P.; Simpson, M. J.

2017-12-01

Climate change-induced landscape alterations have consequences for vulnerable wildlife. In high-latitude regions, dramatic changes in water levels have been linked to climate warming. While most attention has focused on shrinking Arctic lakes, here, we document the opposite scenario: extensive lake expansion in Canada's Northwest Territories that has implications for the conservation of ecologically-important wood bison. We quantified lake area changes since 1986 using remote sensing techniques, and recorded a net gain of > 500 km2, from 5.7% to 11% total water coverage. Inter-annual variability in water level was significantly correlated to the Pacific/North American pattern teleconnection and the summer sea surface temperature anomaly. Historical reconstructions using proxy data archived in dated sediment cores showed that recent lake expansion is outside the range of natural variability of these ecosystems over at least the last 300 years. Lake expansion resulted in increased allochthonous carbon transport, as shown unequivocally by increases in lignin-derived phenols, but with a greater proportional increase in the contribution of organic matter from phytoplankton, as a result of increased open-water habitat. We conclude that complex hydrological changes occurring as a result of recent climatic change have resulted in rapid and widespread lake expansion that may significantly affect at-risk wildlife populations. This study is based on results we reported in Nature Communications in 2017 (DOI: 10.1038/ncomms14510).

A spring forward for hominin evolution in East Africa.

PubMed

Cuthbert, Mark O; Ashley, Gail M

2014-01-01

Groundwater is essential to modern human survival during drought periods. There is also growing geological evidence of springs associated with stone tools and hominin fossils in the East African Rift System (EARS) during a critical period for hominin evolution (from 1.8 Ma). However it is not known how vulnerable these springs may have been to climate variability and whether groundwater availability may have played a part in human evolution. Recent interdisciplinary research at Olduvai Gorge, Tanzania, has documented climate fluctuations attributable to astronomic forcing and the presence of paleosprings directly associated with archaeological sites. Using palaeogeological reconstruction and groundwater modelling of the Olduvai Gorge paleo-catchment, we show how spring discharge was likely linked to East African climate variability of annual to Milankovitch cycle timescales. Under decadal to centennial timescales, spring flow would have been relatively invariant providing good water resource resilience through long droughts. For multi-millennial periods, modelled spring flows lag groundwater recharge by 100 s to 1000 years. The lag creates long buffer periods allowing hominins to adapt to new habitats as potable surface water from rivers or lakes became increasingly scarce. Localised groundwater systems are likely to have been widespread within the EARS providing refugia and intense competition during dry periods, thus being an important factor in natural selection and evolution, as well as a vital resource during hominin dispersal within and out of Africa.

Towards decadal time series of Arctic and Antarctic sea ice thickness from radar altimetry

NASA Astrophysics Data System (ADS)

Hendricks, S.; Rinne, E. J.; Paul, S.; Ricker, R.; Skourup, H.; Kern, S.; Sandven, S.

2016-12-01

The CryoSat-2 mission has demonstrated the value of radar altimetry to assess the interannual variability and short-term trends of Arctic sea ice over the existing observational record of 6 winter seasons. CryoSat-2 is a particular successful mission for sea ice mass balance assessment due to its novel radar altimeter concept and orbit configuration, but radar altimetry data is available since 1993 from the ERS-1/2 and Envisat missions. Combining these datasets promises a decadal climate data record of sea ice thickness, but inter-mission biases must be taken into account due to the evolution of radar altimeters and the impact of changing sea ice conditions on retrieval algorithm parametrizations. The ESA Climate Change Initiative on Sea Ice aims to extent the list of data records for Essential Climate Variables (ECV's) with a consistent time series of sea ice thickness from available radar altimeter data. We report on the progress of the algorithm development and choices for auxiliary data sets for sea ice thickness retrieval in the Arctic and Antarctic Oceans. Particular challenges are the classification of surface types and freeboard retrieval based on radar waveforms with significantly varying footprint sizes. In addition, auxiliary data sets, e.g. for snow depth, are far less developed in the Antarctic and we will discuss the expected skill of the sea ice thickness ECV's in both hemispheres.

Solar Variability in the Context of Other Climate Forcing Mechanisms

NASA Technical Reports Server (NTRS)

Hansen, James E.

1999-01-01

I compare and contrast climate forcings due to solar variability with climate forcings due to other mechanisms of climate change, interpretation of the role of the sun in climate change depends upon climate sensitivity and upon the net forcing by other climate change mechanisms. Among the potential indirect climate forcings due to solar variability, only that due to solar cycle induced ozone changes has been well quantified. There is evidence that the sun has been a significant player in past climate change on decadal to century time scales, and that it has the potential to contribute to climate change in the 21st century.

Micronutrients for Sustainable Agriculture

USDA-ARS?s Scientific Manuscript database

The micronutrients are essential for plant growth and reproduction, however deficiencies of these elements is major cause of yield decline in many parts of the world. Micronutrients are essential for proper physiological and biological functions in plant. Climatic factors (hot humid climate), soil d...

Flexible stocking as a strategy for enhancing ranch profitability in the face of a changing and variable climate

USDA-ARS?s Scientific Manuscript database

Predicted climate change impacts include increased weather variability and increased occurrences of extreme events such as drought. Such climate changes potentially affect cattle performance as well as pasture and range productivity. These climate induced risks are often coupled with variable market...

Statistical structure of intrinsic climate variability under global warming

NASA Astrophysics Data System (ADS)

Zhu, Xiuhua; Bye, John; Fraedrich, Klaus

2017-04-01

Climate variability is often studied in terms of fluctuations with respect to the mean state, whereas the dependence between the mean and variability is rarely discussed. We propose a new climate metric to measure the relationship between means and standard deviations of annual surface temperature computed over non-overlapping 100-year segments. This metric is analyzed based on equilibrium simulations of the Max Planck Institute-Earth System Model (MPI-ESM): the last millennium climate (800-1799), the future climate projection following the A1B scenario (2100-2199), and the 3100-year unforced control simulation. A linear relationship is globally observed in the control simulation and thus termed intrinsic climate variability, which is most pronounced in the tropical region with negative regression slopes over the Pacific warm pool and positive slopes in the eastern tropical Pacific. It relates to asymmetric changes in temperature extremes and associates fluctuating climate means with increase or decrease in intensity and occurrence of both El Niño and La Niña events. In the future scenario period, the linear regression slopes largely retain their spatial structure with appreciable changes in intensity and geographical locations. Since intrinsic climate variability describes the internal rhythm of the climate system, it may serve as guidance for interpreting climate variability and climate change signals in the past and the future.

Analysing the teleconnection systems affecting the climate of the Carpathian Basin

NASA Astrophysics Data System (ADS)

Kristóf, Erzsébet; Bartholy, Judit; Pongrácz, Rita

2017-04-01

Nowadays, the increase of the global average near-surface air temperature is unequivocal. Atmospheric low-frequency variabilities have substantial impacts on climate variables such as air temperature and precipitation. Therefore, assessing their effects is essential to improve global and regional climate model simulations for the 21st century. The North Atlantic Oscillation (NAO) is one of the best-known atmospheric teleconnection patterns affecting the Carpathian Basin in Central Europe. Besides NAO, we aim to analyse other interannual-to-decadal teleconnection patterns, which might have significant impacts on the Carpathian Basin, namely, the East Atlantic/West Russia pattern, the Scandinavian pattern, the Mediterranean Oscillation, and the North-Sea Caspian Pattern. For this purpose primarily the European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA-20C atmospheric reanalysis dataset and multivariate statistical methods are used. The indices of each teleconnection pattern and their correlations with temperature and precipitation will be calculated for the period of 1961-1990. On the basis of these data first the long range (i. e. seasonal and/or annual scale) forecast ability is evaluated. Then, we aim to calculate the same indices of the relevant teleconnection patterns for the historical and future simulations of Coupled Model Intercomparison Project Phase 5 (CMIP5) models and compare them against each other using statistical methods. Our ultimate goal is to examine all available CMIP5 models and evaluate their abilities to reproduce the selected teleconnection systems. Thus, climate predictions for the 21st century for the Carpathian Basin may be improved using the best-performing models among all CMIP5 model simulations.

Deriving a sea surface climatology of CO2 fugacity in support of air-sea gas flux studies

NASA Astrophysics Data System (ADS)

Goddijn-Murphy, L. M.; Woolf, D. K.; Land, P. E.; Shutler, J. D.; Donlon, C.

2014-07-01

Climatologies, or long-term averages, of essential climate variables are useful for evaluating models and providing a baseline for studying anomalies. The Surface Ocean Carbon Dioxide (CO2) Atlas (SOCAT) has made millions of global underway sea surface measurements of CO2 publicly available, all in a uniform format and presented as fugacity, fCO2. fCO2 is highly sensitive to temperature and the measurements are only valid for the instantaneous sea surface temperature (SST) that is measured concurrent with the in-water CO2 measurement. To create a climatology of fCO2 data suitable for calculating air-sea CO2 fluxes it is therefore desirable to calculate fCO2 valid for climate quality SST. This paper presents a method for creating such a climatology. We recomputed SOCAT's fCO2 values for their respective measurement month and year using climate quality SST data from satellite Earth observation and then extrapolated the resulting fCO2 values to reference year 2010. The data were then spatially interpolated onto a 1° × 1° grid of the global oceans to produce 12 monthly fCO2 distributions for 2010. The partial pressure of CO2 (pCO2) is also provided for those who prefer to use pCO2. The CO2 concentration difference between ocean and atmosphere is the thermodynamic driving force of the air-sea CO2 flux, and hence the presented fCO2 distributions can be used in air-sea gas flux calculations together with climatologies of other climate variables.

Linking global climate and temperature variability to widespread amphibian declines putatively caused by disease.

PubMed

Rohr, Jason R; Raffel, Thomas R

2010-05-04

The role of global climate change in the decline of biodiversity and the emergence of infectious diseases remains controversial, and the effect of climatic variability, in particular, has largely been ignored. For instance, it was recently revealed that the proposed link between climate change and widespread amphibian declines, putatively caused by the chytrid fungus Batrachochytrium dendrobatidis (Bd), was tenuous because it was based on a temporally confounded correlation. Here we provide temporally unconfounded evidence that global El Niño climatic events drive widespread amphibian losses in genus Atelopus via increased regional temperature variability, which can reduce amphibian defenses against pathogens. Of 26 climate variables tested, only factors associated with temperature variability could account for the spatiotemporal patterns of declines thought to be associated with Bd. Climatic predictors of declines became significant only after controlling for a pattern consistent with epidemic spread (by temporally detrending the data). This presumed spread accounted for 59% of the temporal variation in amphibian losses, whereas El Niño accounted for 59% of the remaining variation. Hence, we could account for 83% of the variation in declines with these two variables alone. Given that global climate change seems to increase temperature variability, extreme climatic events, and the strength of Central Pacific El Niño episodes, climate change might exacerbate worldwide enigmatic declines of amphibians, presumably by increasing susceptibility to disease. These results suggest that changes to temperature variability associated with climate change might be as significant to biodiversity losses and disease emergence as changes to mean temperature.

Effects of short-term variability of meteorological variables on soil temperature in permafrost regions

NASA Astrophysics Data System (ADS)

Beer, Christian; Porada, Philipp; Ekici, Altug; Brakebusch, Matthias

2018-03-01

Effects of the short-term temporal variability of meteorological variables on soil temperature in northern high-latitude regions have been investigated. For this, a process-oriented land surface model has been driven using an artificially manipulated climate dataset. Short-term climate variability mainly impacts snow depth, and the thermal diffusivity of lichens and bryophytes. These impacts of climate variability on insulating surface layers together substantially alter the heat exchange between atmosphere and soil. As a result, soil temperature is 0.1 to 0.8 °C higher when climate variability is reduced. Earth system models project warming of the Arctic region but also increasing variability of meteorological variables and more often extreme meteorological events. Therefore, our results show that projected future increases in permafrost temperature and active-layer thickness in response to climate change will be lower (i) when taking into account future changes in short-term variability of meteorological variables and (ii) when representing dynamic snow and lichen and bryophyte functions in land surface models.

Indicators and metrics for the assessment of climate engineering

NASA Astrophysics Data System (ADS)

Oschlies, A.; Held, H.; Keller, D.; Keller, K.; Mengis, N.; Quaas, M.; Rickels, W.; Schmidt, H.

2017-01-01

Selecting appropriate indicators is essential to aggregate the information provided by climate model outputs into a manageable set of relevant metrics on which assessments of climate engineering (CE) can be based. From all the variables potentially available from climate models, indicators need to be selected that are able to inform scientists and society on the development of the Earth system under CE, as well as on possible impacts and side effects of various ways of deploying CE or not. However, the indicators used so far have been largely identical to those used in climate change assessments and do not visibly reflect the fact that indicators for assessing CE (and thus the metrics composed of these indicators) may be different from those used to assess global warming. Until now, there has been little dedicated effort to identifying specific indicators and metrics for assessing CE. We here propose that such an effort should be facilitated by a more decision-oriented approach and an iterative procedure in close interaction between academia, decision makers, and stakeholders. Specifically, synergies and trade-offs between social objectives reflected by individual indicators, as well as decision-relevant uncertainties should be considered in the development of metrics, so that society can take informed decisions about climate policy measures under the impression of the options available, their likely effects and side effects, and the quality of the underlying knowledge base.

Timing of climate variability and grassland productivity

PubMed Central

Craine, Joseph M.; Nippert, Jesse B.; Elmore, Andrew J.; Skibbe, Adam M.; Hutchinson, Stacy L.; Brunsell, Nathaniel A.

2012-01-01

Future climates are forecast to include greater precipitation variability and more frequent heat waves, but the degree to which the timing of climate variability impacts ecosystems is uncertain. In a temperate, humid grassland, we examined the seasonal impacts of climate variability on 27 y of grass productivity. Drought and high-intensity precipitation reduced grass productivity only during a 110-d period, whereas high temperatures reduced productivity only during 25 d in July. The effects of drought and heat waves declined over the season and had no detectable impact on grass productivity in August. If these patterns are general across ecosystems, predictions of ecosystem response to climate change will have to account not only for the magnitude of climate variability but also for its timing. PMID:22331914

Terrestrial carbon turnover time constraints on future carbon cycle-climate feedback

NASA Astrophysics Data System (ADS)

Fan, N.; Carvalhais, N.; Reichstein, M.

2017-12-01

Understanding the terrestrial carbon cycle-climate feedback is essential to reduce the uncertainties resulting from the between model spread in prognostic simulations (Friedlingstein et al., 2006). One perspective is to investigate which factors control the variability of the mean residence times of carbon in the land surface, and how these may change in the future, consequently affecting the response of the terrestrial ecosystems to changes in climate as well as other environmental conditions. Carbon turnover time of the whole ecosystem is a dynamic parameter that represents how fast the carbon cycle circulates. Turnover time τ is an essential property for understanding the carbon exchange between the land and the atmosphere. Although current Earth System Models (ESMs), supported by GVMs for the description of the land surface, show a strong convergence in GPP estimates, but tend to show a wide range of simulated turnover times (Carvalhais, 2014). Thus, there is an emergent need of constraints on the projected response of the balance between terrestrial carbon fluxes and carbon stock which will give us more certainty in response of carbon cycle to climate change. However, the difficulty of obtaining such a constraint is partly due to lack of observational data on temporal change of terrestrial carbon stock. Since more new datasets of carbon stocks such as SoilGrid (Hengl, et al., 2017) and fluxes such as GPP (Jung, et al., 2017) are available, improvement in estimating turnover time can be achieved. In addition, previous study ignored certain aspects such as the relationship between τ and nutrients, fires, etc. We would like to investigate τ and its role in carbon cycle by combining observatinoal derived datasets and state-of-the-art model simulations.

Selection of climate change scenario data for impact modelling.

PubMed

Sloth Madsen, M; Maule, C Fox; MacKellar, N; Olesen, J E; Christensen, J Hesselbjerg

2012-01-01

Impact models investigating climate change effects on food safety often need detailed climate data. The aim of this study was to select climate change projection data for selected crop phenology and mycotoxin impact models. Using the ENSEMBLES database of climate model output, this study illustrates how the projected climate change signal of important variables as temperature, precipitation and relative humidity depends on the choice of the climate model. Using climate change projections from at least two different climate models is recommended to account for model uncertainty. To make the climate projections suitable for impact analysis at the local scale a weather generator approach was adopted. As the weather generator did not treat all the necessary variables, an ad-hoc statistical method was developed to synthesise realistic values of missing variables. The method is presented in this paper, applied to relative humidity, but it could be adopted to other variables if needed.

Means and extremes: building variability into community-level climate change experiments.

PubMed

Thompson, Ross M; Beardall, John; Beringer, Jason; Grace, Mike; Sardina, Paula

2013-06-01

Experimental studies assessing climatic effects on ecological communities have typically applied static warming treatments. Although these studies have been informative, they have usually failed to incorporate either current or predicted future, patterns of variability. Future climates are likely to include extreme events which have greater impacts on ecological systems than changes in means alone. Here, we review the studies which have used experiments to assess impacts of temperature on marine, freshwater and terrestrial communities, and classify them into a set of 'generations' based on how they incorporate variability. The majority of studies have failed to incorporate extreme events. In terrestrial ecosystems in particular, experimental treatments have reduced temperature variability, when most climate models predict increased variability. Marine studies have tended to not concentrate on changes in variability, likely in part because the thermal mass of oceans will moderate variation. In freshwaters, climate change experiments have a much shorter history than in the other ecosystems, and have tended to take a relatively simple approach. We propose a new 'generation' of climate change experiments using down-scaled climate models which incorporate predicted changes in climatic variability, and describe a process for generating data which can be applied as experimental climate change treatments. © 2013 John Wiley & Sons Ltd/CNRS.

How potentially predictable are midlatitude ocean currents?

PubMed Central

Nonaka, Masami; Sasai, Yoshikazu; Sasaki, Hideharu; Taguchi, Bunmei; Nakamura, Hisashi

2016-01-01

Predictability of atmospheric variability is known to be limited owing to significant uncertainty that arises from intrinsic variability generated independently of external forcing and/or boundary conditions. Observed atmospheric variability is therefore regarded as just a single realization among different dynamical states that could occur. In contrast, subject to wind, thermal and fresh-water forcing at the surface, the ocean circulation has been considered to be rather deterministic under the prescribed atmospheric forcing, and it still remains unknown how uncertain the upper-ocean circulation variability is. This study evaluates how much uncertainty the oceanic interannual variability can potentially have, through multiple simulations with an eddy-resolving ocean general circulation model driven by the observed interannually-varying atmospheric forcing under slightly different conditions. These ensemble “hindcast” experiments have revealed substantial uncertainty due to intrinsic variability in the extratropical ocean circulation that limits potential predictability of its interannual variability, especially along the strong western boundary currents (WBCs) in mid-latitudes, including the Kuroshio and its eastward extention. The intrinsic variability also greatly limits potential predictability of meso-scale oceanic eddy activity. These findings suggest that multi-member ensemble simulations are essential for understanding and predicting variability in the WBCs, which are important for weather and climate variability and marine ecosystems. PMID:26831954

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources

NASA Astrophysics Data System (ADS)

Forsythe, N.; Blenkinsop, S.; Fowler, H. J.

2015-05-01

A three-step climate classification was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal component analysis (PCA) of those variables and k-means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight macro-climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential for enhancing assessment of climatic influences on water resources and food security as well as for characterising the skill and bias of gridded data sets, both meteorological reanalyses and climate models, for reproducing subregional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.

Atmospheric Teleconnection and Climate Variability: Affecting Rice Productivity of Bihar, India

NASA Astrophysics Data System (ADS)

Saini, A.

2017-12-01

Climate variability brought various negative results to the environment around us and area under rice crop in Bihar has also faced a lot of negative impacts due to variability in temperature and rainfall. Location of Bihar in Northern Plain of India automatically makes it prime location for agriculture and therefore variability in climatic variables brings highly sensitive results to the agricultural production (especially rice). In this study, rainfall and temperature variables are taken into consideration to investigate the impact on rice cultivated area. Change in climate variable with the passage of time is prevailing since the start of geological time scale, how the variability in climate variables has affected the major crops. Climate index of Pacific Ocean and Indian Ocean influences the seasonal weather in Bihar and therefore role of ENSO and IOD is an interesting point of inquiry. Does there exists direct relation between climate variability and area under agricultural crops? How many important variables directly signals towards the change in area under agriculture production? These entire questions are answered with respect to change in area under rice cultivation of Bihar State of India. Temperature, rainfall and ENSO are a good indicator with respect to rice cultivation in Indian subcontinent. Impact on the area under rice has been signaled through ONI, Niño3 and DMI. Increasing range of temperature in the rice productivity declining years is observed since 1990.

A National Road Map to a Climate Literate Society: Advancing Climate Literacy by Coordinating Federal Climate Change Educational Programs (Invited)

NASA Astrophysics Data System (ADS)

Niepold, F.; Karsten, J. L.

2009-12-01

Over the 21st century, climate scientists expect Earth's temperature to continue increasing, very likely more than it did during the 20th century. Two anticipated results are rising global sea level and increasing frequency and intensity of heat waves, droughts, and floods. [IPCC 2007, USGCRP 2009] These changes will affect almost every aspect of human society, including economic prosperity, human and environmental health, and national security. Climate change will bring economic and environmental challenges as well as opportunities, and citizens who have an understanding of climate science will be better prepared to respond to both. Society needs citizens who understand the climate system and know how to apply that knowledge in their careers and in their engagement as active members of their communities. Climate change will continue to be a significant element of public discourse. Understanding the essential principles of climate science will enable all people to assess news stories and contribute to their everyday conversations as informed citizens. Key to our nations response to climate change will be a Climate Literate society that understands their influence on climate and climate’s influence on them and society. In order to ensure the nation increases its literacy, the Climate Literacy: Essential Principles of Climate Science document has been endorsed by the 13 Federal agencies that make up the US Global Change Research Program (http://globalchange.gov/resources/educators/climate-literacy) and twenty-four other science and educational institutions. This session will explore the coordinated efforts by the federal agencies and partner organizations to ensure a climate literate society. "Climate Literacy: The Essential Principles of Climate Sciences: A Guide for Individuals and Communities" produced by the U.S. Global Change Research Program in March 2009

Projecting water yield and ecosystem productivity across the United States by linking an ecohydrological model to WRF dynamically downscaled climate data

NASA Astrophysics Data System (ADS)

Sun, Shanlei; Sun, Ge; Cohen, Erika; McNulty, Steven G.; Caldwell, Peter V.; Duan, Kai; Zhang, Yang

2016-03-01

Quantifying the potential impacts of climate change on water yield and ecosystem productivity is essential to developing sound watershed restoration plans, and ecosystem adaptation and mitigation strategies. This study links an ecohydrological model (Water Supply and Stress Index, WaSSI) with WRF (Weather Research and Forecasting Model) using dynamically downscaled climate data of the HadCM3 model under the IPCC SRES A2 emission scenario. We evaluated the future (2031-2060) changes in evapotranspiration (ET), water yield (Q) and gross primary productivity (GPP) from the baseline period of 1979-2007 across the 82 773 watersheds (12-digit Hydrologic Unit Code level) in the coterminous US (CONUS). Across the CONUS, the future multi-year means show increases in annual precipitation (P) of 45 mm yr-1 (6 %), 1.8° C increase in temperature (T), 37 mm yr-1 (7 %) increase in ET, 9 mm yr-1 (3 %) increase in Q, and 106 gC m-2 yr-1 (9 %) increase in GPP. We found a large spatial variability in response to climate change across the CONUS 12-digit HUC watersheds, but in general, the majority would see consistent increases all variables evaluated. Over half of the watersheds, mostly found in the northeast and the southern part of the southwest, would see an increase in annual Q (> 100 mm yr-1 or 20 %). In addition, we also evaluated the future annual and monthly changes of hydrology and ecosystem productivity for the 18 Water Resource Regions (WRRs) or two-digit HUCs. The study provides an integrated method and example for comprehensive assessment of the potential impacts of climate change on watershed water balances and ecosystem productivity at high spatial and temporal resolutions. Results may be useful for policy-makers and land managers to formulate appropriate watershed-specific strategies for sustaining water and carbon sources in the face of climate change.

Framework for a U.S. Geological Survey Hydrologic Climate-Response Program in Maine

USGS Publications Warehouse

Hodgkins, Glenn A.; Lent, Robert M.; Dudley, Robert W.; Schalk, Charles W.

2009-01-01

This report presents a framework for a U.S. Geological Survey (USGS) hydrologic climate-response program designed to provide early warning of changes in the seasonal water cycle of Maine. Climate-related hydrologic changes on Maine's rivers and lakes in the winter and spring during the last century are well documented, and several river and lake variables have been shown to be sensitive to air-temperature changes. Monitoring of relevant hydrologic data would provide important baseline information against which future climate change can be measured. The framework of the hydrologic climate-response program presented here consists of four major parts: (1) identifying homogeneous climate-response regions; (2) identifying hydrologic components and key variables of those components that would be included in a hydrologic climate-response data network - as an example, streamflow has been identified as a primary component, with a key variable of streamflow being winter-spring streamflow timing; the data network would be created by maintaining existing USGS data-collection stations and establishing new ones to fill data gaps; (3) regularly updating historical trends of hydrologic data network variables; and (4) establishing basins for process-based studies. Components proposed for inclusion in the hydrologic climate-response data network have at least one key variable for which substantial historical data are available. The proposed components are streamflow, lake ice, river ice, snowpack, and groundwater. The proposed key variables of each component have extensive historical data at multiple sites and are expected to be responsive to climate change in the next few decades. These variables are also important for human water use and (or) ecosystem function. Maine would be divided into seven climate-response regions that follow major river-basin boundaries (basins subdivided to hydrologic units with 8-digit codes or larger) and have relatively homogeneous climates. Key hydrologic variables within each climate-response region would be analyzed regularly to maintain up-to-date analyses of year-to-year variability, decadal variability, and longer term trends. Finally, one basin in each climate-response region would be identified for process-based hydrologic and ecological studies.

Climate change

USGS Publications Warehouse

Cronin, Thomas M.

2016-01-01

Climate change (including climate variability) refers to regional or global changes in mean climate state or in patterns of climate variability over decades to millions of years often identified using statistical methods and sometimes referred to as changes in long-term weather conditions (IPCC, 2012). Climate is influenced by changes in continent-ocean configurations due to plate tectonic processes, variations in Earth’s orbit, axial tilt and precession, atmospheric greenhouse gas (GHG) concentrations, solar variability, volcanism, internal variability resulting from interactions between the atmosphere, oceans and ice (glaciers, small ice caps, ice sheets, and sea ice), and anthropogenic activities such as greenhouse gas emissions and land use and their effects on carbon cycling.

Open access to Water Indicators for Climate Change Adaptation: proof-of-concept for the Copernicus Climate Change Service (C3S)

NASA Astrophysics Data System (ADS)

Lottle, Lorna; Arheimer, Berit; Gyllensvärd, Frida; Dejong, Fokke; Ludwig, Fulco; Hutjes, Ronald; Martinez, Bernat

2017-04-01

Copernicus Climate Change Service (C3S) is still in the development phase and will combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate and climate dependent sectors in Europe and worldwide. C3S will provide key indicators on climate change drivers and selected sectorial impacts. The aim of these indicators will be to support adaptation and mitigation. This presentation will show one service already operational as a proof-of-concept of this future climate service. The project "Service for Water Indicators in Climate Change Adaptation" (SWICCA) has developed a sectorial information service for water management. It offers readily available climate-impact data, for open access from the web-site http://swicca.climate.copernicus.eu/. The development is user-driven with the overall goal to speed up the workflow in climate-change adaptation of water management across Europe. The service is co-designed by consultant engineers and agencies in 15 case-studies spread out over the continent. SWICCA has an interactive user-interface, which shows maps and graphs, and facilitates data download in user-friendly formats. In total, more than 900 open dataset are given for various hydrometeorological (and a few socioeconomical) variables, model ensembles, resolutions, time-periods and RCPs. The service offers more than 40 precomputed climate impact indicators (CIIs) and transient time-series of 4 essential climate variables ECVs) with high spatial and temporal resolution. To facilitate both near future and far future assessments, SWICCA provides the indicators for different time ranges; normally, absolute values are given for a reference period (e.g. 1971-2000) and the expected future changes for different 30-year periods, such as early century (2011-2040), mid-century (2041-2070) and end-century (2071-2100). An ensemble of model results is always given to indicate confidence in the estimates. The SWICCA demonstrator also includes user guidance, information sheets, tutorials, and links to other relevant websites. The aim of this service is to provide research data and guidance for climate impact assessments in the water sector. The main target group is consulting engineers (so called Purveyors) working with climate change adaptation in the water sector. By using indicators, climate impact assessments can be done without having to run a full production chain from raw climate model results - instead the indicators can be included in the local workflow with local methods applied, to facilitate decision-making and strategies to meet the future. Working with real users will ensure that useful data is inserted into the C3S Climate Data Store (CDS).

Effects of amphibian phylogeny, climate and human impact on the occurrence of the amphibian-killing chytrid fungus.

PubMed

Bacigalupe, Leonardo D; Soto-Azat, Claudio; García-Vera, Cristobal; Barría-Oyarzo, Ismael; Rezende, Enrico L

2017-09-01

Chytridiomycosis, due to the fungus Batrachochytrium dendrobatidis (Bd), has been associated with the alarming decline and extinction crisis of amphibians worldwide. Because conservation programs are implemented locally, it is essential to understand how the complex interactions among host species, climate and human activities contribute to Bd occurrence at regional scales. Using weighted phylogenetic regressions and model selection, we investigated geographic patterns of Bd occurrence along a latitudinal gradient of 1500 km within a biodiversity hot spot in Chile (1845 individuals sampled from 253 sites and representing 24 species), and its association with climatic, socio-demographic and economic variables. Analyses show that Bd prevalence decreases with latitude although it has increased by almost 10% between 2008 and 2013, possibly reflecting an ongoing spread of Bd following the introduction of Xenopus laevis. Occurrence of Bd was higher in regions with high gross domestic product (particularly near developed centers) and with a high variability in rainfall regimes, whereas models including other bioclimatic or geographic variables, including temperature, exhibited substantially lower fit and virtually no support based on Akaike weights. In addition, Bd prevalence exhibited a strong phylogenetic signal, with five species having high numbers of infected individuals and higher prevalence than the average of 13.3% across all species. Taken together, our results highlight that Bd in Chile might still be spreading south, facilitated by a subset of species that seem to play an important epidemiological role maintaining this pathogen in the communities, in combination with climatic and human factors affecting the availability and quality of amphibian breeding sites. This information may be employed to design conservation strategies and mitigate the impacts of Bd in the biodiversity hot spot of southern Chile, and similar studies may prove useful to disentangle the role of different factors contributing to the emergence and spread of this catastrophic disease. © 2017 John Wiley & Sons Ltd.

THE INFLUENCE OF VARIABLE TEMPERATURE AND HUMIDITY ON THE PREDATION EFFICIENCY OF P. PERSIMILIS, N. CALIFORNICUS AND N. FALLACIS.

PubMed

Audenaert, J; Vangansbeke, D; Verhoeven, R; De Clercq, P; Tirry, L; Gobin, B

2014-01-01

Predatory mites like Phytoseiulus persimilis Athias-Henriot, Neoseiulus californicus McGregor and N. fallacis (Garman) (Acari: Phytoseiidae) are essential in sustainable control strategies of the two-spotted spider mite Tetranychus urticae Koch (Acari: Tetranychidae) in warm greenhouse cultures to complement imited available pesticides and to tackle emerging resistance. However, in response to high energy prices, greenhouse plant breeders have recently changed their greenhouse steering strategies, allowing more variation in temperature and humidity. The impact of these variations on biological control agents is poorly understood. Therefore, we constructed functional response models to demonstrate the impact of realistic climate variations on predation efficiency. First, two temperature regimes were compared at constant humidity (70%) and photoperiod (16L:8D): DIF0 (constant temperature) and DIF15 (variable temperature with day-night difference of 15°C). At mean temperatures of 25°C, DIF15 had a negative influence on the predation efficiency of P. persimilis and N. californicus, as compared to DIF0. At low mean temperatures of 15°C, however, DIF15 showed a higher predation efficiency for P. persimilis and N. californicus. For N. fallacis no difference was observed at both 15°C and 25°C. Secondly, two humidity regimes were compared, at a mean temperature of 25°C (DIFO) and constant photoperiod (16L:8D): RHCTE (constant 70% humidity) and RHALT (alternating 40% L:70%D humidity). For P. persimilis and N. fallacis RHCTE resulted in a higher predation efficiency than RHALT, for N. californicus this effect was opposite. This shows that N. californicus is more adapted to dry climates as compared to the other predatory mites. We conclude that variable greenhouse climates clearly affect predation efficiency of P. persimilis, N. californicus and N. fallacis. To obtain optimal control efficiency, the choice of predatory mites (including dose and application frequency) should be adapted to the actual greenhouse climate.

Impacts of climate change under CMIP5 RCP scenarios on the streamflow in the Dinder River and ecosystem habitats in Dinder National Park, Sudan

NASA Astrophysics Data System (ADS)

Basheer, Amir K.; Lu, Haishen; Omer, Abubaker; Ali, Abubaker B.; Abdelgader, Abdeldime M. S.

2016-04-01

The fate of seasonal river ecosystem habitats under climate change essentially depends on the changes in annual recharge of the river, which are related to alterations in precipitation and evaporation over the river basin. Therefore, the change in climate conditions is expected to significantly affect hydrological and ecological components, particularly in fragmented ecosystems. This study aims to assess the impacts of climate change on the streamflow in the Dinder River basin (DRB) and to infer its relative possible effects on the Dinder National Park (DNP) ecosystem habitats in Sudan. Four global circulation models (GCMs) from Coupled Model Intercomparison Project Phase 5 and two statistical downscaling approaches combined with a hydrological model (SWAT - the Soil and Water Assessment Tool) were used to project the climate change conditions over the study periods 2020s, 2050s, and 2080s. The results indicated that the climate over the DRB will become warmer and wetter under most scenarios. The projected precipitation variability mainly depends on the selected GCM and downscaling approach. Moreover, the projected streamflow is quite sensitive to rainfall and temperature variation, and will likely increase in this century. In contrast to drought periods during the 1960s, 1970s, and 1980s, the predicted climate change is likely to affect ecosystems in DNP positively and promote the ecological restoration for the habitats of flora and fauna.

Growth-climate relationships across topographic gradients in the northern Great Lakes

USGS Publications Warehouse

Dymond, S.F.; D'Amato, A.W.; Kolka, R.K.; Bolstad, P.V.; Sebestyen, S.D.; Bradford, John B.

2016-01-01

Climatic conditions exert important control over the growth, productivity, and distribution of forests, and characterizing these relationships is essential for understanding how forest ecosystems will respond to climate change. We used dendrochronological methods to develop climate–growth relationships for two dominant species, Populus tremuloides (quaking aspen) and Pinus resinosa (red pine), in the upper Great Lakes region to understand how climate and water availability influence annual forest productivity. Trees were sampled along a topographic gradient at the Marcell Experimental Forest (Minnesota, USA) to assess growth response to variations in temperature and different water availability metrics (precipitation, potential evapotranspiration (PET), cumulative moisture index (CMI), and soil water storage). Climatic variables were able to explain 33–58% of the variation in annual growth (as measured by ring-width increment) for quaking aspen and 37–74% of the variation for red pine. Climate–growth relationships were influenced by topography for quaking aspen but not for red pine. Annual ring growth for quaking aspen decreased with June CMI on ridges, decreased with temperature in the November prior to the growing season on sideslopes, and decreased with June PET on toeslopes. Red pine growth increased with increasing July PET across all topographic positions. These results indicate the sensitivity of both quaking aspen and red pine to local climate and show several vulnerabilities of these species to shifts in water supply and temperature because of climate change.

Analysis of the Relationship Between Climate and NDVI Variability at Global Scales

NASA Technical Reports Server (NTRS)

Zeng, Fan-Wei; Collatz, G. James; Pinzon, Jorge; Ivanoff, Alvaro

2011-01-01

interannual variability in modeled (CASA) C flux is in part caused by interannual variability in Normalized Difference Vegetation Index (NDVI) Fraction of Photosynthetically Active Radiation (FPAR). This study confirms a mechanism producing variability in modeled NPP: -- NDVI (FPAR) interannual variability is strongly driven by climate; -- The climate driven variability in NDVI (FPAR) can lead to much larger fluctuation in NPP vs. the NPP computed from FPAR climatology

Spring Soil Temperature Anomalies over Tibetan Plateau and Summer Droughts/Floods in East Asia

NASA Astrophysics Data System (ADS)

Xue, Y.; Li, W.; LI, Q.; Diallo, I.; Chu, P. C.; Guo, W.; Fu, C.

2017-12-01

Recurrent extreme climate events, such as droughts and floods, are important features of the climate of East Asia, especially over the Yangtze River basin. Many studies have attributed these episodes to variability and anomaly of global sea surface temperatures (SST) anomaly. In addition, snow in the Tibetan Plateau has also been considered as one of the factors affecting the Asian monsoon variability. However, studies have consistently shown that SST along is unable to explain the extreme climate events fully and snow has difficulty to use as a predictor. Remote effects of observed large-scale land surface temperature (LST) and subsurface temperature variability in Tibetan Plateau (TP) on East Asian regional droughts/floods, however, have been largely ignored. We conjecture that a temporally filtered response to snow anomalies may be preserved in the LST anomaly. In this study, evidence from climate observations and model simulations addresses the LST/SUBT effects. The Maximum Covariance Analysis (MCA) of observational data identifies that a pronounce spring LST anomaly pattern over TP is closely associated with precipitation anomalies in East Asia with a dipole pattern, i.e., negative/positive TP spring LST anomaly is associated with the summer drought/flood over the region south of the Yangtze River and wet/dry conditions to the north of the Yangtze River. Climate models were used to demonstrate a causal relationship between spring cold LST anomaly in the TP and the severe 2003 drought over the southern part of the Yangtze River in eastern Asia. This severe drought resulted in 100 x 106 kg crop yield losses and an economic loss of 5.8 billion Chinese Yuan. The modeling study suggests that the LST effect produced about 58% of observed precipitation deficit; while the SST effect produced about 32% of the drought conditions. Meanwhile, the LST and SST effects also simulated the observed flood over to the north of the Yangtze River. This suggests that inclusion of this LST effect is essential to make reliable East Asian drought/flood predictions.

Resilience, rapid transitions and regime shifts: fingerprinting the responses of Lake Żabińskie (NE Poland) to climate variability and human disturbance since 1000 AD

NASA Astrophysics Data System (ADS)

Tylmann, Wojciech; Hernández-Almeida, Iván; Grosjean, Martin; José Gómez Navarro, Juan; Larocque-Tobler, Isabelle; Bonk, Alicja; Enters, Dirk; Ustrzycka, Alicja; Piotrowska, Natalia; Przybylak, Rajmund; Wacnik, Agnieszka; Witak, Małgorzata

2016-04-01

Rapid ecosystem transitions and adverse effects on ecosystem services as responses to combined climate and human impacts are of major concern. Yet few quantitative observational data exist, particularly for ecosystems that have a long history of human intervention. Here, we combine quantitative summer and winter climate reconstructions, climate model simulations and proxies for three major environmental pressures (land use, nutrients and erosion) to explore the system dynamics, resilience, and the role of disturbance regimes in varved eutrophic Lake Żabińskie since AD 1000. Comparison between regional and global climate simulations and quantitative climate reconstructions indicate that proxy data capture noticeably natural forced climate variability, while internal variability appears as the dominant source of climate variability in the climate model simulations during most parts of the last millennium. Using different multivariate analyses and change point detection techniques, we identify ecosystem changes through time and shifts between rather stable states and highly variable ones, as expressed by the proxies for land-use, erosion and productivity in the lake. Prior to AD 1600, the lake ecosystem was characterized by a high stability and resilience against considerable observed natural climate variability. In contrast, lake-ecosystem conditions started to fluctuate at high frequency across a broad range of states after AD 1600. The period AD 1748-1868 represents the phase with the strongest human disturbance of the ecosystem. Analyses of the frequency of change points in the multi-proxy dataset suggests that the last 400 years were highly variable and flickering with increasing vulnerability of the ecosystem to the combined effects of climate variability and anthropogenic disturbances. This led to significant rapid ecosystem transformations.

[Modelling the effect of local climatic variability on dengue transmission in Medellin (Colombia) by means of time series analysis].

PubMed

Rúa-Uribe, Guillermo L; Suárez-Acosta, Carolina; Chauca, José; Ventosilla, Palmira; Almanza, Rita

2013-09-01

Dengue fever is a major impact on public health vector-borne disease, and its transmission is influenced by entomological, sociocultural and economic factors. Additionally, climate variability plays an important role in the transmission dynamics. A large scientific consensus has indicated that the strong association between climatic variables and disease could be used to develop models to explain the incidence of the disease. To develop a model that provides a better understanding of dengue transmission dynamics in Medellin and predicts increases in the incidence of the disease. The incidence of dengue fever was used as dependent variable, and weekly climatic factors (maximum, mean and minimum temperature, relative humidity and precipitation) as independent variables. Expert Modeler was used to develop a model to better explain the behavior of the disease. Climatic variables with significant association to the dependent variable were selected through ARIMA models. The model explains 34% of observed variability. Precipitation was the climatic variable showing statistically significant association with the incidence of dengue fever, but with a 20 weeks delay. In Medellin, the transmission of dengue fever was influenced by climate variability, especially precipitation. The strong association dengue fever/precipitation allowed the construction of a model to help understand dengue transmission dynamics. This information will be useful to develop appropriate and timely strategies for dengue control.

The role of internal variability for decadal carbon uptake anomalies in the Southern Ocean

NASA Astrophysics Data System (ADS)

Spring, Aaron; Hi, Hongmei; Ilyina, Tatiana

2017-04-01

The Southern Ocean is a major sink for anthropogenic CO2 emissions and hence it plays an essential role in modulating global carbon cycle and climate change. Previous studies based on observations (e.g., Landschützer et al. 2015) show pronounced decadal variations of carbon uptake in the Southern Ocean in recent decades and this variability is largely driven by internal climate variability. However, due to limited ensemble size of simulations, the variability of this important ocean sink is still poorly assessed by the state-of-the-art earth system models (ESMs). To assess the internal variability of carbon sink in the Southern Ocean, we use a large ensemble of 100 member simulations based on the Max Planck Institute-ESM (MPI-ESM). The large ensemble of simulations is generated via perturbed initial conditions in the ocean and atmosphere. Each ensemble member includes a historical simulation from 1850 to 2005 with an extension until 2100 under Representative Concentration Pathway (RCP) 4.5 future projections. Here we use model simulations from 1980-2015 to compare with available observation-based dataset. We found several ensemble members showing decadal decreasing trends in the carbon sink, which are similar to the trend shown in observations. This result suggests that MPI-ESM large ensemble simulations are able to reproduce decadal variation of carbon sink in the Southern Ocean. Moreover, the decreasing trends of Southern Ocean carbon sink in MPI-ESM are mainly contributed by region between 50-60°S. To understand the internal variability of the air-sea carbon fluxes in the Southern Ocean, we further investigate the variability of underlying processes, such as physical climate variability and ocean biological processes. Our results indicate two main drivers for the decadal decreasing trend of carbon sink: i) Intensified winds enhance upwelling of old carbon-rich waters, this leads to increase of the ocean surface pCO2; ii) Primary production is reduced in area from 50-60°S, probably induced by reduced euphotic water column stability; therefore the biological drawdown of ocean surface pCO2 is weakened accordingly and hence the ocean is in favor of carbon outgassing. Landschützer, et al. (2015): The reinvigoration of the Southern Ocean carbon sink, Science, 349, 1221-1224.

Evaluation of the global MODIS 30 arc-second spatially and temporally complete snow-free land surface albedo and reflectance anisotropy dataset

NASA Astrophysics Data System (ADS)

Sun, Qingsong; Wang, Zhuosen; Li, Zhan; Erb, Angela; Schaaf, Crystal B.

2017-06-01

Land surface albedo is an essential variable for surface energy and climate modeling as it describes the proportion of incident solar radiant flux that is reflected from the Earth's surface. To capture the temporal variability and spatial heterogeneity of the land surface, satellite remote sensing must be used to monitor albedo accurately at a global scale. However, large data gaps caused by cloud or ephemeral snow have slowed the adoption of satellite albedo products by the climate modeling community. To address the needs of this community, we used a number of temporal and spatial gap-filling strategies to improve the spatial and temporal coverage of the global land surface MODIS BRDF, albedo and NBAR products. A rigorous evaluation of the gap-filled values shows good agreement with original high quality data (RMSE = 0.027 for the NIR band albedo, 0.020 for the red band albedo). This global snow-free and cloud-free MODIS BRDF and albedo dataset (established from 2001 to 2015) offers unique opportunities to monitor and assess the impact of the changes on the Earth's land surface.

Reconstruction of Past Mediterranean Climate

NASA Astrophysics Data System (ADS)

García-Herrera, Ricardo; Luterbacher, Jürg; Lionello, Piero; Gonzáles-Rouco, Fidel; Ribera, Pedro; Rodó, Xavier; Kull, Christoph; Zerefos, Christos

2007-02-01

First MEDCLIVAR Workshop on Reconstruction of Past Mediterranean Climate; Pablo de Olavide University, Carmona, Spain, 8-11 November 2006; Mediterranean Climate Variability and Predictability (MEDCLIVAR; http://www.medclivar.eu) is a program that coordinates and promotes research on different aspects of Mediterranean climate. The main MEDCLIVAR goals include the reconstruction of past climate, describing patterns and mechanisms characterizing climate space-time variability, extremes at different time and space scales, coupled climate model/empirical reconstruction comparisons, seasonal forecasting, and the identification of the forcings responsible for the observed changes. The program has been endorsed by CLIVAR (Climate Variability and Predictability project) and is funded by the European Science Foundation.

Preliminary review of adaptation options for climate-sensitive ecosystems and resources. A report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research

USGS Publications Warehouse

Baron, Jill S.; Griffith, Brad; Joyce, Linda A.; Kareiva, Peter; Keller, Brian D.; Palmer, Margaret A.; Peterson, Charles H.; Scott, J. Michael; Julius, Susan Herrod; West, Jordan M.

2008-01-01

Climate variables are key determinants of geographic distributions and biophysical characteristics of ecosystems, communities, and species. Climate change is therefore affecting many species attributes, ecological interactions, and ecosystem processes. Because changes in the climate system will continue into the future regardless of emissions mitigation, strategies for protecting climate-sensitive ecosystems through management will be increasingly important. While there will always be uncertainties associated with the future path of climate change, the response of ecosystems to climate impacts, and the effects of management, it is both possible and essential for adaptation to proceed using the best available science. This report provides a preliminary review of adaptation options for climate-sensitive ecosystems and resources in the United States. The term “adaptation” in this document refers to adjustments in human social systems (e.g., management) in response to climate stimuli and their effects. Since management always occurs in the context of desired ecosystem conditions or natural resource management goals, it is instructive to examine particular goals and processes used by different organizations to fulfill their objectives. Such an examination allows for discussion of specific adaptation options as well as potential barriers and opportunities for implementation. Using this approach, this report presents a series of chapters on the following selected management systems: National Forests, National Parks, National Wildlife Refuges, Wild and Scenic Rivers, National Estuaries, and Marine Protected Areas. For these chapters, the authors draw on the literature, their own expert opinion, and expert workshops composed of resource management scientists and representatives of managing agencies. The information drawn from across these chapters is then analyzed to develop the key synthetic messages presented below.

The Construction of 3-d Neutral Density for Arbitrary Data Sets

NASA Astrophysics Data System (ADS)

Riha, S.; McDougall, T. J.; Barker, P. M.

2014-12-01

The Neutral Density variable allows inference of water pathways from thermodynamic properties in the global ocean, and is therefore an essential component of global ocean circulation analysis. The widely used algorithm for the computation of Neutral Density yields accurate results for data sets which are close to the observed climatological ocean. Long-term numerical climate simulations, however, often generate a significant drift from present-day climate, which renders the existing algorithm inaccurate. To remedy this problem, new algorithms which operate on arbitrary data have been developed, which may potentially be used to compute Neutral Density during runtime of a numerical model.We review existing approaches for the construction of Neutral Density in arbitrary data sets, detail their algorithmic structure, and present an analysis of the computational cost for implementations on a single-CPU computer. We discuss possible strategies for the implementation in state-of-the-art numerical models, with a focus on distributed computing environments.

Assessing performance and seasonal bias of pollen-based climate reconstructions in a perfect model world

NASA Astrophysics Data System (ADS)

Trachsel, M.; Rehfeld, K.; Telford, R.; Laepple, T.

2017-12-01

Reconstructions of summer, winter or annual mean temperatures based on the species composition of bio-indicators such as pollen are routinely used in climate model-proxy data comparison studies. Most reconstruction algorithms exploit the joint distribution of modern spatial climate and species distribution for the development of the reconstructions. They rely on the space-for-time substitution and the specific assumption that environmental variables other than those reconstructed are not important or that their relationship with the reconstructed variable(s) should be the same in the past as in the modern spatial calibration dataset. Here we test the implications of this "correlative uniformitarianism" assumption on climate reconstructions in an ideal model world, in which climate and vegetation are known at all times. The alternate reality is a climate simulation of the last 6000 years with dynamic vegetation. Transient changes of plant functional types are considered as surrogate pollen counts and allow us to establish, apply and evaluate transfer functions in the modeled world. We find that the transfer function cross validation r2 is of limited use to identify reconstructible climate variables, as it only relies on the modern spatial climate-vegetation relationship. However, ordination approaches that assess the amount of fossil vegetation variance explained by the reconstructions are promising. We show that correlations between climate variables in the modern climate-vegetation relationship are systematically extended into the reconstructions. Summer temperatures, the most prominent driving variable for modeled vegetation change in the Northern Hemisphere, are accurately reconstructed. However, the amplitude of the model winter and mean annual temperature cooling between the mid-Holocene and present day is overestimated and similar to the summer trend in magnitude. This effect occurs because temporal changes of a dominant climate variable are imprinted on a less important variable, leading to reconstructions biased towards the dominant variable's trends. Our results, although based on a model vegetation that is inevitably simpler than reality, indicate that reconstructions of multiple climate variables based on modern spatial bio-indicator datasets should be treated with caution.

Coral Records of 20th Century Central Tropical Pacific SST and Salinity: Signatures of Natural and Anthropogenic Climate Change

NASA Astrophysics Data System (ADS)

Nurhati, I. S.; Cobb, K.; Di Lorenzo, E.

2011-12-01

Accurate forecasts of regional climate changes in many regions of the world largely depend on quantifying anthropogenic trends in tropical Pacific climate against its rich background of interannual to decadal-scale climate variability. However, the strong natural climate variability combined with limited instrumental climate datasets have obscured potential anthropogenic climate signals in the region. Here, we present coral-based sea-surface temperature (SST) and salinity proxy records over the 20th century (1898-1998) from the central tropical Pacific - a region sensitive to El Niño-Southern Oscillation (ENSO) whose variability strongly impacts the global climate. The SST and salinity proxy records are reconstructed via coral Sr/Ca and the oxygen isotopic composition of seawater (δ18Osw), respectively. On interannual (2-7yr) timescales, the SST proxy record tracks both eastern- and central-Pacific flavors of ENSO variability (R=0.65 and R=0.67, respectively). Interannual-scale salinity variability in our coral record highlights profound differences in precipitation and ocean advections during the two flavors of ENSO. On decadal (8yr-lowpassed) timescales, the central tropical Pacific SST and salinity proxy records are controlled by different sets of dynamics linked to the leading climate modes of North Pacific climate variability. Decadal-scale central tropical Pacific SST is highly correlated to the recently discovered North Pacific Gyre Oscillation (NPGO; R=-0.85), reflecting strong dynamical links between the central Pacific warming mode and extratropical decadal climate variability. Whereas decadal-scale salinity variations in the central tropical Pacific are significantly correlated with the Pacific Decadal Oscillation (PDO; R=0.54), providing a better understanding on low-frequency salinity variability in the region. Having characterized natural climate variability in this region, the coral record shows a +0.5°C warming trend throughout the last century. However, the most prominent feature of the new coral records is an unprecedented freshening trend since the mid-20th century, in line with global climate models (GCMs) projections of enhanced hydrological patterns (wet areas are getting wetter and vice versa) under greenhouse forcing. Taken together, the coral records provide key constraints on tropical Pacific climate trends that may improve regional climate projections in areas affected by tropical Pacific climate variability.
Central Tropical Pacific SST and Salinity Proxy Records

Measuring Holocene Indian Summer Monsoon Precipitation through Lake Sedimentary Proxies, Eastern Tibet

NASA Astrophysics Data System (ADS)

Perello, M. M.; Bird, B. W.; Lei, Y.; Polissar, P. J.; Thompson, L. G.; Yao, T.

2017-12-01

The Tibetan Plateau is the headwaters of several major river systems in South Asia, which serve as essential water resources for more than 40% of the world's population. The majority of regional precipitation that sustains these water resources is from the Indian summer monsoon (ISM), which can experience considerably variability in response to local and remote forcings and teleconnections. Despite the ISM's importance, its sensitivity to long term and abrupt changes in climatic boundary conditions is not well established with the modern instrumental record or the available body of paleoclimate data. Here, we present results from an ongoing study that utilizes lake sediment records to provide a longer record of relative levels of precipitation and lake level during the monsoon season. The sediments cores used in this study were collected from five lakes along an east-west transect in the Eastern Tibetan Plateau (87-95°E). Using these records, we assess temporal and spatial variability in the intensity of the ISM throughout the Holocene on decadal frequencies. Multiple proxies, including sedimentology, grain size, geochemistry, terrestrial and aquatic leaf wax isotopes, and diatom community assemblages, are used to assess paleo-precipitation and lake level. Preliminary records from our lakes indicate regional trends in monsoon strength, with higher lake levels in the Early Holocene, but with greater variability in the Late Holocene than in other regional paleoclimate records. We have also observed weak responses in our lakes to the Late Holocene events, the Medieval Climate Anomaly and the Little Ice Age. These paleoclimate reconstructions furthers our understanding of strong versus weak monsoon intensities and can be incorporated in climate models for predicting future monsoon conditions.

Attributing Climate Conditions for Stable Malaria Transmission to Human Activity in sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Sheldrake, L.; Mitchell, D.; Allen, M. R.

2015-12-01

Temperature and precipitation limit areas of stable malaria transmission, but the effects of climate change on the disease remain controversial. Previously, studies have not separated the influence of anthropogenic climate change and natural variability, despite being an essential step in the attribution of climate change impacts. Ensembles of 2900 simulations of regional climate in sub-Saharan Africa for the year 2013, one representing realistic conditions and the other how climate might have been in the absence of human influence, were used to force a P.falciparium climate suitability model developed by the Mapping Malaria Risk in Africa project. Strongest signals were detected in areas of unstable transmission, indicating their heightened sensitivity to climatic factors. Evidently, impacts of human-induced climate change were unevenly distributed: the probability of conditions being suitable for stable malaria transmission were substantially reduced (increased) in the Sahel (Greater Horn of Africa (GHOA), particularly in the Ethiopian and Kenyan highlands). The length of the transmission season was correspondingly shortened in the Sahel and extended in the GHOA, by 1 to 2 months, including in Kericho (Kenya), where the role of climate change in driving recent malaria occurrence is hotly contested. Human-induced warming was primarily responsible for positive anomalies in the GHOA, while reduced rainfall caused negative anomalies in the Sahel. The latter was associated with anthropogenic impacts on the West African Monsoon, but uncertainty in the RCM's ability to reproduce precipitation trends in the region weakens confidence in the result. That said, outputs correspond well with broad-scale changes in observed endemicity, implying a potentially important contribution of anthropogenic climate change to the malaria burden during the past century. Results support the health-framing of climate risk and help indicate hotspots of climate vulnerability, providing information to direct control interventions and investment, and allude to climate injustices. Extending methods, such as by using multiple climate and malaria models and investigating trends over longer timescales, would make results more generally applicable and improve their policy relevance.

Analyzing the responses of species assemblages to climate change across the Great Basin, USA.

NASA Astrophysics Data System (ADS)

Henareh Khalyani, A.; Falkowski, M. J.; Crookston, N.; Yousef, F.

2016-12-01

The potential impacts of climate change on the future distribution of tree species in not well understood. Climate driven changes in tree species distribution could cause significant changes in realized species niches, potentially resulting in the loss of ecotonal species as well as the formation on novel assemblages of overlapping tree species. In an effort to gain a better understating of how the geographic distribution of tree species may respond to climate change, we model the potential future distribution of 50 different tree species across 70 million ha in the Great Basin, USA. This is achieved by leveraging a species realized niche model based on non-parametric analysis of species occurrences across climatic, topographic, and edaphic variables. Spatially explicit, high spatial resolution (30 m) climate variables (e.g., precipitation, and minimum, maximum, and mean temperature) and associated climate indices were generated on an annual basis between 1981-2010 by integrating climate station data with digital elevation data (Shuttle Radar Topographic Mission (SRTM) data) in a thin plate spline interpolation algorithm (ANUSPLIN). Bioclimate models of species niches in in the cotemporary period and three following 30 year periods were then generated by integrating the climate variables, soil data, and CMIP 5 general circulation model projections. Our results suggest that local scale contemporary variations in species realized niches across space are influenced by edaphic and topographic variables as well as climatic variables. The local variability in soil properties and topographic variability across space also affect the species responses to climate change through time and potential formation of species assemblages in future. The results presented here in will aid in the development of adaptive forest management techniques aimed at mitigating negative impacts of climate change on forest composition, structure, and function.

Climate Drivers of Alaska Summer Stream Temperature

NASA Astrophysics Data System (ADS)

Bieniek, P.; Bhatt, U. S.; Plumb, E. W.; Thoman, R.; Trammell, E. J.

2016-12-01

The temperature of the water in lakes, rivers and streams has wide ranging impacts from local water quality and fish habitats to global climate change. Salmon fisheries in Alaska, a critical source of food in many subsistence communities, are sensitive to large-scale climate variability and river and stream temperatures have also been linked with salmon production in Alaska. Given current and projected climate change, understanding the mechanisms that link the large-scale climate and river and stream temperatures is essential to better understand the changes that may occur with aquatic life in Alaska's waterways on which subsistence users depend. An analysis of Alaska stream temperatures in the context of reanalysis, downscaled, station and other climate data is undertaken in this study to fill that need. Preliminary analysis identified eight stream observation sites with sufficiently long (>15 years) data available for climate-scale analysis in Alaska with one station, Terror Creek in Kodiak, having a 30-year record. Cross-correlation of summer (June-August) water temperatures between the stations are generally high even though they are spread over a large geographic region. Correlation analysis of the Terror Creek summer observations with seasonal sea surface temperatures (SSTs) in the North Pacific broadly resembles the SST anomaly fields typically associated with the Pacific Decadal Oscillation (PDO). A similar result was found for the remaining stations and in both cases PDO-like correlation patterns also occurred in the preceding spring. These preliminary results demonstrate that there is potential to diagnose the mechanisms that link the large-scale climate system and Alaska stream temperatures.

Adaptations to Climate-Mediated Selective Pressures in Sheep

PubMed Central

Lv, Feng-Hua; Agha, Saif; Kantanen, Juha; Colli, Licia; Stucki, Sylvie; Kijas, James W.; Joost, Stéphane; Li, Meng-Hua; Ajmone Marsan, Paolo

2014-01-01

Following domestication, sheep (Ovis aries) have become essential farmed animals across the world through adaptation to a diverse range of environments and varied production systems. Climate-mediated selective pressure has shaped phenotypic variation and has left genetic “footprints” in the genome of breeds raised in different agroecological zones. Unlike numerous studies that have searched for evidence of selection using only population genetics data, here, we conducted an integrated coanalysis of environmental data with single nucleotide polymorphism (SNP) variation. By examining 49,034 SNPs from 32 old, autochthonous sheep breeds that are adapted to a spectrum of different regional climates, we identified 230 SNPs with evidence for selection that is likely due to climate-mediated pressure. Among them, 189 (82%) showed significant correlation (P ≤ 0.05) between allele frequency and climatic variables in a larger set of native populations from a worldwide range of geographic areas and climates. Gene ontology analysis of genes colocated with significant SNPs identified 17 candidates related to GTPase regulator and peptide receptor activities in the biological processes of energy metabolism and endocrine and autoimmune regulation. We also observed high linkage disequilibrium and significant extended haplotype homozygosity for the core haplotype TBC1D12-CH1 of TBC1D12. The global frequency distribution of the core haplotype and allele OAR22_18929579-A showed an apparent geographic pattern and significant (P ≤ 0.05) correlations with climatic variation. Our results imply that adaptations to local climates have shaped the spatial distribution of some variants that are candidates to underpin adaptive variation in sheep. PMID:25249477

Oceanographic mechanisms and penguin population increases during the Little Ice Age in the southern Ross Sea, Antarctica

NASA Astrophysics Data System (ADS)

Yang, Lianjiao; Sun, Liguang; Emslie, Steven D.; Xie, Zhouqing; Huang, Tao; Gao, Yuesong; Yang, Wenqing; Chu, Zhuding; Wang, Yuhong

2018-01-01

The Adélie penguin is a well-known indicator for climate and environmental changes. Exploring how large-scale climate variability affects penguin ecology in the past is essential for understanding the responses of Southern Ocean ecosystems to future global change. Using ornithogenic sediments at Cape Bird, Ross Island, Antarctica, we inferred relative population changes of Adélie penguins in the southern Ross Sea over the past 500 yr, and observed an increase in penguin populations during the Little Ice Age (LIA; 1500-1850 AD). We used cadmium content in ancient penguin guano as a proxy of ocean upwelling and identified a close linkage between penguin dynamics and atmospheric circulation and oceanic conditions. During the cold period of ∼1600-1825 AD, a deepened Amundsen Sea Low (ASL) led to stronger winds, intensified ocean upwelling, enlarged Ross Sea and McMurdo Sound polynyas, and thus higher food abundance and penguin populations. We propose a mechanism linking Antarctic marine ecology and atmospheric/oceanic dynamics which can help explain and predict responses of Antarctic high latitudes ecosystems to climate change.

Climate variability has a stabilizing effect on the coexistence of prairie grasses

PubMed Central

Adler, Peter B.; HilleRisLambers, Janneke; Kyriakidis, Phaedon C.; Guan, Qingfeng; Levine, Jonathan M.

2006-01-01

How expected increases in climate variability will affect species diversity depends on the role of such variability in regulating the coexistence of competing species. Despite theory linking temporal environmental fluctuations with the maintenance of diversity, the importance of climate variability for stabilizing coexistence remains unknown because of a lack of appropriate long-term observations. Here, we analyze three decades of demographic data from a Kansas prairie to demonstrate that interannual climate variability promotes the coexistence of three common grass species. Specifically, we show that (i) the dynamics of the three species satisfy all requirements of “storage effect” theory based on recruitment variability with overlapping generations, (ii) climate variables are correlated with interannual variation in species performance, and (iii) temporal variability increases low-density growth rates, buffering these species against competitive exclusion. Given that environmental fluctuations are ubiquitous in natural systems, our results suggest that coexistence based on the storage effect may be underappreciated and could provide an important alternative to recent neutral theories of diversity. Field evidence for positive effects of variability on coexistence also emphasizes the need to consider changes in both climate means and variances when forecasting the effects of global change on species diversity. PMID:16908862

Spatial variability in forest growth—climate relationships in the Olympic Mountains, Washington.

Treesearch

Jill M. Nakawatase; David L. Peterson

2006-01-01

For many Pacific Northwest forests, little is known about the spatial and temporal variability in tree growth - climate relationships, yet it is this information that is needed to predict how forests will respond to future climatic change. We studied the effects of climatic variability on forest growth at 74 plots in the western and northeastern Olympic Mountains....

Frontiers in Decadal Climate Variability: Proceedings of a Workshop

DOE Office of Scientific and Technical Information (OSTI.GOV)

Purcell, Amanda

A number of studies indicate an apparent slowdown in the overall rise in global average surface temperature between roughly 1998 and 2014. Most models did not predict such a slowdown--a fact that stimulated a lot of new research on variability of Earth's climate system. At a September 2015 workshop, leading scientists gathered to discuss current understanding of climate variability on decadal timescales (10 to 30 years) and whether and how prediction of it might be improved. Many researchers have focused their attention on the climate system itself, which is known to vary across seasons, decades, and other timescales. Several naturalmore » variables produce "ups and downs" in the climate system, which are superimposed on the long-term warming trend due to human influence. Understanding decadal climate variability is important not only for assessing global climate change but also for improving decision making related to infrastructure, water resources, agriculture, energy, and other realms. Like the well-studied El Nino and La Nina interannual variations, decadal climate variability is associated with specific regional patterns of temperature and precipitation, such as heat waves, cold spells, and droughts. Several participants shared research that assesses decadal predictive capability of current models.« less

Joint effects of climate variability and socioecological factors on dengue transmission: epidemiological evidence.

PubMed

Akter, Rokeya; Hu, Wenbiao; Naish, Suchithra; Banu, Shahera; Tong, Shilu

2017-06-01

To assess the epidemiological evidence on the joint effects of climate variability and socioecological factors on dengue transmission. Following PRISMA guidelines, a detailed literature search was conducted in PubMed, Web of Science and Scopus. Peer-reviewed, freely available and full-text articles, considering both climate and socioecological factors in relation to dengue, published in English from January 1993 to October 2015 were included in this review. Twenty studies have met the inclusion criteria and assessed the impact of both climatic and socioecological factors on dengue dynamics. Among those, four studies have further investigated the relative importance of climate variability and socioecological factors on dengue transmission. A few studies also developed predictive models including both climatic and socioecological factors. Due to insufficient data, methodological issues and contextual variability of the studies, it is hard to draw conclusion on the joint effects of climate variability and socioecological factors on dengue transmission. Future research should take into account socioecological factors in combination with climate variables for a better understanding of the complex nature of dengue transmission as well as for improving the predictive capability of dengue forecasting models, to develop effective and reliable early warning systems. © 2017 John Wiley & Sons Ltd.

Decadal-scale Climate Variability on the Central Iranian Plateau Spanning the So-called 4.2 ka BP Drought Event

NASA Astrophysics Data System (ADS)

Carolin, S.; Walker, R. T.; Henderson, G. M.; Maxfield, L.; Ersek, V.; Sloan, A.; Talebian, M.; Fattahi, M.; Nezamdoust, J.

2015-12-01

The influence of climate on the growth and development of ancient civilizations throughout the Holocene remains a topic of heated debate. The 4.2 ka BP global-scale mid-to-low latitude aridification event (Walker et al., 2012) in particular has incited various correlation proposals. Some authors suggest that this event may have led to the collapse of the Akkadian empire in Mesopotamia, one of the first empires in human history, as well as to changes among other Early Bronze Age societies dependent on cereal agriculture (eg. Staubwasser and Weiss, 2006). Other authors remain doubtful of the impact of environmental factors on the collapse of past societies (eg. Middleton, 2012). While coincident timing of an environmental event with archeological evidence does not necessitate a causation, a comprehensive understanding of climate variability in the ancient Near East is nonetheless an essential component to resolving the full history of early human settlements. Paleoclimate data on the Central Iranian Plateau, a region rich with ancient history, is exceptionally sparse compared to other areas. Many karst locations are found throughout the region, however, setting the stage for the development of several high-resolution, accurate and precisely-dated climate proxy records if a correlation between the chemistry of semi-arid speleothem samples and climate is resolved. Here we present a 5.1-3.7 ka BP record of decadal-scale stalagmite stable isotope and trace metal variability. The stalagmite was collected in Gol-e zard cave (35.8oN, 52.0oE), ~100 km NE of Tehran on the southern flank of the Alborz mountain range (2530masl). The area currently receives ~270mm mean annual precipitation, with more than 90% of precipitation falling within the wet season (November-May). We use GNIP data from Tehran and local and regional meteorological data to resolve the large-scale mechanisms forcing isotopic variations in rainwater over Gol-e zard cave. We discuss possible transformation of water isotopes during transition through the karst aquifer based on site properties and simple model experiments. Finally, we discuss the timing and magnitude of significant events in the stable isotope and trace metal records, particularly in relation to the 4.2 ka BP drought event apparent in certain other regional climate records.

Impacts of Considering Climate Variability on Investment Decisions in Ethiopia

NASA Astrophysics Data System (ADS)

Strzepek, K.; Block, P.; Rosegrant, M.; Diao, X.

2005-12-01

In Ethiopia, climate extremes, inducing droughts or floods, are not unusual. Monitoring the effects of these extremes, and climate variability in general, is critical for economic prediction and assessment of the country's future welfare. The focus of this study involves adding climate variability to a deterministic, mean climate-driven agro-economic model, in an attempt to understand its effects and degree of influence on general economic prediction indicators for Ethiopia. Four simulations are examined, including a baseline simulation and three investment strategies: simulations of irrigation investment, roads investment, and a combination investment of both irrigation and roads. The deterministic model is transformed into a stochastic model by dynamically adding year-to-year climate variability through climate-yield factors. Nine sets of actual, historic, variable climate data are individually assembled and implemented into the 12-year stochastic model simulation, producing an ensemble of economic prediction indicators. This ensemble allows for a probabilistic approach to planning and policy making, allowing decision makers to consider risk. The economic indicators from the deterministic and stochastic approaches, including rates of return to investments, are significantly different. The predictions of the deterministic model appreciably overestimate the future welfare of Ethiopia; the predictions of the stochastic model, utilizing actual climate data, tend to give a better semblance of what may be expected. Inclusion of climate variability is vital for proper analysis of the predictor values from this agro-economic model.

Post-Fire Recovery of Eco-Hydrologic Behavior Given Historic and Projected Climate Variability in California Mediterranean Type Environments

NASA Astrophysics Data System (ADS)

Seaby, L. P.; Tague, C. L.; Hope, A. S.

2006-12-01

The Mediterranean type environments (MTEs) of California are characterized by a distinct wet and dry season and high variability in inter-annual climate. Water limitation in MTEs makes eco-hydrological processes highly sensitive to both climate variability and frequent fire disturbance. This research modeled post-fire eco- hydrologic behavior under historical and moderate and extreme scenarios of future climate in a semi-arid chaparral dominated southern California MTE. We used a physically-based, spatially-distributed, eco- hydrological model (RHESSys - Regional Hydro-Ecologic Simulation System), to capture linkages between water and vegetation response to the combined effects of fire and historic and future climate variability. We found post-fire eco-hydrologic behavior to be strongly influenced by the episodic nature of MTE climate, which intensifies under projected climate change. Higher rates of post-fire net primary productivity were found under moderate climate change, while more extreme climate change produced water stressed conditions which were less favorable for vegetation productivity. Precipitation variability in the historic record follows the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), and these inter-annual climate characteristics intensify under climate change. Inter-annual variation in streamflow follows these precipitation patterns. Post-fire streamflow and carbon cycling trajectories are strongly dependent on climate characteristics during the first 5 years following fire, and historic intra-climate variability during this period tends to overwhelm longer term trends and variation that might be attributable to climate change. Results have implications for water resource availability, vegetation type conversion from shrubs to grassland, and changes in ecosystem structure and function.

Slowing down of North Pacific climate variability and its implications for abrupt ecosystem change.

PubMed

Boulton, Chris A; Lenton, Timothy M

2015-09-15

Marine ecosystems are sensitive to stochastic environmental variability, with higher-amplitude, lower-frequency--i.e., "redder"--variability posing a greater threat of triggering large ecosystem changes. Here we show that fluctuations in the Pacific Decadal Oscillation (PDO) index have slowed down markedly over the observational record (1900-present), as indicated by a robust increase in autocorrelation. This "reddening" of the spectrum of climate variability is also found in regionally averaged North Pacific sea surface temperatures (SSTs), and can be at least partly explained by observed deepening of the ocean mixed layer. The progressive reddening of North Pacific climate variability has important implications for marine ecosystems. Ecosystem variables that respond linearly to climate forcing will have become prone to much larger variations over the observational record, whereas ecosystem variables that respond nonlinearly to climate forcing will have become prone to more frequent "regime shifts." Thus, slowing down of North Pacific climate variability can help explain the large magnitude and potentially the quick succession of well-known abrupt changes in North Pacific ecosystems in 1977 and 1989. When looking ahead, despite model limitations in simulating mixed layer depth (MLD) in the North Pacific, global warming is robustly expected to decrease MLD. This could potentially reverse the observed trend of slowing down of North Pacific climate variability and its effects on marine ecosystems.

Influence of climate variability on acute myocardial infarction mortality in Havana, 2001-2012.

PubMed

Rivero, Alina; Bolufé, Javier; Ortiz, Paulo L; Rodríguez, Yunisleydi; Reyes, María C

2015-04-01

Death from acute myocardial infarction is due to many factors; influences on risk to the individual include habits, lifestyle and behavior, as well as weather, climate and other environmental components. Changing climate patterns make it especially important to understand how climatic variability may influence acute myocardial infarction mortality. Describe the relationship between climate variability and acute myocardial infarction mortality during the period 2001-2012 in Havana. An ecological time-series study was conducted. The universe comprised 23,744 deaths from acute myocardial infarction (ICD-10: I21-I22) in Havana residents from 2001 to 2012. Climate variability and seasonal anomalies were described using the Bultó-1 bioclimatic index (comprising variables of temperature, humidity, precipitation, and atmospheric pressure), along with series analysis to determine different seasonal-to-interannual climate variation signals. The role played by climate variables in acute myocardial infarction mortality was determined using factor analysis. The Mann-Kendall and Pettitt statistical tests were used for trend analysis with a significance level of 5%. The strong association between climate variability conditions described using the Bultó-1 bioclimatic index and acute myocardial infarctions accounts for the marked seasonal pattern in AMI mortality. The highest mortality rate occurred during the dry season, i.e., the winter months in Cuba (November-April), with peak numbers in January, December and March. The lowest mortality coincided with the rainy season, i.e., the summer months (May-October). A downward trend in total number of deaths can be seen starting with the change point in April 2009. Climate variability is inversely associated with an increase in acute myocardial infarction mortality as is shown by the Bultó-1 index. This inverse relationship accounts for acute myocardial infarction mortality's seasonal pattern.

Central Tropical Pacific Variability And ENSO Response To Changing Climate Boundary Conditions: Evidence From Individual Line Island Foraminifera

NASA Astrophysics Data System (ADS)

Rustic, G. T.; Polissar, P. J.; Ravelo, A. C.; White, S. M.

2017-12-01

The El Niño Southern Oscillation (ENSO) plays a dominant role in Earth's climate variability. Paleoceanographic evidence suggests that ENSO has changed in the past, and these changes have been linked to large-scale climatic shifts. While a close relationship between ENSO evolution and climate boundary conditions has been predicted, testing these predictions remains challenging. These climate boundary conditions, including insolation, the mean surface temperature gradient of the tropical Pacific, global ice volume, and tropical thermocline depth, often co-vary and may work together to suppress or enhance the ocean-atmosphere feedbacks that drive ENSO variability. Furthermore, suitable paleo-archives spanning multiple climate states are sparse. We have aimed to test ENSO response to changing climate boundary conditions by generating new reconstructions of mixed-layer variability from sedimentary archives spanning the last three glacial-interglacial cycles from the Central Tropical Pacific Line Islands, where El Niño is strongly expressed. We analyzed Mg/Ca ratios from individual foraminifera to reconstruct mixed-layer variability at discrete time intervals representing combinations of climatic boundary conditions from the middle Holocene to Marine Isotope Stage (MIS) 8. We observe changes in the mixed-layer temperature variability during MIS 5 and during the previous interglacial (MIS 7) showing significant reductions in ENSO amplitude. Differences in variability during glacial and interglacial intervals are also observed. Additionally, we reconstructed mixed-layer and thermocline conditions using multi-species Mg/Ca and stable isotope measurements to more fully characterize the state of the Central Tropical Pacific during these intervals. These reconstructions provide us with a unique view of Central Tropical Pacific variability and water-column structure at discrete intervals under varying boundary climate conditions with which to assess factors that shape ENSO variability.

Oblique radiation lateral open boundary conditions for a regional climate atmospheric model

NASA Astrophysics Data System (ADS)

Cabos Narvaez, William; De Frutos Redondo, Jose Antonio; Perez Sanz, Juan Ignacio; Sein, Dmitry

2013-04-01

The prescription of lateral boundary conditions in regional atmospheric models represent a very important issue for limited area models. The ill-posed nature of the open boundary conditions makes it necessary to devise schemes in order to filter spurious wave reflections at boundaries, being desirable to have one boundary condition per variable. On the other side, due to the essentially hyperbolic nature of the equations solved in state of the art atmospheric models, external data is required only for inward boundary fluxes. These circumstances make radiation lateral boundary conditions a good choice for the filtering of spurious wave reflections. Here we apply the adaptive oblique radiation modification proposed by Mikoyada and Roseti to each of the prognostic variables of the REMO regional atmospheric model and compare it to the more common normal radiation condition used in REMO. In the proposed scheme, special attention is paid to the estimation of the radiation phase speed, essential to detecting the direction of boundary fluxes. One of the differences with the classical scheme is that in case of outward propagation, the adaptive nudging imposed in the boundaries allows to minimize under and over specifications problems, adequately incorporating the external information.

Biotic and Climatic Velocity Identify Contrasting Areas of Vulnerability to Climate Change.

PubMed

Carroll, Carlos; Lawler, Joshua J; Roberts, David R; Hamann, Andreas

2015-01-01

Metrics that synthesize the complex effects of climate change are essential tools for mapping future threats to biodiversity and predicting which species are likely to adapt in place to new climatic conditions, disperse and establish in areas with newly suitable climate, or face the prospect of extirpation. The most commonly used of such metrics is the velocity of climate change, which estimates the speed at which species must migrate over the earth's surface to maintain constant climatic conditions. However, "analog-based" velocities, which represent the actual distance to where analogous climates will be found in the future, may provide contrasting results to the more common form of velocity based on local climate gradients. Additionally, whereas climatic velocity reflects the exposure of organisms to climate change, resultant biotic effects are dependent on the sensitivity of individual species as reflected in part by their climatic niche width. This has motivated development of biotic velocity, a metric which uses data on projected species range shifts to estimate the velocity at which species must move to track their climatic niche. We calculated climatic and biotic velocity for the Western Hemisphere for 1961-2100, and applied the results to example ecological and conservation planning questions, to demonstrate the potential of such analog-based metrics to provide information on broad-scale patterns of exposure and sensitivity. Geographic patterns of biotic velocity for 2954 species of birds, mammals, and amphibians differed from climatic velocity in north temperate and boreal regions. However, both biotic and climatic velocities were greatest at low latitudes, implying that threats to equatorial species arise from both the future magnitude of climatic velocities and the narrow climatic tolerances of species in these regions, which currently experience low seasonal and interannual climatic variability. Biotic and climatic velocity, by approximating lower and upper bounds on migration rates, can inform conservation of species and locally-adapted populations, respectively, and in combination with backward velocity, a function of distance to a source of colonizers adapted to a site's future climate, can facilitate conservation of diversity at multiple scales in the face of climate change.

Biotic and Climatic Velocity Identify Contrasting Areas of Vulnerability to Climate Change

PubMed Central

Carroll, Carlos; Lawler, Joshua J.; Roberts, David R.; Hamann, Andreas

2015-01-01

Metrics that synthesize the complex effects of climate change are essential tools for mapping future threats to biodiversity and predicting which species are likely to adapt in place to new climatic conditions, disperse and establish in areas with newly suitable climate, or face the prospect of extirpation. The most commonly used of such metrics is the velocity of climate change, which estimates the speed at which species must migrate over the earth’s surface to maintain constant climatic conditions. However, “analog-based” velocities, which represent the actual distance to where analogous climates will be found in the future, may provide contrasting results to the more common form of velocity based on local climate gradients. Additionally, whereas climatic velocity reflects the exposure of organisms to climate change, resultant biotic effects are dependent on the sensitivity of individual species as reflected in part by their climatic niche width. This has motivated development of biotic velocity, a metric which uses data on projected species range shifts to estimate the velocity at which species must move to track their climatic niche. We calculated climatic and biotic velocity for the Western Hemisphere for 1961–2100, and applied the results to example ecological and conservation planning questions, to demonstrate the potential of such analog-based metrics to provide information on broad-scale patterns of exposure and sensitivity. Geographic patterns of biotic velocity for 2954 species of birds, mammals, and amphibians differed from climatic velocity in north temperate and boreal regions. However, both biotic and climatic velocities were greatest at low latitudes, implying that threats to equatorial species arise from both the future magnitude of climatic velocities and the narrow climatic tolerances of species in these regions, which currently experience low seasonal and interannual climatic variability. Biotic and climatic velocity, by approximating lower and upper bounds on migration rates, can inform conservation of species and locally-adapted populations, respectively, and in combination with backward velocity, a function of distance to a source of colonizers adapted to a site’s future climate, can facilitate conservation of diversity at multiple scales in the face of climate change. PMID:26466364

Climatic extremes improve predictions of spatial patterns of tree species

USGS Publications Warehouse

Zimmermann, N.E.; Yoccoz, N.G.; Edwards, T.C.; Meier, E.S.; Thuiller, W.; Guisan, Antoine; Schmatz, D.R.; Pearman, P.B.

2009-01-01

Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D2, +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

Watling, James I.; Romañach, Stephanie S.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Pearlstine, Leonard G.; Mazzotti, Frank J.

2012-01-01

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

Intraseasonal Oscillations over South America: A Study with a Regional Climate Model

NASA Technical Reports Server (NTRS)

Chen, Baode; Chao, Winston

2003-01-01

The National Center for Atmospheric Research (NCAR) regional climate model version 2 (RegCM2) is used to investigate the observed characteristics of intraseasonal oscillations over South America. Our study is mainly concentrated on an intraseaonal mode, which is observed to account for a large portion of the intraseasonal variation, to have a standing feature and to be independent of the MJO. The NCEPDOE AMIP-II reanalysis is utilized to provide initial and lateral boundary conditions for the RegCM2 based upon the OOZ, 062, 122 and 182 data.Our results indicate that the intraseasonal oscillation still exists with time- averaged lateral boundary condition, which prevents the MJO and other outside disturbances from entering the model's domain, suggesting a locally forced oscillation responsible for ths intraseasonal mode independent of the MJO. Further experiments show that the annual and daily variabilities and a radiative-convective interaction are not essential to the locally forced intraseasonal oscillation. The intraseasonal oscillations over Amazon in our model essentially result from interactions among atmospheric continental- scale circulation, surface radiation, surface sensible and latent heat fluxes, and cumulus convection. The wavelet analyses of various surface energy fluxes and surface energy budget also verify that the primary cause of intraseasonal oscillation is the interaction of land surface processes with the atmosphere.

Climate | National Oceanic and Atmospheric Administration

Science.gov Websites

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Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

PubMed

Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

2015-04-01

Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for epidemiological surveillance.

Recent changes in county-level corn yield variability in the United States from observations and crop models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leng, Guoyong

The United States is responsible for 35% and 60% of global corn supply and exports. Enhanced supply stability through a reduction in the year-to-year variability of US corn yield would greatly benefit global food security. Important in this regard is to understand how corn yield variability has evolved geographically in the history and how it relates to climatic and non-climatic factors. Results showed that year-to-year variation of US corn yield has decreased significantly during 1980-2010, mainly in Midwest Corn Belt, Nebraska and western arid regions. Despite the country-scale decreasing variability, corn yield variability exhibited an increasing trend in South Dakota,more » Texas and Southeast growing regions, indicating the importance of considering spatial scales in estimating yield variability. The observed pattern is partly reproduced by process-based crop models, simulating larger areas experiencing increasing variability and underestimating the magnitude of decreasing variability. And 3 out of 11 models even produced a differing sign of change from observations. Hence, statistical model which produces closer agreement with observations is used to explore the contribution of climatic and non-climatic factors to the changes in yield variability. It is found that climate variability dominate the change trends of corn yield variability in the Midwest Corn Belt, while the ability of climate variability in controlling yield variability is low in southeastern and western arid regions. Irrigation has largely reduced the corn yield variability in regions (e.g. Nebraska) where separate estimates of irrigated and rain-fed corn yield exist, demonstrating the importance of non-climatic factors in governing the changes in corn yield variability. The results highlight the distinct spatial patterns of corn yield variability change as well as its influencing factors at the county scale. I also caution the use of process-based crop models, which have substantially underestimated the change trend of corn yield variability, in projecting its future changes.« less

Violence and Disorder, School Climate, and PBIS: The Relationship among School Climate, Student Outcomes, and the Use of Positive Behavioral Interventions and Supports

ERIC Educational Resources Information Center

Eacho, Thomas Christopher

2013-01-01

The primary purpose of this study was to examine the relationship between school climate and student outcome variables. The secondary purpose was to examine the relationship between the use of Positive Behavioral Interventions and Supports (PBIS) and the same student outcome variables. Variables depicting student perceptions of school climate,…

Ecosystem resilience to abrupt late Quaternary change in continental southern Siberia

NASA Astrophysics Data System (ADS)

Harding, Poppy; Mackay, Anson; Bezrukova, Elena; Shchetnikov, Alexander

2017-04-01

Quaternary climate variability is dominated by long term orbital forcing along with abrupt sub-Milankovitch events on the scales of millennia to centuries, driven by internal feedback mechanisms, volcanic forcing and fluctuating solar activity. Although these are well documented in the North Atlantic region, their expression is poorly understood in Siberia, particularly in relation to abrupt climatic events. Siberia has the world's highest level of continentality offering an opportunity to study changes remote from oceanic influences and improving understanding of interactions between the Siberian High and other atmospheric systems including the Aleutian Low, Arctic oscillation and Icelandic Low1 and ENSO2. Understanding of palaeoenvironmental change in Siberia is essential due to the region's high sensitivity to climatic change, with warming rates considerably higher than the global average over the past 50 years3, triggering significant environmental changes, including permafrost degradation, shifts in the forest-steppe biome, increases in forest fires and warming of seasonally ice-covered lakes. Additionally, the region provides essential palaeoenvironmental context for early hominins, for example at globally important sites such as Denisova cave4, and megafauna extinctions5. This presentation outlines ongoing work at Lake Baunt, SE Siberia including: key quaternary climate forcings, the site and its regional context, the key methods and preliminary results. These include a dated record back to ˜30ka BP (based on multiple 14C dates and Bayesian age modelling), multiproxy indicators of palaeoproductivity (e.g. biogenic silica and diatom analyses) and lake mixing regimes (inferred from diatom analyses). Together these highlight several key Quaternary fluctuations potentially correlated to events recorded in Greenland Ice Cores (GS2, GS2.1, GI1, GS1), and these are considered against key Quaternary records including those from nearby Lake Baikal and Hulu Cave in east China. Our analyses suggest that teleconnections between the Siberian High and the East Asian monsoon are also significant for this study, with Lake Baunt showing a relationship between productivity and variability in strength of the Siberian High. References: 1. Tubi, A. & Dayan, U. (2013). Int. J. Climatol. 33, 1357-1366. 2. Park, T.-W. et al. (2014). Clim. Dyn. 45, 1207-1217. 3. Tingley, M. P. & Huybers, P. (2013). Nature 496, 201-5. 4. Krause, J. et al.. (2010). Nature 464, 894-7. 5. Stuart, A. J. et al. (2004). Nature 431, 684-9.

Towards Generating Long-term AMSR-based Soil Moisture Data Record

NASA Astrophysics Data System (ADS)

Mladenova, I. E.; Jackson, T. J.; Bindlish, R.; Cosh, M. H.

2014-12-01

Research done over the past couple of years, such as Jung et al. (Nature, 2010) among others, demonstrates the potential for using soil moisture as an indicator and parameter for identifying long-term changes in climate trends. The study mentioned links the reduction in global evapotranspiration observed after the 1998 El Nino to decline in moisture supplies in the soil profile. Due to its crucial role in the terrestrial cycles and the demonstrated strong feedback with other climate variables, soil moisture has been recognized by the Global Climate Observing System as one of the 50 Essential Climate Variables (ECVs). The most cost and time effective way of monitoring soil moisture at global scale on routine basis, which is one of the requirements for ECVs, is using satellite technologies. AMSR-E was the first satellite mission to include soil moisture as an operational product. AMSR-E provided us with almost a decade of soil moisture data that are now extended by AMSR2, allowing the generation of a consistent and continuous global soil moisture data record. AMSR-E and AMSR2 are technically alike, thus, they are expected to have similar performance and accuracy, which needs to be confirmed and this the main focus of our research. AMSR-E stopped operating at its optimal rotational speed about 6 months before the launch of AMSR2, which complicates the direct inter-comparison and assessment of AMSR2 performance relative to AMSR-E. The AMSR-E and AMSR2 brightness temperature data and the corresponding soil moisture retrievals derived using the Single Channel Approach were evaluated separately at several ground validation sides located in the US. Brightness temperature inter-comparisons were done using monthly climatology and the low spin AMSR-E data acquired at 2 rpm. Both analyses showed very high agreement between the two instruments and revealed a constant positive bias at all locations in the AMSR2 observations relative to AMSR-E. Removal of this bias is essential in order to ensure consistency between both instruments. The corresponding soil moisture retrievals from AMSR-E and AMSR2 demonstrated reasonable agreement relative to in situ data. A detailed discussion that focuses on this analysis as well as possible approaches for removing the observed bias in the brightness temperature observations will be presented.

Downscaling essential climate variable soil moisture using multisource data from 2003 to 2010 in China

NASA Astrophysics Data System (ADS)

Wang, Hui-Lin; An, Ru; You, Jia-jun; Wang, Ying; Chen, Yuehong; Shen, Xiao-ji; Gao, Wei; Wang, Yi-nan; Zhang, Yu; Wang, Zhe; Quaye-Ballard, Jonathan Arthur

2017-10-01

Soil moisture plays an important role in the water cycle within the surface ecosystem, and it is the basic condition for the growth of plants. Currently, the spatial resolutions of most soil moisture data from remote sensing range from ten to several tens of km, while those observed in-situ and simulated for watershed hydrology, ecology, agriculture, weather, and drought research are generally <1 km. Therefore, the existing coarse-resolution remotely sensed soil moisture data need to be downscaled. This paper proposes a universal and multitemporal soil moisture downscaling method suitable for large areas. The datasets comprise land surface, brightness temperature, precipitation, and soil and topographic parameters from high-resolution data and active/passive microwave remotely sensed essential climate variable soil moisture (ECV_SM) data with a spatial resolution of 25 km. Using this method, a total of 288 soil moisture maps of 1-km resolution from the first 10-day period of January 2003 to the last 10-day period of December 2010 were derived. The in-situ observations were used to validate the downscaled ECV_SM. In general, the downscaled soil moisture values for different land cover and land use types are consistent with the in-situ observations. Mean square root error is reduced from 0.070 to 0.061 using 1970 in-situ time series observation data from 28 sites distributed over different land uses and land cover types. The performance was also assessed using the GDOWN metric, a measure of the overall performance of the downscaling methods based on the same dataset. It was positive in 71.429% of cases, indicating that the suggested method in the paper generally improves the representation of soil moisture at 1-km resolution.

The Climate Variability & Predictability (CVP) Program at NOAA - Recent Program Advancements in Understanding AMOC

NASA Astrophysics Data System (ADS)

Lucas, S. E.

2016-12-01

The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International and U.S. Climate Variability and Predictability (CLIVAR/US CLIVAR) Program, and the U.S. Global Change Research Program (USGCRP). The CVP program sits within NOAA's Climate Program Office (http://cpo.noaa.gov/CVP). This poster will present the recently funded CVP projects on improving the understanding Atlantic Meridional Overturning Circulation (AMOC), its impact on decadal predictability, and its relationship with the overall climate system.

Effects of climatic factors and ecosystem responses on the inter-annual variability of evapotranspiration in a coniferous plantation in subtropical China.

PubMed

Xu, Mingjie; Wen, Xuefa; Wang, Huimin; Zhang, Wenjiang; Dai, Xiaoqin; Song, Jie; Wang, Yidong; Fu, Xiaoli; Liu, Yunfen; Sun, Xiaomin; Yu, Guirui

2014-01-01

Because evapotranspiration (ET) is the second largest component of the water cycle and a critical process in terrestrial ecosystems, understanding the inter-annual variability of ET is important in the context of global climate change. Eight years of continuous eddy covariance measurements (2003-2010) in a subtropical coniferous plantation were used to investigate the impacts of climatic factors and ecosystem responses on the inter-annual variability of ET. The mean and standard deviation of annual ET for 2003-2010 were 786.9 and 103.4 mm (with a coefficient of variation of 13.1%), respectively. The inter-annual variability of ET was largely created in three periods: March, May-June, and October, which are the transition periods between seasons. A set of look-up table approaches were used to separate the sources of inter-annual variability of ET. The annual ETs were calculated by assuming that (a) both the climate and ecosystem responses among years are variable (Vcli-eco), (b) the climate is variable but the ecosystem responses are constant (Vcli), and (c) the climate is constant but ecosystem responses are variable (Veco). The ETs that were calculated under the above assumptions suggested that the inter-annual variability of ET was dominated by ecosystem responses and that there was a negative interaction between the effects of climate and ecosystem responses. These results suggested that for long-term predictions of water and energy balance in global climate change projections, the ecosystem responses must be taken into account to better constrain the uncertainties associated with estimation.

Effects of Climatic Factors and Ecosystem Responses on the Inter-Annual Variability of Evapotranspiration in a Coniferous Plantation in Subtropical China

PubMed Central

Xu, Mingjie; Wen, Xuefa; Wang, Huimin; Zhang, Wenjiang; Dai, Xiaoqin; Song, Jie; Wang, Yidong; Fu, Xiaoli; Liu, Yunfen; Sun, Xiaomin; Yu, Guirui

2014-01-01

Because evapotranspiration (ET) is the second largest component of the water cycle and a critical process in terrestrial ecosystems, understanding the inter-annual variability of ET is important in the context of global climate change. Eight years of continuous eddy covariance measurements (2003–2010) in a subtropical coniferous plantation were used to investigate the impacts of climatic factors and ecosystem responses on the inter-annual variability of ET. The mean and standard deviation of annual ET for 2003–2010 were 786.9 and 103.4 mm (with a coefficient of variation of 13.1%), respectively. The inter-annual variability of ET was largely created in three periods: March, May–June, and October, which are the transition periods between seasons. A set of look-up table approaches were used to separate the sources of inter-annual variability of ET. The annual ETs were calculated by assuming that (a) both the climate and ecosystem responses among years are variable (Vcli-eco), (b) the climate is variable but the ecosystem responses are constant (Vcli), and (c) the climate is constant but ecosystem responses are variable (Veco). The ETs that were calculated under the above assumptions suggested that the inter-annual variability of ET was dominated by ecosystem responses and that there was a negative interaction between the effects of climate and ecosystem responses. These results suggested that for long-term predictions of water and energy balance in global climate change projections, the ecosystem responses must be taken into account to better constrain the uncertainties associated with estimation. PMID:24465610

Solving the Global Climate Monitoring Problem in the Atmosphere: Towards SI-tied Climate Records with Integrated Uncertainty Propagation

NASA Astrophysics Data System (ADS)

Kirchengast, G.; Schwaerz, M.; Fritzer, J.; Schwarz, J.; Scherllin-Pirscher, B.; Steiner, A. K.

2013-12-01

Monitoring the atmosphere to gain accurate and long-term stable records of essential climate variables (ECVs) such as temperature and greenhouse gases is the backbone of contemporary atmospheric and climate science. Earth observation from space is the key to obtain such data globally in the atmosphere. Currently, however, not any existing satellite-based atmospheric ECV record can serve as authoritative benchmark over months to decades so that climate variability and change in the atmosphere are not yet reliably monitored. Radio occultation (RO) using Global Navigation Satellite System (GNSS) signals provides a unique opportunity to solve this problem in the free atmosphere (from ~1-2 km altitude upwards) for core ECVs: the thermodynamic variables temperature and pressure, and to some degree water vapor, which are key parameters for tracking climate change. On top of RO we have recently conceived next-generation methods, microwave and infrared-laser occultation and nadir-looking infrared-laser reflectometry. These can monitor a full set of thermo-dynamic ECVs (incl. wind) as well as the greenhouse gases such as carbon dioxide and methane as main drivers of climate change; for the latter we also target the boundary layer for tracking carbon sources and sinks. We briefly introduce to why the atmospheric climate monitoring challenge is unsolved so far and why just the above methods have the capabilities to break through. We then focus on RO, which already provided more than a decade of observations. RO accurately measures time delays from refraction of GNSS signals during atmospheric occultation events. This enables to tie RO-derived ECVs and their uncertainty to fundamental time standards, effectively the SI second, and to their unique long-term stability and narrow uncertainty. However, despite impressive advances since the pioneering RO mission GPS/Met in the mid-1990ties no rigorous trace from fundamental time to the ECVs (duly accounting also for relevant side influences) exists so far. Establishing such a trace first-time in form of the Reference Occultation Processing System rOPS, providing reference RO data for climate science and applications, is therefore a current cornerstone endeavor at the Wegener Center over 2011 to 2015, supported also by colleagues from other key groups at EUMETSAT Darmstadt, UCAR Boulder, DMI Copenhagen, ECMWF Reading, IAP Moscow, AIUB Berne, and RMIT Melbourne. With the rOPS we undertake to process the full chain from the SI-tied raw data to the atmospheric ECVs with integrated uncertainty propagation. We summarize where we currently stand in quantifying RO accuracy and long-term stability and then discuss the concept, development status and initial results from the rOPS, with emphasis on its novel capability to provide SI-tied reference data with integrated uncertainty estimation. We comment how these data can provide ground-breaking support to challenges such as climate model evaluation, anthropogenic change detection and attribution, and calibration of complementary climate observing systems.

Assessing the Impacts of Climate Change on Tourism-Dependent Communities in the Great Lakes

NASA Astrophysics Data System (ADS)

Chin, N.; Day, J.; Sydnor, S.; Cherkauer, K. A.

2013-12-01

Tourism is an essential element of the Laurentian Great Lakes economy as well as one of the sectors expected to be affected most by climate change, particularly through extreme weather events. While studies looking at climate change impacts on the Great Lakes tourism, specifically, are limited, the results of other studies suggest that both summer tourism activities, such as beach-going, and winter tourism activities, such as skiing and snowboarding, could feel the effects of a changing climate. The purpose of this study was to determine how existing data and models might be used to predict the potential impacts of climate change on tourism-dependent communities at the local scale. Future climate projections and variable infiltration capacity (VIC) model simulations based on historical climate data were used to quantify trends in environmental metrics with a potential influence on tourism for several tourism-dependent Great Lakes communities. The results of this research show that the potential impacts of climate change vary at the local scale and could require different adaptation strategies for different communities and for different sectors of the tourism industry. For example, communities in the northern parts of the Great Lakes may find benefit in a greater diversification of their tourism industries, given that warming temperatures could be beneficial for summer tourism activities, while communities in the southern parts of the Great Lakes may have to find other ways to cope with climate conditions that are less conducive to summer tourism activities. Stakeholder input could also help inform the process of producing scientific information that is useful to policymakers when it comes to tourism sector-related decision making.

Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

NASA Astrophysics Data System (ADS)

Jones, Philip D.; Harpham, Colin; Troccoli, Alberto; Gschwind, Benoit; Ranchin, Thierry; Wald, Lucien; Goodess, Clare M.; Dorling, Stephen

2017-07-01

The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979-2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos

2016-01-01

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

Response-Guided Community Detection: Application to Climate Index Discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bello, Gonzalo; Angus, Michael; Pedemane, Navya

Discovering climate indices-time series that summarize spatiotemporal climate patterns-is a key task in the climate science domain. In this work, we approach this task as a problem of response-guided community detection; that is, identifying communities in a graph associated with a response variable of interest. To this end, we propose a general strategy for response-guided community detection that explicitly incorporates information of the response variable during the community detection process, and introduce a graph representation of spatiotemporal data that leverages information from multiple variables. We apply our proposed methodology to the discovery of climate indices associated with seasonal rainfall variability.more » Our results suggest that our methodology is able to capture the underlying patterns known to be associated with the response variable of interest and to improve its predictability compared to existing methodologies for data-driven climate index discovery and official forecasts.« less

Does internal climate variability overwhelm climate change signals in streamflow? The upper Po and Rhone basin case studies.

PubMed

Fatichi, S; Rimkus, S; Burlando, P; Bordoy, R

2014-09-15

Projections of climate change effects in streamflow are increasingly required to plan water management strategies. These projections are however largely uncertain due to the spread among climate model realizations, internal climate variability, and difficulties in transferring climate model results at the spatial and temporal scales required by catchment hydrology. A combination of a stochastic downscaling methodology and distributed hydrological modeling was used in the ACQWA project to provide projections of future streamflow (up to year 2050) for the upper Po and Rhone basins, respectively located in northern Italy and south-western Switzerland. Results suggest that internal (stochastic) climate variability is a fundamental source of uncertainty, typically comparable or larger than the projected climate change signal. Therefore, climate change effects in streamflow mean, frequency, and seasonality can be masked by natural climatic fluctuations in large parts of the analyzed regions. An exception to the overwhelming role of stochastic variability is represented by high elevation catchments fed by glaciers where streamflow is expected to be considerably reduced due to glacier retreat, with consequences appreciable in the main downstream rivers in August and September. Simulations also identify regions (west upper Rhone and Toce, Ticino river basins) where a strong precipitation increase in the February to April period projects streamflow beyond the range of natural climate variability during the melting season. This study emphasizes the importance of including internal climate variability in climate change analyses, especially when compared to the limited uncertainty that would be accounted for by few deterministic projections. The presented results could be useful in guiding more specific impact studies, although design or management decisions should be better based on reliability and vulnerability criteria as suggested by recent literature. Copyright © 2013 Elsevier B.V. All rights reserved.

A northern Australian coral record of seasonal rainfall and terrestrial runoff (1775-1986)

NASA Astrophysics Data System (ADS)

Patterson, E. W.; Cole, J. E.; Vetter, L.; Lough, J.

2017-12-01

Northern Australia is a climatically dynamic region influenced by both the El Niño-Southern Oscillation (ENSO) and the Australian monsoon. However, this region is largely devoid of long climate records with sub-annual resolution. Understanding long-term climate variations is essential to assess how the storm-prone coasts and rainfall-reliant rangelands of northern Australia have been impacted in the past and may be in the future. In this study, we present a continuous multicentury (1775-1986) coral reconstruction of rainfall and hydroclimate in northern Australia, developed from a Porites spp. coral core collected off the coast of Darwin, Northern Territory, Australia. We combined Ba/Ca measurements with luminescence data as tracers of terrestrial erosion and river discharge respectively. Our results show a strong seasonal cycle in Ba/Ca linked to wet austral summers driven by the Australian monsoon. The Ba/Ca record is corroborated by oxygen isotope data from the same coral and indices of regional river discharge and rainfall. Consistently high levels of Ba measured throughout the record further attest to the importance of river influence on this coral. Our record also shows changes in variability and the baseline level of Ba in coastal waters through time, which may be driven in part by historical land-use change, such as damming or agricultural practices. We will additionally use these records to examine decadal to centennial-scale variability in monsoonal precipitation and regional ENSO signals.

Groundwater recharge estimation under semi arid climate: Case of Northern Gafsa watershed, Tunisia

NASA Astrophysics Data System (ADS)

Melki, Achraf; Abdollahi, Khodayar; Fatahi, Rouhallah; Abida, Habib

2017-08-01

Natural groundwater recharge under semi arid climate, like rainfall, is subjected to large variations in both time and space and is therefore very difficult to predict. Nevertheless, in order to set up any strategy for water resources management in such regions, understanding the groundwater recharge variability is essential. This work is interested in examining the impact of rainfall on the aquifer system recharge in the Northern Gafsa Plain in Tunisia. The study is composed of two main parts. The first is interested in the analysis of rainfall spatial and temporal variability in the study basin while the second is devoted to the simulation of groundwater recharge. Rainfall analysis was performed based on annual precipitation data recorded in 6 rainfall stations over a period of 56 years (1960-2015). Potential evapotranspiration data were also collected from 1960 to 2011 (52 years). The hydrologic distributed model WetSpass was used for the estimation of groundwater recharge. Model calibration was performed based on an assessment of the agreement between the sum of recharge and runoff values estimated by the WetSpass hydrological model and those obtained by the climatic method. This latter is based on the difference calculated between rainfall and potential evapotranspiration recorded at each rainy day. Groundwater recharge estimation, on monthly scale, showed that average annual precipitation (183.3 mm/year) was partitioned to 5, 15.3, 36.8, and 42.8% for interception, runoff, actual evapotranspiration and recharge respectively.

Data Mashups: Linking Human Health and Wellbeing with Weather, Climate and the Environment

NASA Astrophysics Data System (ADS)

Fleming, L. E.; Sarran, C.; Golding, B.; Haines, A.; Kessel, A.; Djennad, M.; Hajat, S.; Nichols, G.; Gordon Brown, H.; Depledge, M.

2016-12-01

A large part of the global disease burden can be linked to environmental factors, underpinned by unhealthy behaviours. Research into these linkages suffers from lack of common tools and databases for investigations across many different scientific disciplines to explore these complex associations. The MEDMI (Medical and Environmental Data-a Mash-up Infrastructure) Partnership brings together leading organisations and researchers in climate, weather, environment, and human health. We have created a proof-of-concept central data and analysis system with the UK Met Office and Public Health England data as the internet-based MEDMI Platform (www.data-mashup.org.uk) to serve as a common resource for researchers to link and analyse complex meteorological, environmental and epidemiological data in the UK. The Platform is hosted on its own dedicated server, with secure internet and in-person access with appropriate safeguards for ethical, copyright, security, preservation, and data sharing issues. Via the Platform, there is a demonstration Browser Application with access to user-selected subsets of the data for: a) analyses using time series (e.g. mortality/environmental variables), and b) data visualizations (e.g. infectious diseases/environmental variables). One demonstration project is linking climate change, harmful algal blooms and oceanographic modelling building on the hydrodynamic-biogeochemical coupled models; in situ and satellite observations as well as UK HAB data and hospital episode statistics data are being used for model verification and future forecasting. The MEDMI Project provides a demonstration of the potential, barriers and challenges, of these "data mashups" of environment and health data. Although there remain many challenges to creating and sustaining such a shared resource, these activities and resources are essential to truly explore the complex interactions between climate and other environmental change and health at the local and global scale.

Climate and ET: Does Plant Water Requirements Increase during Droughts?

NASA Astrophysics Data System (ADS)

Fipps, G.; Bonaiti, G.; Swanson, C.

2012-04-01

With the expected rise in global warming and increased frequency of extreme climate variability in the coming decades, conservation and efficient use of water resources is essential and must make use of the most accurate and representative data available. Historically, governmental and private organizations have used estimates of plant water use estimated from a variety of methods for long-term water planning, for designing hydraulic structures, and for establishing regulatory guidance and conservation programs intended to reduce water waste. In recent years, there has been an expansion of agricultural weather station networks which report daily ETo (potential evapotranspiration) and commercial irrigation controllers with instrumentation which calculate real-time ETo from weather parameters. Efforts are underway to use this more precise information for regional water planning and ETo is routinely used for designing and implementing drought response programs. The year 2011 marked the driest year on record in the State of Texas. Compounding the lack of rainfall was record heat during the summer of 2011. In 2011, real-time ETo (reference evapotranspiration) data in Texas was 30 to 50% higher than historic averages. The implications are quite serious, as most current water planning and drought contingency plans do not take into consideration increases in ET during such periods, and irrigation planning and capacity sizing are based on historic averages of consumptive use. This paper examines the relationship between ET and climate during this extreme climatic event. While the solar radiation was near normal levels, temperature and wind was much higher and dew points much lower than norms. The variability and statistical difference between long term average ETo and ETo measurements (from 2006 to 2011) for selected weather stations of the Texas ET Network.

The interplay of internal and forced modes of Hadley Cell expansion: lessons from the global warming hiatus

NASA Astrophysics Data System (ADS)

Amaya, Dillon J.; Siler, Nicholas; Xie, Shang-Ping; Miller, Arthur J.

2017-09-01

The poleward branches of the Hadley Cells and the edge of the tropics show a robust poleward shift during the satellite era, leading to concerns over the possible encroachment of the globe's subtropical dry zones into currently temperate climates. The extent to which this trend is caused by anthropogenic forcing versus internal variability remains the subject of considerable debate. In this study, we use a Joint EOF method to identify two distinct modes of tropical width variability: (1) an anthropogenically-forced mode, which we identify using a 20-member simulation of the historical climate, and (2) an internal mode, which we identify using a 1000-year pre-industrial control simulation. The forced mode is found to be closely related to the top of the atmosphere radiative imbalance and exhibits a long-term trend since 1860, while the internal mode is essentially indistinguishable from the El Niño Southern Oscillation. Together these two modes explain an average of 70% of the interannual variability seen in model "edge indices" over the historical period. Since 1980, the superposition of forced and internal modes has resulted in a period of accelerated Hadley Cell expansion and decelerated global warming (i.e., the "hiatus"). A comparison of the change in these modes since 1980 indicates that by 2013 the signal has emerged above the noise of internal variability in the Southern Hemisphere, but not in the Northern Hemisphere, with the latter also exhibiting strong zonal asymmetry, particularly in the North Atlantic. Our results highlight the important interplay of internal and forced modes of tropical width change and improve our understanding of the interannual variability and long-term trend seen in observations.

Improvements in sub-grid, microphysics averages using quadrature based approaches

NASA Astrophysics Data System (ADS)

Chowdhary, K.; Debusschere, B.; Larson, V. E.

2013-12-01

Sub-grid variability in microphysical processes plays a critical role in atmospheric climate models. In order to account for this sub-grid variability, Larson and Schanen (2013) propose placing a probability density function on the sub-grid cloud microphysics quantities, e.g. autoconversion rate, essentially interpreting the cloud microphysics quantities as a random variable in each grid box. Random sampling techniques, e.g. Monte Carlo and Latin Hypercube, can be used to calculate statistics, e.g. averages, on the microphysics quantities, which then feed back into the model dynamics on the coarse scale. We propose an alternate approach using numerical quadrature methods based on deterministic sampling points to compute the statistical moments of microphysics quantities in each grid box. We have performed a preliminary test on the Kessler autoconversion formula, and, upon comparison with Latin Hypercube sampling, our approach shows an increased level of accuracy with a reduction in sample size by almost two orders of magnitude. Application to other microphysics processes is the subject of ongoing research.

Climate Prediction Center - Outreach: 41st Annual Climate Diagnostics &

Science.gov Websites

the University of Maine Climate Change Institute and School of Earth and Climate Sciences and is co (drought, heat waves, severe weather, tropical cyclones) in the framework of climate variability and change and including the use of paleoclimate data. Arctic climate variability and change, and linkages to

Accurate Radiometry from Space: An Essential Tool for Climate Studies

NASA Technical Reports Server (NTRS)

Fox, Nigel; Kaiser-Weiss, Andrea; Schmutz, Werner; Thome, Kurtis; Young, Dave; Wielicki, Bruce; Winkler, Rainer; Woolliams, Emma

2011-01-01

The Earth s climate is undoubtedly changing; however, the time scale, consequences and causal attribution remain the subject of significant debate and uncertainty. Detection of subtle indicators from a background of natural variability requires measurements over a time base of decades. This places severe demands on the instrumentation used, requiring measurements of sufficient accuracy and sensitivity that can allow reliable judgements to be made decades apart. The International System of Units (SI) and the network of National Metrology Institutes were developed to address such requirements. However, ensuring and maintaining SI traceability of sufficient accuracy in instruments orbiting the Earth presents a significant new challenge to the metrology community. This paper highlights some key measurands and applications driving the uncertainty demand of the climate community in the solar reflective domain, e.g. solar irradiances and reflectances/radiances of the Earth. It discusses how meeting these uncertainties facilitate significant improvement in the forecasting abilities of climate models. After discussing the current state of the art, it describes a new satellite mission, called TRUTHS, which enables, for the first time, high-accuracy SI traceability to be established in orbit. The direct use of a primary standard and replication of the terrestrial traceability chain extends the SI into space, in effect realizing a metrology laboratory in space . Keywords: climate change; Earth observation; satellites; radiometry; solar irradiance

GEO Carbon and GHG Initiative Task 3: Optimizing in-situ measurements of essential carbon cycle variables across observational networks

NASA Astrophysics Data System (ADS)

Durden, D.; Muraoka, H.; Scholes, R. J.; Kim, D. G.; Loescher, H. W.; Bombelli, A.

2017-12-01

The development of an integrated global carbon cycle observation system to monitor changes in the carbon cycle, and ultimately the climate system, across the globe is of crucial importance in the 21stcentury. This system should be comprised of space and ground-based observations, in concert with modelling and analysis, to produce more robust budgets of carbon and other greenhouse gases (GHGs). A global initiative, the GEO Carbon and GHG Initiative, is working within the framework of Group on Earth Observations (GEO) to promote interoperability and provide integration across different parts of the system, particularly at domain interfaces. Thus, optimizing the efforts of existing networks and initiatives to reduce uncertainties in budgets of carbon and other GHGs. This is a very ambitious undertaking; therefore, the initiative is separated into tasks to provide actionable objectives. Task 3 focuses on the optimization of in-situ observational networks. The main objective of Task 3 is to develop and implement a procedure for enhancing and refining the observation system for identified essential carbon cycle variables (ECVs) that meets user-defined specifications at minimum total cost. This work focuses on the outline of the implementation plan, which includes a review of essential carbon cycle variables and observation technologies, mapping the ECVs performance, and analyzing gaps and opportunities in order to design an improved observing system. A description of the gap analysis of in-situ observations that will begin in the terrestrial domain to address issues of missing coordination and large spatial gaps, then extend to ocean and atmospheric observations in the future, will be outlined as the subsequent step to landscape mapping of existing observational networks.

School Climate: An Essential Component of a Comprehensive School Safety Plan

ERIC Educational Resources Information Center

Stark, Heidi

2017-01-01

The intentional assessment and management of school climate is an essential component of a comprehensive school safety plan. The value of this preventive aspect of school safety is often diminished as schools invest resources in physical security measures as a narrowly focused effort to increase school safety (Addington, 2009). This dissertation…

Complex life cycles and the responses of insects to climate change.

PubMed

Kingsolver, Joel G; Woods, H Arthur; Buckley, Lauren B; Potter, Kristen A; MacLean, Heidi J; Higgins, Jessica K

2011-11-01

Many organisms have complex life cycles with distinct life stages that experience different environmental conditions. How does the complexity of life cycles affect the ecological and evolutionary responses of organisms to climate change? We address this question by exploring several recent case studies and synthetic analyses of insects. First, different life stages may inhabit different microhabitats, and may differ in their thermal sensitivities and other traits that are important for responses to climate. For example, the life stages of Manduca experience different patterns of thermal and hydric variability, and differ in tolerance to high temperatures. Second, life stages may differ in their mechanisms for adaptation to local climatic conditions. For example, in Colias, larvae in different geographic populations and species adapt to local climate via differences in optimal and maximal temperatures for feeding and growth, whereas adults adapt via differences in melanin of the wings and in other morphological traits. Third, we extend a recent analysis of the temperature-dependence of insect population growth to demonstrate how changes in temperature can differently impact juvenile survival and adult reproduction. In both temperate and tropical regions, high rates of adult reproduction in a given environment may not be realized if occasional, high temperatures prevent survival to maturity. This suggests that considering the differing responses of multiple life stages is essential to understand the ecological and evolutionary consequences of climate change. © The Author 2011. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved.

Forecasting Glacier Evolution and Hindcasting Paleoclimates In Light of Mass Balance Nonlinearities

NASA Astrophysics Data System (ADS)

Malone, A.; Doughty, A. M.; MacAyeal, D. R.

2016-12-01

Glaciers are commonly used barometers of present and past climate change, with their variations often being linked to shifts in the mean climate. Climate variability within a unchanging mean state, however, can produce short term mass balance and glacier length anomalies, complicating this linkage. Also, the mass balance response to this variability can be nonlinear, possibly impacting the longer term state of the glacier. We propose a conceptual model to understand these nonlinearities and quantify their impacts on the longer term mass balance and glacier length. The relationship between mass balance and elevation, i.e. the vertical balance profile (VBP), illuminates these nonlinearities (Figure A). The VBP, given here for a wet tropical glacier, is piecewise, which can lead to different mass balance responses to climate anomalies of similar magnitude but opposite sign. We simulate the mass balance response to climate variability by vertically (temperature anomalies) and horizontally (precipitation anomalies) transposing the VBP for the mean climate (Figure A). The resulting anomalous VBP is the superposition of the two translations. We drive a 1-D flowline model with 10,000 years of anomalous VBPs. The aggregate VBP for the mean climate including variability differs from the VBP for the mean climate excluding variability, having a higher equilibrium line altitude (ELA) and a negative mass balance (Figure B). Accordingly, the glacier retreats, and the equilibrium glacier length for the aggregate VBP is the same as the mean length from the 10,000 year flowline simulation (Figure C). The magnitude of the VBP shift and glacier retreat increases with greater temperature variability and larger discontinuities in the VBP slope. These results highlight the importance of both the climate mean and variability in determining the longer term state of the glacier. Thus, forecasting glacier evolution or hindcasting past climates should also include representation of climate variability.

Impacts of climate change and variability on transportation systems and infrastructure : Gulf Coast study, phase 2 : task 2 : climate variability and change in Mobile, Alabama.

DOT National Transportation Integrated Search

2012-09-01

Despite increasing confidence in global climate change projections in recent years, projections of : climate effects at local scales remains scarce. Location-specific risks to transportation systems : imposed by changes in climate are not yet well kn...

Assessing performance and seasonal bias of pollen-based climate reconstructions in a perfect model world

NASA Astrophysics Data System (ADS)

Rehfeld, Kira; Trachsel, Mathias; Telford, Richard J.; Laepple, Thomas

2016-12-01

Reconstructions of summer, winter or annual mean temperatures based on the species composition of bio-indicators such as pollen, foraminifera or chironomids are routinely used in climate model-proxy data comparison studies. Most reconstruction algorithms exploit the joint distribution of modern spatial climate and species distribution for the development of the reconstructions. They rely on the space-for-time substitution and the specific assumption that environmental variables other than those reconstructed are not important or that their relationship with the reconstructed variable(s) should be the same in the past as in the modern spatial calibration dataset. Here we test the implications of this "correlative uniformitarianism" assumption on climate reconstructions in an ideal model world, in which climate and vegetation are known at all times. The alternate reality is a climate simulation of the last 6000 years with dynamic vegetation. Transient changes of plant functional types are considered as surrogate pollen counts and allow us to establish, apply and evaluate transfer functions in the modeled world. We find that in our model experiments the transfer function cross validation r2 is of limited use to identify reconstructible climate variables, as it only relies on the modern spatial climate-vegetation relationship. However, ordination approaches that assess the amount of fossil vegetation variance explained by the reconstructions are promising. We furthermore show that correlations between climate variables in the modern climate-vegetation relationship are systematically extended into the reconstructions. Summer temperatures, the most prominent driving variable for modeled vegetation change in the Northern Hemisphere, are accurately reconstructed. However, the amplitude of the model winter and mean annual temperature cooling between the mid-Holocene and present day is overestimated and similar to the summer trend in magnitude. This effect occurs because temporal changes of a dominant climate variable, such as summer temperatures in the model's Arctic, are imprinted on a less important variable, leading to reconstructions biased towards the dominant variable's trends. Our results, although based on a model vegetation that is inevitably simpler than reality, indicate that reconstructions of multiple climate variables based on modern spatial bio-indicator datasets should be treated with caution. Expert knowledge on the ecophysiological drivers of the proxies, as well as statistical methods that go beyond the cross validation on modern calibration datasets, are crucial to avoid misinterpretation.

Change in the magnitude and mechanisms of global temperature variability with warming.

PubMed

Brown, Patrick T; Ming, Yi; Li, Wenhong; Hill, Spencer A

2017-01-01

Natural unforced variability in global mean surface air temperature (GMST) can mask or exaggerate human-caused global warming, and thus a complete understanding of this variability is highly desirable. Significant progress has been made in elucidating the magnitude and physical origins of present-day unforced GMST variability, but it has remained unclear how such variability may change as the climate warms. Here we present modeling evidence that indicates that the magnitude of low-frequency GMST variability is likely to decline in a warmer climate and that its generating mechanisms may be fundamentally altered. In particular, a warmer climate results in lower albedo at high latitudes, which yields a weaker albedo feedback on unforced GMST variability. These results imply that unforced GMST variability is dependent on the background climatological conditions, and thus climate model control simulations run under perpetual preindustrial conditions may have only limited relevance for understanding the unforced GMST variability of the future.

Change in the Magnitude and Mechanisms of Global Temperature Variability with Warming

NASA Astrophysics Data System (ADS)

Brown, P. T.; Ming, Y.; Li, W.; Hill, S. A.

2017-12-01

Natural unforced variability in global mean surface air temperature (GMST) can mask or exaggerate human-caused global warming, and thus a complete understanding of this variability is highly desirable. Significant progress has been made in elucidating the magnitude and physical origins of present-day unforced GMST variability, but it has remained unclear how such variability may change as the climate warms. Here we present modeling evidence that indicates that the magnitude of low-frequency GMST variability is likely to decline in a warmer climate and that its generating mechanisms may be fundamentally altered. In particular, a warmer climate results in lower albedo at high latitudes, which yields a weaker albedo feedback on unforced GMST variability. These results imply that unforced GMST variability is dependent on the background climatological conditions, and thus climate model control simulations run under perpetual preindustrial conditions may have only limited relevance for understanding the unforced GMST variability of the future.

The CESM Large Ensemble Project: Inspiring New Ideas and Understanding

NASA Astrophysics Data System (ADS)

Kay, J. E.; Deser, C.

2016-12-01

While internal climate variability is known to affect climate projections, its influence is often underappreciated and confused with model error. Why? In general, modeling centers contribute a small number of realizations to international climate model assessments [e.g., phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. As a result, model error and internal climate variability are difficult, and at times impossible, to disentangle. In response, the Community Earth System Model (CESM) community designed the CESM Large Ensemble (CESM-LE) with the explicit goal of enabling assessment of climate change in the presence of internal climate variability. All CESM-LE simulations use a single CMIP5 model (CESM with the Community Atmosphere Model, version 5). The core simulations replay the twenty to twenty-first century (1920-2100) 40+ times under historical and representative concentration pathway 8.5 external forcing with small initial condition differences. Two companion 2000+-yr-long preindustrial control simulations (fully coupled, prognostic atmosphere and land only) allow assessment of internal climate variability in the absence of climate change. Comprehensive outputs, including many daily fields, are available as single-variable time series on the Earth System Grid for anyone to use. Examples of scientists and stakeholders that are using the CESM-LE outputs to help interpret the observational record, to understand projection spread and to plan for a range of possible futures influenced by both internal climate variability and forced climate change will be highlighted the presentation.

LAMPPOST: A Mnemonic Device for Teaching Climate Variables

ERIC Educational Resources Information Center

Fahrer, Chuck; Harris, Dan

2004-01-01

This article introduces the word "LAMPPOST" as a mnemonic device to aid in the instruction of climate variables. It provides instructors with a framework for discussing climate patterns that is based on eight variables: latitude, altitude, maritime influence and continentality, pressure systems, prevailing winds, ocean currents, storms, and…

A Generalized Stability Analysis of the AMOC in Earth System Models: Implication for Decadal Variability and Abrupt Climate Change

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fedorov, Alexey V.

2015-01-14

The central goal of this research project was to understand the mechanisms of decadal and multi-decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) as related to climate variability and abrupt climate change within a hierarchy of climate models ranging from realistic ocean models to comprehensive Earth system models. Generalized Stability Analysis, a method that quantifies the transient and asymptotic growth of perturbations in the system, is one of the main approaches used throughout this project. The topics we have explored range from physical mechanisms that control AMOC variability to the factors that determine AMOC predictability in the Earth systemmore » models, to the stability and variability of the AMOC in past climates.« less

Alternating high and low climate variability: The context of natural selection and speciation in Plio-Pleistocene hominin evolution.

PubMed

Potts, Richard; Faith, J Tyler

2015-10-01

Interaction of orbital insolation cycles defines a predictive model of alternating phases of high- and low-climate variability for tropical East Africa over the past 5 million years. This model, which is described in terms of climate variability stages, implies repeated increases in landscape/resource instability and intervening periods of stability in East Africa. It predicts eight prolonged (>192 kyr) eras of intensified habitat instability (high variability stages) in which hominin evolutionary innovations are likely to have occurred, potentially by variability selection. The prediction that repeated shifts toward high climate variability affected paleoenvironments and evolution is tested in three ways. In the first test, deep-sea records of northeast African terrigenous dust flux (Sites 721/722) and eastern Mediterranean sapropels (Site 967A) show increased and decreased variability in concert with predicted shifts in climate variability. These regional measurements of climate dynamics are complemented by stratigraphic observations in five basins with lengthy stratigraphic and paleoenvironmental records: the mid-Pleistocene Olorgesailie Basin, the Plio-Pleistocene Turkana and Olduvai Basins, and the Pliocene Tugen Hills sequence and Hadar Basin--all of which show that highly variable landscapes inhabited by hominin populations were indeed concentrated in predicted stages of prolonged high climate variability. Second, stringent null-model tests demonstrate a significant association of currently known first and last appearance datums (FADs and LADs) of the major hominin lineages, suites of technological behaviors, and dispersal events with the predicted intervals of prolonged high climate variability. Palynological study in the Nihewan Basin, China, provides a third test, which shows the occupation of highly diverse habitats in eastern Asia, consistent with the predicted increase in adaptability in dispersing Oldowan hominins. Integration of fossil, archeological, sedimentary, and paleolandscape evidence illustrates the potential influence of prolonged high variability on the origin and spread of critical adaptations and lineages in the evolution of Homo. The growing body of data concerning environmental dynamics supports the idea that the evolution of adaptability in response to climate and overall ecological instability represents a unifying theme in hominin evolutionary history. Published by Elsevier Ltd.

Beyond a Climate-Centric View of Plant Distribution: Edaphic Variables Add Value to Distribution Models

PubMed Central

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change. PMID:24658097

Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models.

PubMed

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change.

Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?

PubMed Central

Esperón-Rodríguez, Manuel; Baumgartner, John B.; Beaumont, Linda J.

2017-01-01

Background Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent. Methods This study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only. Results The predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables. Conclusions Our study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants. PMID:28652933

Understanding uncertainty in precipitation changes in a balanced perturbed-physics ensemble under multiple climate forcings

NASA Astrophysics Data System (ADS)

Millar, R.; Ingram, W.; Allen, M. R.; Lowe, J.

2013-12-01

Temperature and precipitation patterns are the climate variables with the greatest impacts on both natural and human systems. Due to the small spatial scales and the many interactions involved in the global hydrological cycle, in general circulation models (GCMs) representations of precipitation changes are subject to considerable uncertainty. Quantifying and understanding the causes of uncertainty (and identifying robust features of predictions) in both global and local precipitation change is an essential challenge of climate science. We have used the huge distributed computing capacity of the climateprediction.net citizen science project to examine parametric uncertainty in an ensemble of 20,000 perturbed-physics versions of the HadCM3 general circulation model. The ensemble has been selected to have a control climate in top-of-atmosphere energy balance [Yamazaki et al. 2013, J.G.R.]. We force this ensemble with several idealised climate-forcing scenarios including carbon dioxide step and transient profiles, solar radiation management geoengineering experiments with stratospheric aerosols, and short-lived climate forcing agents. We will present the results from several of these forcing scenarios under GCM parametric uncertainty. We examine the global mean precipitation energy budget to understand the robustness of a simple non-linear global precipitation model [Good et al. 2012, Clim. Dyn.] as a better explanation of precipitation changes in transient climate projections under GCM parametric uncertainty than a simple linear tropospheric energy balance model. We will also present work investigating robust conclusions about precipitation changes in a balanced ensemble of idealised solar radiation management scenarios [Kravitz et al. 2011, Atmos. Sci. Let.].

How Climate Science got to be in the Next Generation Science Standards (Invited)

NASA Astrophysics Data System (ADS)

Wysession, M. E.

2013-12-01

Climate science plays a prominent role in the new national K-12 Next Generation Science Standards (NGSS). This represents the culmination of a significant amount of effort by many different organizations that have worked hard to educate the public on one of the most interesting, complex, complicated, and societally important aspects of geoscience. While there are significant challenges to the full implementation of the NGSS, especially those aspects that relate to climate change, the fact that so many states are currently adopting the NGSS represents a significant milestone in geoscience education. When grade 6-12 textbooks were written ten years ago, such as Pearson's high school Physical Science: Concepts in Action (Wysession et al., 2004), very little mention of climate change was incorporated because it did not appear in state standards. Now, climate and climate change are an integral part of the middle school and high school NGSS standards, and textbook companies are fully incorporating this content into their programs. There are many factors that have helped the shift toward teaching about climate, such as the IPCC report, Al Gore's 'An Inconvenient Truth,' and the many reports on climate change published by the National Research Council (NRC). However, four major community-driven literacy documents (The Essential Principles of Ocean Science, Essential Principles and Fundamental Concepts for Atmospheric Science Literacy, The Earth Science Literacy Principles, and The Essential Principles of Climate Science) were essential in that they directly informed the construction of the Earth and Space Science (ESS) content of the NRC's 'Framework for K-12 Science Education' by the ESS Design Team. The actual performance expectations of the NGSS were then informed directly by the disciplinary core ideas of the NRC Framework, which were motivated by the community-driven literacy documents and the significant credentials these bore. The work in getting climate science into classrooms has just begun: having standards that address climate science does not ensure that it will reach students. However, the fact that climate science plays an important role in the nation's first attempt at a national K-12 science program represents a significant advancement.

How Climate Science got to be in the Next Generation Science Standards (Invited)

NASA Astrophysics Data System (ADS)

Westnedge, K. L.; Dallimore, A.; Salish Sea Expedition Team

2011-12-01

Climate science plays a prominent role in the new national K-12 Next Generation Science Standards (NGSS). This represents the culmination of a significant amount of effort by many different organizations that have worked hard to educate the public on one of the most interesting, complex, complicated, and societally important aspects of geoscience. While there are significant challenges to the full implementation of the NGSS, especially those aspects that relate to climate change, the fact that so many states are currently adopting the NGSS represents a significant milestone in geoscience education. When grade 6-12 textbooks were written ten years ago, such as Pearson's high school Physical Science: Concepts in Action (Wysession et al., 2004), very little mention of climate change was incorporated because it did not appear in state standards. Now, climate and climate change are an integral part of the middle school and high school NGSS standards, and textbook companies are fully incorporating this content into their programs. There are many factors that have helped the shift toward teaching about climate, such as the IPCC report, Al Gore's 'An Inconvenient Truth,' and the many reports on climate change published by the National Research Council (NRC). However, four major community-driven literacy documents (The Essential Principles of Ocean Science, Essential Principles and Fundamental Concepts for Atmospheric Science Literacy, The Earth Science Literacy Principles, and The Essential Principles of Climate Science) were essential in that they directly informed the construction of the Earth and Space Science (ESS) content of the NRC's 'Framework for K-12 Science Education' by the ESS Design Team. The actual performance expectations of the NGSS were then informed directly by the disciplinary core ideas of the NRC Framework, which were motivated by the community-driven literacy documents and the significant credentials these bore. The work in getting climate science into classrooms has just begun: having standards that address climate science does not ensure that it will reach students. However, the fact that climate science plays an important role in the nation's first attempt at a national K-12 science program represents a significant advancement.

Contributions of the ARM Program to Radiative Transfer Modeling for Climate and Weather Applications

NASA Technical Reports Server (NTRS)

Mlawer, Eli J.; Iacono, Michael J.; Pincus, Robert; Barker, Howard W.; Oreopoulos, Lazaros; Mitchell, David L.

2016-01-01

Accurate climate and weather simulations must account for all relevant physical processes and their complex interactions. Each of these atmospheric, ocean, and land processes must be considered on an appropriate spatial and temporal scale, which leads these simulations to require a substantial computational burden. One especially critical physical process is the flow of solar and thermal radiant energy through the atmosphere, which controls planetary heating and cooling and drives the large-scale dynamics that moves energy from the tropics toward the poles. Radiation calculations are therefore essential for climate and weather simulations, but are themselves quite complex even without considering the effects of variable and inhomogeneous clouds. Clear-sky radiative transfer calculations have to account for thousands of absorption lines due to water vapor, carbon dioxide, and other gases, which are irregularly distributed across the spectrum and have shapes dependent on pressure and temperature. The line-by-line (LBL) codes that treat these details have a far greater computational cost than can be afforded by global models. Therefore, the crucial requirement for accurate radiation calculations in climate and weather prediction models must be satisfied by fast solar and thermal radiation parameterizations with a high level of accuracy that has been demonstrated through extensive comparisons with LBL codes. See attachment for continuation.

Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Kauwe, M. G.; Zhou, S. -X.; Medlyn, B. E.

Future climate change has the potential to increase drought in many regions of the globe, making it essential that land surface models (LSMs) used in coupled climate models realistically capture the drought responses of vegetation. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art LSMs currently assume the same drought sensitivity for all vegetation. We tested whether variable drought sensitivities are needed to explain the observed large-scale patterns of drought impact on the carbon, water and energy fluxes. We implemented data-driven drought sensitivities in the Community Atmosphere Biosphere Land Exchange (CABLE) LSMmore » and evaluated alternative sensitivities across a latitudinal gradient in Europe during the 2003 heatwave. The model predicted an overly abrupt onset of drought unless average soil water potential was calculated with dynamic weighting across soil layers. We found that high drought sensitivity at the most mesic sites, and low drought sensitivity at the most xeric sites, was necessary to accurately model responses during drought. Furthermore, our results indicate that LSMs will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.« less

Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe

DOE PAGES

De Kauwe, M. G.; Zhou, S. -X.; Medlyn, B. E.; ...

2015-12-21

Future climate change has the potential to increase drought in many regions of the globe, making it essential that land surface models (LSMs) used in coupled climate models realistically capture the drought responses of vegetation. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art LSMs currently assume the same drought sensitivity for all vegetation. We tested whether variable drought sensitivities are needed to explain the observed large-scale patterns of drought impact on the carbon, water and energy fluxes. We implemented data-driven drought sensitivities in the Community Atmosphere Biosphere Land Exchange (CABLE) LSMmore » and evaluated alternative sensitivities across a latitudinal gradient in Europe during the 2003 heatwave. The model predicted an overly abrupt onset of drought unless average soil water potential was calculated with dynamic weighting across soil layers. We found that high drought sensitivity at the most mesic sites, and low drought sensitivity at the most xeric sites, was necessary to accurately model responses during drought. Furthermore, our results indicate that LSMs will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.« less

JRC Copernicus Climate Change Service (C3S) F4P platform.

NASA Astrophysics Data System (ADS)

Mota, Bernardo; Cappucci, Fabrizio; Gobron, Nadine

2016-04-01

With the increasing number of Earth Observation satellites and derived land surface products, concerns of quality assurance led the Global Climate Observing System (GCOS) to establish accuracy criteria and standards. In this context, the Climate Change Copernicus Service (C3S) fitness-for-purpose (F4P) platform, developed at the Joint Research Centre, aims assessing the quality of land Essential Climate Variables (ECVs) in compliance with GCOS criteria. In this paper, we first summarize the JRC C3S FP4 goals and secondly present the automatic review platform to assess multi-mission physical consistencies and physical coherence of and between various land products, at global and regional scales. We propose new metrics, such as Gamma Index and Triple Collocation Error Model, for multi-mission product inter-comparison and stability assessment, and resource selection statistical methods to assess physical coherence with other related ECV products. Examples concern the consistency of five global albedo products (GlobAlbedo, GLASS, MCD43C3, GIO and MISR), between 2000 And 2011, and their coherence with four burnt area products (MCD45A1, MCD64A1, Fire_CCI and GIO) for the overlapping period (2006 to 2008). Preliminary results show reasonable agreement with the inherent limitations of each product algorithm and sensor resolution.

Characterizing a Century of Climate and Hydrological Variability of a Mediterranean and Mountainous Watersheds: the Durance River Case-Study

NASA Astrophysics Data System (ADS)

Mathevet, T.; Kuentz, A.; Gailhard, J.; Andreassian, V.

2013-12-01

Improving the understanding of mountain watersheds hydrological variability is a great scientific issue, for both researchers and water resources managers, such as Electricite de France (Energy and Hydropower Company). The past and current context of climate variability enhances the interest on this topic, since multi-purposes water resources management is highly sensitive to this variability. The Durance River watershed (14000 km2), situated in the French Alps, is a good example of the complexity of this issue. It is characterized by a variety of hydrological processes (from snowy to Mediterranean regimes) and a wide range of anthropogenic influences (hydropower, irrigation, flood control, tourism and water supply), mixing potential causes of changes in its hydrological regimes. As water related stakes are numerous in this watershed, improving knowledge on the hydrological variability of the Durance River appears to be essential. In this presentation, we would like to focus on a methodology we developed to build long-term historical hydrometeorological time-series, based on atmospheric reanalysis (20CR : 20th Century Reanalysis) and historical local observations. This methodology allowed us to generate precipitation, air temperature and streamflow time-series at a daily time-step for a sample of 22 watersheds, for the 1883-2010 period. These long-term streamflow reconstructions have been validated thanks to historical searches that allowed to bring to light ten long historical series of daily streamflows, beginning on the early 20th century. Reconstructions appear to have rather good statistical properties, with good correlation (greater than 0.8) and limited mean and variance bias (less than 5%). Then, these long-term hydrometeorological time-series allowed us to characterize the past variability in terms of available water resources, droughts or hydrological regime. These analyses help water resources managers to better know the range of hydrological variabilities, which are usually greatly underestimated with classical available time-series (less than 50 years).

Disease in a more variable and unpredictable climate

NASA Astrophysics Data System (ADS)

McMahon, T. A.; Raffel, T.; Rohr, J. R.; Halstead, N.; Venesky, M.; Romansic, J.

2014-12-01

Global climate change is shifting the dynamics of infectious diseases of humans and wildlife with potential adverse consequences for disease control. Despite this, the role of global climate change in the decline of biodiversity and the emergence of infectious diseases remains controversial. Climate change is expected to increase climate variability in addition to increasing mean temperatures, making climate less predictable. However, few empirical or theoretical studies have considered the effects of climate variability or predictability on disease, despite it being likely that hosts and parasites will have differential responses to climatic shifts. Here we present a theoretical framework for how temperature variation and its predictability influence disease risk by affecting host and parasite acclimation responses. Laboratory experiments and field data on disease-associated frog declines in Latin America support this framework and provide evidence that unpredictable temperature fluctuations, on both monthly and diurnal timescales, decrease frog resistance to the pathogenic chytrid fungus Batrachochytrium dendrobatidis (Bd). Furthermore, the pattern of temperature-dependent growth of the fungus on frogs was inconsistent with the pattern of Bd growth in culture, emphasizing the importance of accounting for the host-parasite interaction when predicting climate-dependent disease dynamics. Consistent with our laboratory experiments, increased regional temperature variability associated with global El Niño climatic events was the best predictor of widespread amphibian losses in the genus Atelopus. Thus, incorporating the effects of small-scale temporal variability in climate can greatly improve our ability to predict the effects of climate change on disease.

The Dynamics of Vulnerability and Implications for Climate Change Adaptation: Lessons from Urban Water Management

NASA Astrophysics Data System (ADS)

Dilling, L.; Daly, M.; Travis, W.; Wilhelmi, O.; Klein, R.; Kenney, D.; Ray, A. J.; Miller, K.

2013-12-01

Recent reports and scholarship have suggested that adapting to current climate variability may represent a "no regrets" strategy for adapting to climate change. Filling "adaptation deficits" and other approaches that rely on addressing current vulnerabilities are of course helpful for responding to current climate variability, but we find here that they are not sufficient for adapting to climate change. First, following a comprehensive review and unique synthesis of the natural hazards and climate adaptation literatures, we advance six reasons why adapting to climate variability is not sufficient for adapting to climate change: 1) Vulnerability is different at different levels of exposure; 2) Coping with climate variability is not equivalent to adaptation to longer term change; 3) The socioeconomic context for vulnerability is constantly changing; 4) The perception of risk associated with climate variability does not necessarily promote adaptive behavior in the face of climate change; 5) Adaptations made to short term climate variability may reduce the flexibility of the system in the long term; and 6) Adaptive actions may shift vulnerabilities to other parts of the system or to other people. Instead we suggest that decision makers faced with choices to adapt to climate change must consider the dynamics of vulnerability in a connected system-- how choices made in one part of the system might impact other valued outcomes or even create new vulnerabilities. Furthermore we suggest that rather than expressing climate change adaptation as an extension of adaptation to climate variability, the research and practice communities would do well to articulate adaptation as an imperfect policy, with tradeoffs and consequences and that decisions be prioritized to preserve flexibility be revisited often as climate change unfolds. We then present the results of a number of empirical studies of decision making for drought in urban water systems in the United States to understand: a) the variety of actions taken; b) the limitations of actions available to water managers; and c) the effectiveness of actions taken to date. Time permitting, we briefly present the results of 3 in-depth case studies of drought response and current perception of preparedness with respect to future drought and climate change among urban water system managers. We examine the role of governance, system connectivity, public perceptions and other factors in driving decision making and outcomes.

The role of climate variability in extreme floods in Europe

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Aerts, Jeroen C. J. H.; Jongman, Brenden; Ward, Philip J.

2017-04-01

Between 1980 and 2015, Europe experienced 18% of worldwide weather-related loss events, which accounted for over US500 billion in damage. Consequently, it is urgent to further develop adaptation strategies to mitigate the consequences of weather-related disasters, such as floods. Europe's capability to prepare for such disasters is challenged by a large range of uncertainties and a limited understanding of the driving forces of hydrometeorological hazards. One of the major sources of uncertainty is the relationship between climate variability and weather-related losses. Previous studies show that climate variability drives temporal changes in hydrometereological variables in Europe. However, their influence on flood risk has received little attention. We investigated the influence of the positive and negative phases of El Niño Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Arctic Oscillation (AO), on the seasonal frequency and intensity of extreme rainfall, and anomalies in flood occurrence and damage compared to the neutral phases of the indices of climate variability. Using statistical methods to analyze relationships between the indices of climate variability and four indicators of flooding, we found that positive and negative phases of NAO and AO are associated with more (or less) frequent and intense seasonal extreme rainfall over large areas of Europe. The relationship between ENSO and both the occurrence of extreme rainfall and intensity of extreme rainfall in Europe is much smaller than the relationship with NAO or AO, but still significant in some regions. We observe that flood damage and flood occurrence have strong links with climate variability, especially in southern and eastern Europe. Therefore, when investigating flooding across Europe, all three indices of climate variability should be considered. Seasonal forecasting of flooding could be enhanced by the inclusion of climate variability indicators .

Integrating geological archives and climate models for the mid-Pliocene warm period.

PubMed

Haywood, Alan M; Dowsett, Harry J; Dolan, Aisling M

2016-02-16

The mid-Pliocene Warm Period (mPWP) offers an opportunity to understand a warmer-than-present world and assess the predictive ability of numerical climate models. Environmental reconstruction and climate modelling are crucial for understanding the mPWP, and the synergy of these two, often disparate, fields has proven essential in confirming features of the past and in turn building confidence in projections of the future. The continual development of methodologies to better facilitate environmental synthesis and data/model comparison is essential, with recent work demonstrating that time-specific (time-slice) syntheses represent the next logical step in exploring climate change during the mPWP and realizing its potential as a test bed for understanding future climate change.

Integrating geological archives and climate models for the mid-Pliocene warm period

PubMed Central

Haywood, Alan M.; Dowsett, Harry J.; Dolan, Aisling M.

2016-01-01

The mid-Pliocene Warm Period (mPWP) offers an opportunity to understand a warmer-than-present world and assess the predictive ability of numerical climate models. Environmental reconstruction and climate modelling are crucial for understanding the mPWP, and the synergy of these two, often disparate, fields has proven essential in confirming features of the past and in turn building confidence in projections of the future. The continual development of methodologies to better facilitate environmental synthesis and data/model comparison is essential, with recent work demonstrating that time-specific (time-slice) syntheses represent the next logical step in exploring climate change during the mPWP and realizing its potential as a test bed for understanding future climate change. PMID:26879640

Using a down-scaled bioclimate envelope model to determine long-term temporal connectivity of Garry oak (Quercus garryana) habitat in western North America: implications for protected area planning.

PubMed

Pellatt, Marlow G; Goring, Simon J; Bodtker, Karin M; Cannon, Alex J

2012-04-01

Under the Canadian Species at Risk Act (SARA), Garry oak (Quercus garryana) ecosystems are listed as "at-risk" and act as an umbrella for over one hundred species that are endangered to some degree. Understanding Garry oak responses to future climate scenarios at scales relevant to protected area managers is essential to effectively manage existing protected area networks and to guide the selection of temporally connected migration corridors, additional protected areas, and to maintain Garry oak populations over the next century. We present Garry oak distribution scenarios using two random forest models calibrated with down-scaled bioclimatic data for British Columbia, Washington, and Oregon based on 1961-1990 climate normals. The suitability models are calibrated using either both precipitation and temperature variables or using only temperature variables. We compare suitability predictions from four General Circulation Models (GCMs) and present CGCM2 model results under two emissions scenarios. For each GCM and emissions scenario we apply the two Garry oak suitability models and use the suitability models to determine the extent and temporal connectivity of climatically suitable Garry oak habitat within protected areas from 2010 to 2099. The suitability models indicate that while 164 km(2) of the total protected area network in the region (47,990 km(2)) contains recorded Garry oak presence, 1635 and 1680 km(2) of climatically suitable Garry oak habitat is currently under some form of protection. Of this suitable protected area, only between 6.6 and 7.3% will be "temporally connected" between 2010 and 2099 based on the CGCM2 model. These results highlight the need for public and private protected area organizations to work cooperatively in the development of corridors to maintain temporal connectivity in climatically suitable areas for the future of Garry oak ecosystems.

Climate-Based Models for Understanding and Forecasting Dengue Epidemics

PubMed Central

Descloux, Elodie; Mangeas, Morgan; Menkes, Christophe Eugène; Lengaigne, Matthieu; Leroy, Anne; Tehei, Temaui; Guillaumot, Laurent; Teurlai, Magali; Gourinat, Ann-Claire; Benzler, Justus; Pfannstiel, Anne; Grangeon, Jean-Paul; Degallier, Nicolas; De Lamballerie, Xavier

2012-01-01

Background Dengue dynamics are driven by complex interactions between human-hosts, mosquito-vectors and viruses that are influenced by environmental and climatic factors. The objectives of this study were to analyze and model the relationships between climate, Aedes aegypti vectors and dengue outbreaks in Noumea (New Caledonia), and to provide an early warning system. Methodology/Principal Findings Epidemiological and meteorological data were analyzed from 1971 to 2010 in Noumea. Entomological surveillance indices were available from March 2000 to December 2009. During epidemic years, the distribution of dengue cases was highly seasonal. The epidemic peak (March–April) lagged the warmest temperature by 1–2 months and was in phase with maximum precipitations, relative humidity and entomological indices. Significant inter-annual correlations were observed between the risk of outbreak and summertime temperature, precipitations or relative humidity but not ENSO. Climate-based multivariate non-linear models were developed to estimate the yearly risk of dengue outbreak in Noumea. The best explicative meteorological variables were the number of days with maximal temperature exceeding 32°C during January–February–March and the number of days with maximal relative humidity exceeding 95% during January. The best predictive variables were the maximal temperature in December and maximal relative humidity during October–November–December of the previous year. For a probability of dengue outbreak above 65% in leave-one-out cross validation, the explicative model predicted 94% of the epidemic years and 79% of the non epidemic years, and the predictive model 79% and 65%, respectively. Conclusions/Significance The epidemic dynamics of dengue in Noumea were essentially driven by climate during the last forty years. Specific conditions based on maximal temperature and relative humidity thresholds were determinant in outbreaks occurrence. Their persistence was also crucial. An operational model that will enable health authorities to anticipate the outbreak risk was successfully developed. Similar models may be developed to improve dengue management in other countries. PMID:22348154

Costal vulnerability systems-network using Fuzzy and Bayesian approaches

NASA Astrophysics Data System (ADS)

Taramelli, A.; Valentini, E.; Filipponi, F.; Nguyen Xuan, A.; Arosio, M.

2016-12-01

Marine drivers such as surge in the context of SLR, are threatening low-lying coastal plains. In order to deal with disturbances a deeper understanding of benefits deriving from ecosystem services assesment, management and planning (e.g. the role of dune ridges in surge mitigation and climate adaptation) can enhance the resilience of coastal systems. In this frame assessing the vulnerability is a key concern of many SOS (social, ecological, institutional) that deals with several challenges like the definition of Essential Variables (EVs) able to synthesize the required information, the assignment of different weight to be attributed to each considered variable, the selection of method for combining the relevant variables, etc.. To this end it is unclear how SLR, subsidence and erosion might affect coastal subsistence resources because of highly complex interactions and because of the subjective system of weighting many variables and their interaction within the systems. In this contribution, making the best use of many EO products, in situ data and modelling, we propose a multidimensional surge vulnerability assessment that aims at combining together geophysical and socioeconomic variable on the base of different approaches: 1) Fuzzy Logic; 2) Bayesian approach. The final goal is providing insight in understanding how to quantify regulating ecosystem services.

Selecting climate change scenarios using impact-relevant sensitivities

Treesearch

Julie A. Vano; John B. Kim; David E. Rupp; Philip W. Mote

2015-01-01

Climate impact studies often require the selection of a small number of climate scenarios. Ideally, a subset would have simulations that both (1) appropriately represent the range of possible futures for the variable/s most important to the impact under investigation and (2) come from global climate models (GCMs) that provide plausible results for future climate in the...

Climate Controls AM Fungal Distributions from Global to Local Scales

NASA Astrophysics Data System (ADS)

Kivlin, S. N.; Hawkes, C.; Muscarella, R.; Treseder, K. K.; Kazenel, M.; Lynn, J.; Rudgers, J.

2016-12-01

Arbuscular mycorrhizal (AM) fungi have key functions in terrestrial biogeochemical processes; thus, determining the relative importance of climate, edaphic factors, and plant community composition on their geographic distributions can improve predictions of their sensitivity to global change. Local adaptation by AM fungi to plant hosts, soil nutrients, and climate suggests that all of these factors may control fungal geographic distributions, but their relative importance is unknown. We created species distribution models for 142 AM fungal taxa at the global scale with data from GenBank. We compared climate variables (BioClim and soil moisture), edaphic variables (phosphorus, carbon, pH, and clay content), and plant variables using model selection on models with (1) all variables, (2) climatic variables only (including soil moisture) and (3) resource-related variables only (all other soil parameters and NPP) using the MaxEnt algorithm evaluated with ENMEval. We also evaluated whether drivers of AM fungal distributions were phylogenetically conserved. To test whether global correlates of AM fungal distributions were reflected at local scales, we then surveyed AM fungi in nine plant hosts along three elevation gradients in the Upper Gunnison Basin, Colorado, USA. At the global scale, the distributions of 55% of AM fungal taxa were affected by both climate and soil resources, whereas 16% were only affected by climate and 29% were only affected by soil resources. Even for AM fungi that were affected by both climate and resources, the effects of climatic variables nearly always outweighed those of resources. Soil moisture and isothermality were the main climatic and NPP and soil carbon the main resource related factors influencing AM fungal distributions. Distributions of closely related AM fungal taxa were similarly affected by climate, but not by resources. Local scale surveys of AM fungi across elevations confirmed that climate was a key driver of AM fungal composition and root colonization, with weaker influences of plant identity and soil nutrients. These two studies across scales suggest prevailing effects of climate on AM fungal distributions. Thus, incorporating climate when forecasting future ranges of AM fungi will enhance predictions of AM fungal abundance and associated ecosystem functions.

Climate and Southern Africa's Water-Energy-Food Nexus

NASA Astrophysics Data System (ADS)

Conway, D.; Osborn, T.; Dorling, S.; Ringler, C.; Lankford, B.; Dalin, C.; Thurlow, J.; Zhu, T.; Deryng, D.; Landman, W.; Archer van Garderen, E.; Krueger, T.; Lebek, K.

2014-12-01

Numerous challenges coalesce to make Southern Africa emblematic of the connections between climate and the water-energy-food nexus. Rainfall and river flows in the region show high levels of variability across a range of spatial and temporal scales. Physical and socioeconomic exposure to climate variability and change is high, for example, the contribution of electricity produced from hydroelectric sources is over 30% in Madagascar and Zimbabwe and almost 100% in the DRC, Lesotho, Malawi, and Zambia. The region's economy is closely linked with that of the rest of the African continent and climate-sensitive food products are an important item of trade. Southern Africa's population is concentrated in regions exposed to high levels of hydro-meteorological variability, and will increase rapidly over the next four decades. The capacity to manage the effects of climate variability tends, however, to be low. Moreover, with climate change annual precipitation levels, soil moisture and runoff are likely to decrease and rising temperatures will increase evaporative demand. Despite high levels of hydro-meteorological variability, the sectoral and cross-sectoral water-energy-food linkages with climate in Southern Africa have not been considered in detail. Lack of data and questionable reliability are compounded by complex dynamic relationships. We review the role of climate in Southern Africa's nexus, complemented by empirical analysis of national level data on climate, water resources, crop and energy production, and economic activity. Our aim is to examine the role of climate variability as a driver of production fluctuations in the nexus, and to improve understanding of the magnitude and temporal dimensions of their interactions. We first consider national level exposure of food, water and energy production to climate in aggregate economic terms and then examine the linkages between interannual and multi-year climate variability and economic activity, focusing on food and hydropower production. We then review the potential for connecting areas with robust seasonal climate forecasting skill with key precursors of economic output and conclude by identifying knowledge gaps in our understanding of regional and national economic linkages in the climate and water-energy-food nexus.

Testing competing forms of the Milankovitch hypothesis: A multivariate approach

NASA Astrophysics Data System (ADS)

Kaufmann, Robert K.; Juselius, Katarina

2016-02-01

We test competing forms of the Milankovitch hypothesis by estimating the coefficients and diagnostic statistics for a cointegrated vector autoregressive model that includes 10 climate variables and four exogenous variables for solar insolation. The estimates are consistent with the physical mechanisms postulated to drive glacial cycles. They show that the climate variables are driven partly by solar insolation, determining the timing and magnitude of glaciations and terminations, and partly by internal feedback dynamics, pushing the climate variables away from equilibrium. We argue that the latter is consistent with a weak form of the Milankovitch hypothesis and that it should be restated as follows: internal climate dynamics impose perturbations on glacial cycles that are driven by solar insolation. Our results show that these perturbations are likely caused by slow adjustment between land ice volume and solar insolation. The estimated adjustment dynamics show that solar insolation affects an array of climate variables other than ice volume, each at a unique rate. This implies that previous efforts to test the strong form of the Milankovitch hypothesis by examining the relationship between solar insolation and a single climate variable are likely to suffer from omitted variable bias.

Climate Variability and Sugarcane Yield in Louisiana.

NASA Astrophysics Data System (ADS)

Greenland, David

2005-11-01

This paper seeks to understand the role that climate variability has on annual yield of sugarcane in Louisiana. Unique features of sugarcane growth in Louisiana and nonclimatic, yield-influencing factors make this goal an interesting and challenging one. Several methods of seeking and establishing the relations between yield and climate variables are employed. First, yield climate relations were investigated at a single research station where crop variety and growing conditions could be held constant and yield relations could be established between a predominant older crop variety and a newer one. Interviews with crop experts and a literature survey were used to identify potential climatic factors that control yield. A statistical analysis was performed using statewide yield data from the American Sugar Cane League from 1963 to 2002 and a climate database. Yield values for later years were adjusted downward to form an adjusted yield dataset. The climate database was principally constructed from daily and monthly values of maximum and minimum temperature and daily and monthly total precipitation for six cooperative weather-reporting stations representative of the area of sugarcane production. The influence of 74 different, though not independent, climate-related variables on sugarcane yield was investigated. The fact that a climate signal exists is demonstrated by comparing mean values of the climate variables corresponding to the upper and lower third of adjusted yield values. Most of these mean-value differences show an intuitively plausible difference between the high- and low-yield years. The difference between means of the climate variables for years corresponding to the upper and lower third of annual yield values for 13 of the variables is statistically significant at or above the 90% level. A correlation matrix was used to identify the variables that had the largest influence on annual yield. Four variables [called here critical climatic variables (CCV)], mean maximum August temperature, mean minimum February temperature, soil water surplus between April and September, and occurrence of autumn (fall) hurricanes, were built into a model to simulate adjusted yield values. The CCV model simulates the yield value with an rmse of 5.1 t ha-1. The mean of the adjusted yield data over the study period was 60.4 t ha-1, with values for the highest and lowest years being 73.1 and 50.6 t ha-1, respectively, and a standard deviation of 5.9 t ha-1. Presumably because of the almost constant high water table and soil water availability, higher precipitation totals, which are inversely related to radiation and temperature, tend to have a negative effect on the yields. Past trends in the values of critical climatic variables and general projections of future climate suggest that, with respect to the climatic environment and as long as land drainage is continued and maintained, future levels of sugarcane yield will rise in Louisiana.

Relationship between climatic variables and the variation in bulk tank milk composition using canonical correlation analysis.

PubMed

Stürmer, Morgana; Busanello, Marcos; Velho, João Pedro; Heck, Vanessa Isabel; Haygert-Velho, Ione Maria Pereira

2018-06-04

A number of studies have addressed the relations between climatic variables and milk composition, but these works used univariate statistical approaches. In our study, we used a multivariate approach (canonical correlation) to study the impact of climatic variables on milk composition, price, and monthly milk production at a dairy farm using bulk tank milk data. Data on milk composition, price, and monthly milk production were obtained from a dairy company that purchased the milk from the farm, while climatic variable data were obtained from the National Institute of Meteorology (INMET). The data are from January 2014 to December 2016. Univariate correlation analysis and canonical correlation analysis were performed. Few correlations between the climatic variables and milk composition were found using a univariate approach. However, using canonical correlation analysis, we found a strong and significant correlation (r c  = 0.95, p value = 0.0029). Lactose, ambient temperature measures (mean, minimum, and maximum), and temperature-humidity index (THI) were found to be the most important variables for the canonical correlation. Our study indicated that 10.2% of the variation in milk composition, pricing, and monthly milk production can be explained by climatic variables. Ambient temperature variables, together with THI, seem to have the most influence on variation in milk composition.

Relationship between climatic variables and the variation in bulk tank milk composition using canonical correlation analysis

NASA Astrophysics Data System (ADS)

Stürmer, Morgana; Busanello, Marcos; Velho, João Pedro; Heck, Vanessa Isabel; Haygert-Velho, Ione Maria Pereira

2018-06-01

A number of studies have addressed the relations between climatic variables and milk composition, but these works used univariate statistical approaches. In our study, we used a multivariate approach (canonical correlation) to study the impact of climatic variables on milk composition, price, and monthly milk production at a dairy farm using bulk tank milk data. Data on milk composition, price, and monthly milk production were obtained from a dairy company that purchased the milk from the farm, while climatic variable data were obtained from the National Institute of Meteorology (INMET). The data are from January 2014 to December 2016. Univariate correlation analysis and canonical correlation analysis were performed. Few correlations between the climatic variables and milk composition were found using a univariate approach. However, using canonical correlation analysis, we found a strong and significant correlation (r c = 0.95, p value = 0.0029). Lactose, ambient temperature measures (mean, minimum, and maximum), and temperature-humidity index (THI) were found to be the most important variables for the canonical correlation. Our study indicated that 10.2% of the variation in milk composition, pricing, and monthly milk production can be explained by climatic variables. Ambient temperature variables, together with THI, seem to have the most influence on variation in milk composition.

Separating out the influence of climatic trend, fluctuations, and extreme events on crop yield: a case study in Hunan Province, China

NASA Astrophysics Data System (ADS)

Wang, Zhu; Shi, Peijun; Zhang, Zhao; Meng, Yongchang; Luan, Yibo; Wang, Jiwei

2017-09-01

Separating out the influence of climatic trend, fluctuations and extreme events on crop yield is of paramount importance to climate change adaptation, resilience, and mitigation. Previous studies lack systematic and explicit assessment of these three fundamental aspects of climate change on crop yield. This research attempts to separate out the impacts on rice yields of climatic trend (linear trend change related to mean value), fluctuations (variability surpassing the "fluctuation threshold" which defined as one standard deviation (1 SD) of the residual between the original data series and the linear trend value for each climatic variable), and extreme events (identified by absolute criterion for each kind of extreme events related to crop yield). The main idea of the research method was to construct climate scenarios combined with crop system simulation model. Comparable climate scenarios were designed to express the impact of each climate change component and, were input to the crop system model (CERES-Rice), which calculated the related simulated yield gap to quantify the percentage impacts of climatic trend, fluctuations, and extreme events. Six Agro-Meteorological Stations (AMS) in Hunan province were selected to study the quantitatively impact of climatic trend, fluctuations and extreme events involving climatic variables (air temperature, precipitation, and sunshine duration) on early rice yield during 1981-2012. The results showed that extreme events were found to have the greatest impact on early rice yield (-2.59 to -15.89%). Followed by climatic fluctuations with a range of -2.60 to -4.46%, and then the climatic trend (4.91-2.12%). Furthermore, the influence of climatic trend on early rice yield presented "trade-offs" among various climate variables and AMS. Climatic trend and extreme events associated with air temperature showed larger effects on early rice yield than other climatic variables, particularly for high-temperature events (-2.11 to -12.99%). Finally, the methodology use to separate out the influences of the climatic trend, fluctuations, and extreme events on crop yield was proved to be feasible and robust. Designing different climate scenarios and feeding them into a crop system model is a potential way to evaluate the quantitative impact of each climate variable.

Factors affecting the evolution of coastal wetlands of the Laurentian Great Lakes: An overview

USGS Publications Warehouse

Mayer, T.; Edsall, T.; Munawar, M.

2004-01-01

Coastal wetlands play a pivotal role in the Great Lakes ecosystem. As buffer zones between the land and open waters of the Great Lakes, they perform a variety of essential functions providing both direct and indirect anthropogenic benefits. Geology, morphology and climate are the dominant variables that influence Laurentian Great Lakes wetland development. However, anthropogenic factors are the major contributors to alteration of natural wetland processes. This paper provides an overview of natural and anthropogenic factors important in Great Lakes coastal wetland development and provides statistical information describing the Great Lakes Basin. A brief description of wetlands classification and research issues is also presented.

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science through Cloud-Enabled Climate Analytics-as-a-Service

NASA Astrophysics Data System (ADS)

Schnase, J. L.; Duffy, D.; Tamkin, G. S.; Nadeau, D.; Thompson, J. H.; Grieg, C. M.; McInerney, M.; Webster, W. P.

2013-12-01

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS built on this principle. MERRA/AS enables MapReduce analytics over NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRA/AS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRA/AS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

NASA Technical Reports Server (NTRS)

Schnase, John L.; Duffy, Daniel Quinn; Tamkin, Glenn S.; Nadeau, Denis; Thompson, John H.; Grieg, Christina M.; McInerney, Mark A.; Webster, William P.

2014-01-01

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

Soil cover patterns influence on the land environmental functions, agroecological quality, land-use and monitoring efficiency in the Central Russia

NASA Astrophysics Data System (ADS)

Vasenev, Ivan; Yashin, Ivan; Lukin, Sergey; Valentini, Riccardo

2015-04-01

First decades of XXI century actualized for soil researches the principal methodical problem of most modern geosciences: what spatial and temporal scale would be optimal for land quality evaluation and land-use practice optimizing? It is becoming obvious that this question cannot have one solution and have to be solved with especial attention on the features of concrete region and landscape, land-use history and practical issues, land current state and environmental functions, soil cover patterns and variability, governmental requirements and local society needs, best available technologies and their potential profitability. Central Russia is one of the most dynamical economic regions with naturally high and man-made complicated landscape and soil cover variability, long-term land-use history and self-contradictory issues, high potential of profitable farming and increased risks of land degradation. Global climate and technological changes essentially complicate the originally high and sharply increased in XX century farming land heterogeneity in the Central Russia that actualizes system analysis of its zonal, intra-zonal and azonal soil cover patterns according to their influence on land environmental functions, agroecological quality, and land-use and monitoring efficiency variability. Developed by the Laboratory of agroecological monitoring, ecosystem modeling & prediction (LAMP / RTSAU with support of RF Governmental projects #11.G34.31.0079 and #14.120.14.4266) regional systems of greenhouse gases environmental monitoring RusFluxNet (6 fixed & 1 mobile eddy covariance stations with zonal functional sets of key plots with chamber investigations in 5 Russian regions) and of agroecological monitoring (in representative key plots with different farming practice in 9 RF regions) allow to do this analysis in frame of enough representative regional multi-factorial matrix of soil cover patterns, bioclimatic conditions, landscape features, and land-use history and current practice versions. Well-elaborated monitoring collaboration with the principal natural reserves in south-taiga and forest-steppe zones provides process-based interaction with long-term data on zonal climatic, landscape and soil features necessary to test the process, functional and evaluation models in the specific conditions of each bioclimatic zone. The dominated erosion and dehumification trends have been essentially activated for last 3-4 decades due to hu¬mus negative balance around 0.6-0.7 t ha-1year-1 and connected disaggregation with annual rate between 1 and 25 g/kg for aggregates 10-0.25 mm. "Standard" monitoring objects and regionally generalized data showed characteristic for Chernozems 2-2.5 % humus drop during this period and active processes of CO2 emission and humus eluvial-illuvial profile redistribution too. Forest-steppe Chernozems are usually characterized by higher stability than steppe ones. The ratio between erosive and biological losses in humus stock can be ten¬tatively estimated as fifty-fifty with essential variability within slope landscape. Both these processes have essential impacts on different sets of soil environmental and agroecological functions (including atmospheric air, surface and ground water quality, biodiversity and profitability) that we need to understand and predict. A drop of humus content below threshold values (for different soils between 1.5 and 6%) considerably reduces not only soil environmental regulation functions but also effectiveness of used fertilizers, crop yield quality and possibility of sustainable agricultural land-use. The carried out long-term researches of representative natural, rural and urban landscapes in Tver, Yaroslavl, Vladimir, Moscow, Kaluga, Kursk, Belgorod, Tambov, Voronezh and Saratov regions give us validation and ranging of the limiting factors of the elementary soil cover patterns current features and transformation processes, environmental functions and agroecological quality, monitoring results functional interpretation, spatial and temporal interpolation and extrapolation. These data allow essentially improve our understanding and quantitative assessments of the regional and within-field variability of land agroecological and environmental functions that is crucial for agroecosystem services evaluation, current and planned land-use environmental impacts, and DSS development for land-use agroecological optimizing taking into attention the regional and local landscapes features and most realistic scenarios of climate change and agro-technology transfer. Developed and verified within the project regionally adapted DSS (ACORD-R - RF #2012612944) gives effective informational and methodological support for land-use agroecological optimization.

Sea-level variability over the Common Era

NASA Astrophysics Data System (ADS)

Kopp, Robert; Horton, Benjamin; Kemp, Andrew; Engelhart, Simon; Little, Chris

2017-04-01

The Common Era (CE) sea-level response to climate forcing, and its relationship to centennial-timescale climate variability such as the Medieval Climate Anomaly (MCA) and the Little Ice Age (LIA), is fragmentary relative to other proxy-derived climate records (e.g. atmospheric surface temperature). However, the Atlantic coast of North America provides a rich sedimentary record of CE relative sea level with sufficient spatial and temporal resolution to inform mechanisms underlying regional and global sea level variability and their relationship to other climate proxies. This coast has a small tidal range, improving the precision of sea-level reconstructions. Coastal subsidence (from glacial isostatic adjustment, GIA) creates accommodation space that is filled by salt-marsh peat and preserves accurate and precise sea-level indicators and abundant material for radiocarbon dating. In addition to longer term GIA induced land-level change from ongoing collapse of the Laurentide forebulge, these records are ideally situated to capture climate-driven sea level changes. The western North Atlantic Ocean sea level is sensitive to static equilibrium effects from melting of the Greenland Ice Sheet, as well as large-scale changes in ocean circulation and winds. Our reconstructions reveal two distinct patterns in sea-level during the CE along the United States Atlantic coast: (1) South of Cape Hatteras, North Carolina, to Florida sea-level rise is essentially flat, with the record dominated by long-term geological processes until the onset of historic rates of rise in the late 19th century; (2) North of Cape Hatteras to Connecticut, sea level rise to maximum around 1000CE, a sea-level minimum around 1500 CE, and a long-term sea-level rise through the second half of the second millennium. The northern-intensified sea-level fall beginning 1000 is coincident with shifts toward persistent positive NAO-like atmospheric states inferred from other proxy records and is consistent with climate model simulations forced with sustained NAO-like heat fluxes. Changes in the wind-driven ocean circulation may also contribute to alongshore sea level variability over the CE. To reveal global mean sea level variability, we combine the salt-marsh data from North American Atlantic coast with tide-gauge records and other high resolution proxies from the northern and southern hemispheres. All reconstructions are from coasts that are tectonically stable and are based on four types of proxy archives (archaeological indicators, coral microatolls, salt marsh sediments and vermetid [mollusk] bioconstructions) that are best capable of capturing submeter-scale RSL changes. The database consists of reconstructions from Australasia (n = 2), Europe (n=5), Greenland (n = 3), North America (n = 6), the northern Gulf of Mexico (n = 3), the Mediterranean (n = 1), South Africa (n = 2), South America (n =2) and the South Pacific (n =3). We apply a noisy-input Gaussian process spatio-temporal modeling framework, which identifies a long-term falling global mean sea-level, interrupted in the middle of the 19th century by an acceleration yielding a 20th century rate of rise extremely likely (probability P = 0:95) faster than any previous century in the CE.

Understanding the weather signal in national crop-yield variability

NASA Astrophysics Data System (ADS)

Frieler, Katja; Schauberger, Bernhard; Arneth, Almut; Balkovič, Juraj; Chryssanthacopoulos, James; Deryng, Delphine; Elliott, Joshua; Folberth, Christian; Khabarov, Nikolay; Müller, Christoph; Olin, Stefan; Pugh, Thomas A. M.; Schaphoff, Sibyll; Schewe, Jacob; Schmid, Erwin; Warszawski, Lila; Levermann, Anders

2017-06-01

Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the United States. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also provide options to represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.

Examining for any impact of climate change on the association between seasonality and hospitalization for mania.

PubMed

Parker, Gordon B; Hadzi-Pavlovic, Dusan; Graham, Rebecca K

2017-01-15

Studies have established higher rates of hospitalization for mania in spring and summer and posit various explanatory climatic variables. As the earth's climate is changing, we pursue whether this is reflected in the yearly seasonal variation in hospitalizations for mania. This would be indicated by the presence of secular changes in both the hospitalization seasonal pattern and climatic variables, and associations between both variable sets. Data were obtained for 21,882 individuals hospitalized to psychiatric hospitals in the Australian state of New South Wales (NSW) over a 14-year period (2000-2014) with ICD-diagnosed mania - and with NSW population figures and salient climatic variables collected for the same period. Regression analyses were conducted to examine the predictive value of climate variables on hospital admissions. Data quantified a peak for manic admissions in spring of the southern hemisphere, in the months of October and November. There was a significant linear increase in manic admissions (0.5%/year) over the 14-year time period, with significant variation across years. In terms of climatic variables, there was a significant linear trend over the interval for solar radiation, although the trend indicated a decrease rather than an increase. Seasonal variation in admissions was most closely associated with two climate variables - evaporation in the current month and temperature in the previous month. Hospitalization rates do not necessarily provide an accurate estimate of the onset of manic episodes and findings may be limited to the southern hemisphere, or New South Wales. While overall findings do not support the hypothesis that climate change is leading to a higher seasonal impact for manic hospital admissions in the southern hemisphere, analyses identified two climate/weather variables - evaporation and temperature - that may account for the yearly spring excess. Copyright © 2016 Elsevier B.V. All rights reserved.

Hydrological resiliency in the Western Boreal Plains: classification of hydrological responses using wavelet analysis to assess landscape resilience

NASA Astrophysics Data System (ADS)

Probert, Samantha; Kettridge, Nicholas; Devito, Kevin; Hannah, David; Parkin, Geoff

2017-04-01

The Boreal represents a system of substantial resilience to climate change, with minimal ecological change over the past 6000 years. However, unprecedented climatic warming, coupled with catchment disturbances could exceed thresholds of hydrological function in the Western Boreal Plains. Knowledge of ecohydrological and climatic feedbacks that shape the resilience of boreal forests has advanced significantly in recent years, but this knowledge is yet to be applied and understood at landscape scales. Hydrological modelling at the landscape scale is challenging in the WBP due to diverse, non-topographically driven hydrology across the mosaic of terrestrial and aquatic ecosystems. This study functionally divides the geologic and ecological components of the landscape into Hydrologic Response Areas (HRAs) and wetland, forestland, interface and pond Hydrologic Units (HUs) to accurately characterise water storage and infer transmission at multiple spatial and temporal scales. Wavelet analysis is applied to pond and groundwater levels to describe the patterns of water storage in response to climate signals; to isolate dominant controls on hydrological responses and to assess the relative importance of physical controls between wet and dry climates. This identifies which components of the landscape exhibit greater magnitude and frequency of variability to wetting and drying trends, further to testing the hierarchical framework for hydrological storage controls of: climate, bedrock geology, surficial geology, soil, vegetation, and topography. Classifying HRA and HU hydrological function is essential to understand and predict water storage and redistribution through drought cycles and wet periods. This work recognises which landscape components are most sensitive under climate change and disturbance and also creates scope for hydrological resiliency research in Boreal systems by recognising critical landscape components and their role in landscape collapse or catastrophic shift in ecosystem function under future climatic scenarios.

Impacts of uncertainties in European gridded precipitation observations on regional climate analysis.

PubMed

Prein, Andreas F; Gobiet, Andreas

2017-01-01

Gridded precipitation data sets are frequently used to evaluate climate models or to remove model output biases. Although precipitation data are error prone due to the high spatio-temporal variability of precipitation and due to considerable measurement errors, relatively few attempts have been made to account for observational uncertainty in model evaluation or in bias correction studies. In this study, we compare three types of European daily data sets featuring two Pan-European data sets and a set that combines eight very high-resolution station-based regional data sets. Furthermore, we investigate seven widely used, larger scale global data sets. Our results demonstrate that the differences between these data sets have the same magnitude as precipitation errors found in regional climate models. Therefore, including observational uncertainties is essential for climate studies, climate model evaluation, and statistical post-processing. Following our results, we suggest the following guidelines for regional precipitation assessments. (1) Include multiple observational data sets from different sources (e.g. station, satellite, reanalysis based) to estimate observational uncertainties. (2) Use data sets with high station densities to minimize the effect of precipitation undersampling (may induce about 60% error in data sparse regions). The information content of a gridded data set is mainly related to its underlying station density and not to its grid spacing. (3) Consider undercatch errors of up to 80% in high latitudes and mountainous regions. (4) Analyses of small-scale features and extremes are especially uncertain in gridded data sets. For higher confidence, use climate-mean and larger scale statistics. In conclusion, neglecting observational uncertainties potentially misguides climate model development and can severely affect the results of climate change impact assessments.

Ice sheet climate modeling: past achievements, ongoing challenges, and future endeavors

NASA Astrophysics Data System (ADS)

Lenaerts, J.

2017-12-01

Fluctuations in surface mass balance (SMB) mask out a substantial portion of contemporary Greenland and Antarctic ice sheet mass loss. That implies that we need accurate, consistent, and long-term SMB time series to isolate the mass loss signal. This in turn requires understanding of the processes driving SMB, and how they interplay. The primary controls on present-day ice sheet SMB are snowfall, which is regulated by large-scale atmospheric variability, and surface meltwater production at the ice sheet's edges, which is a complex result of atmosphere-surface interactions. Additionally, wind-driven snow redistribution and sublimation are large SMB contributors on the downslope areas of the ice sheets. Climate models provide an integrated framework to simulate all these individual ice sheet components. Recent developments in RACMO2, a regional climate model bound by atmospheric reanalyses, have focused on enhancing horizontal resolution, including blowing snow, snow albedo, and meltwater processes. Including these physics not only enhanced our understanding of the ice sheet climate system, but also enabled to obtain increasingly accurate estimates of ice sheet SMB. However, regional models are not suitable to capture the mutual interactions between ice sheet and the remainder of the global climate system in transient climates. To take that next step, global climate models are essential. In this talk, I will highlight our present work on improving ice sheet climate in the Community Earth System Model (CESM). In particular, we focus on an improved representation of polar firn, ice sheet clouds, and precipitation. For this exercise, we extensively use field observations, remote sensing data, as well as RACMO2. Next, I will highlight how CESM is used to enhance our understanding of ice sheet SMB, its drivers, and past and present changes.

Adaptations to climate-mediated selective pressures in sheep.

PubMed

Lv, Feng-Hua; Agha, Saif; Kantanen, Juha; Colli, Licia; Stucki, Sylvie; Kijas, James W; Joost, Stéphane; Li, Meng-Hua; Ajmone Marsan, Paolo

2014-12-01

Following domestication, sheep (Ovis aries) have become essential farmed animals across the world through adaptation to a diverse range of environments and varied production systems. Climate-mediated selective pressure has shaped phenotypic variation and has left genetic "footprints" in the genome of breeds raised in different agroecological zones. Unlike numerous studies that have searched for evidence of selection using only population genetics data, here, we conducted an integrated coanalysis of environmental data with single nucleotide polymorphism (SNP) variation. By examining 49,034 SNPs from 32 old, autochthonous sheep breeds that are adapted to a spectrum of different regional climates, we identified 230 SNPs with evidence for selection that is likely due to climate-mediated pressure. Among them, 189 (82%) showed significant correlation (P ≤ 0.05) between allele frequency and climatic variables in a larger set of native populations from a worldwide range of geographic areas and climates. Gene ontology analysis of genes colocated with significant SNPs identified 17 candidates related to GTPase regulator and peptide receptor activities in the biological processes of energy metabolism and endocrine and autoimmune regulation. We also observed high linkage disequilibrium and significant extended haplotype homozygosity for the core haplotype TBC1D12-CH1 of TBC1D12. The global frequency distribution of the core haplotype and allele OAR22_18929579-A showed an apparent geographic pattern and significant (P ≤ 0.05) correlations with climatic variation. Our results imply that adaptations to local climates have shaped the spatial distribution of some variants that are candidates to underpin adaptive variation in sheep. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Quantitative approaches in climate change ecology

PubMed Central

Brown, Christopher J; Schoeman, David S; Sydeman, William J; Brander, Keith; Buckley, Lauren B; Burrows, Michael; Duarte, Carlos M; Moore, Pippa J; Pandolfi, John M; Poloczanska, Elvira; Venables, William; Richardson, Anthony J

2011-01-01

Contemporary impacts of anthropogenic climate change on ecosystems are increasingly being recognized. Documenting the extent of these impacts requires quantitative tools for analyses of ecological observations to distinguish climate impacts in noisy data and to understand interactions between climate variability and other drivers of change. To assist the development of reliable statistical approaches, we review the marine climate change literature and provide suggestions for quantitative approaches in climate change ecology. We compiled 267 peer-reviewed articles that examined relationships between climate change and marine ecological variables. Of the articles with time series data (n = 186), 75% used statistics to test for a dependency of ecological variables on climate variables. We identified several common weaknesses in statistical approaches, including marginalizing other important non-climate drivers of change, ignoring temporal and spatial autocorrelation, averaging across spatial patterns and not reporting key metrics. We provide a list of issues that need to be addressed to make inferences more defensible, including the consideration of (i) data limitations and the comparability of data sets; (ii) alternative mechanisms for change; (iii) appropriate response variables; (iv) a suitable model for the process under study; (v) temporal autocorrelation; (vi) spatial autocorrelation and patterns; and (vii) the reporting of rates of change. While the focus of our review was marine studies, these suggestions are equally applicable to terrestrial studies. Consideration of these suggestions will help advance global knowledge of climate impacts and understanding of the processes driving ecological change.

A method for screening climate change-sensitive infectious diseases.

PubMed

Wang, Yunjing; Rao, Yuhan; Wu, Xiaoxu; Zhao, Hainan; Chen, Jin

2015-01-14

Climate change is a significant and emerging threat to human health, especially where infectious diseases are involved. Because of the complex interactions between climate variables and infectious disease components (i.e., pathogen, host and transmission environment), systematically and quantitatively screening for infectious diseases that are sensitive to climate change is still a challenge. To address this challenge, we propose a new statistical indicator, Relative Sensitivity, to identify the difference between the sensitivity of the infectious disease to climate variables for two different climate statuses (i.e., historical climate and present climate) in non-exposure and exposure groups. The case study in Anhui Province, China has demonstrated the effectiveness of this Relative Sensitivity indicator. The application results indicate significant sensitivity of many epidemic infectious diseases to climate change in the form of changing climatic variables, such as temperature, precipitation and absolute humidity. As novel evidence, this research shows that absolute humidity has a critical influence on many observed infectious diseases in Anhui Province, including dysentery, hand, foot and mouth disease, hepatitis A, hemorrhagic fever, typhoid fever, malaria, meningitis, influenza and schistosomiasis. Moreover, some infectious diseases are more sensitive to climate change in rural areas than in urban areas. This insight provides guidance for future health inputs that consider spatial variability in response to climate change.

A Method for Screening Climate Change-Sensitive Infectious Diseases

PubMed Central

Wang, Yunjing; Rao, Yuhan; Wu, Xiaoxu; Zhao, Hainan; Chen, Jin

2015-01-01

Climate change is a significant and emerging threat to human health, especially where infectious diseases are involved. Because of the complex interactions between climate variables and infectious disease components (i.e., pathogen, host and transmission environment), systematically and quantitatively screening for infectious diseases that are sensitive to climate change is still a challenge. To address this challenge, we propose a new statistical indicator, Relative Sensitivity, to identify the difference between the sensitivity of the infectious disease to climate variables for two different climate statuses (i.e., historical climate and present climate) in non-exposure and exposure groups. The case study in Anhui Province, China has demonstrated the effectiveness of this Relative Sensitivity indicator. The application results indicate significant sensitivity of many epidemic infectious diseases to climate change in the form of changing climatic variables, such as temperature, precipitation and absolute humidity. As novel evidence, this research shows that absolute humidity has a critical influence on many observed infectious diseases in Anhui Province, including dysentery, hand, foot and mouth disease, hepatitis A, hemorrhagic fever, typhoid fever, malaria, meningitis, influenza and schistosomiasis. Moreover, some infectious diseases are more sensitive to climate change in rural areas than in urban areas. This insight provides guidance for future health inputs that consider spatial variability in response to climate change. PMID:25594780

Multi-objective optimization for evaluation of simulation fidelity for precipitation, cloudiness and insolation in regional climate models

NASA Astrophysics Data System (ADS)

Lee, H.

2016-12-01

Precipitation is one of the most important climate variables that are taken into account in studying regional climate. Nevertheless, how precipitation will respond to a changing climate and even its mean state in the current climate are not well represented in regional climate models (RCMs). Hence, comprehensive and mathematically rigorous methodologies to evaluate precipitation and related variables in multiple RCMs are required. The main objective of the current study is to evaluate the joint variability of climate variables related to model performance in simulating precipitation and condense multiple evaluation metrics into a single summary score. We use multi-objective optimization, a mathematical process that provides a set of optimal tradeoff solutions based on a range of evaluation metrics, to characterize the joint representation of precipitation, cloudiness and insolation in RCMs participating in the North American Regional Climate Change Assessment Program (NARCCAP) and Coordinated Regional Climate Downscaling Experiment-North America (CORDEX-NA). We also leverage ground observations, NASA satellite data and the Regional Climate Model Evaluation System (RCMES). Overall, the quantitative comparison of joint probability density functions between the three variables indicates that performance of each model differs markedly between sub-regions and also shows strong seasonal dependence. Because of the large variability across the models, it is important to evaluate models systematically and make future projections using only models showing relatively good performance. Our results indicate that the optimized multi-model ensemble always shows better performance than the arithmetic ensemble mean and may guide reliable future projections.

The response of the southwest Western Australian wave climate to Indian Ocean climate variability

NASA Astrophysics Data System (ADS)

Wandres, Moritz; Pattiaratchi, Charitha; Hetzel, Yasha; Wijeratne, E. M. S.

2018-03-01

Knowledge of regional wave climates is critical for coastal planning, management, and protection. In order to develop a regional wave climate, it is important to understand the atmospheric systems responsible for wave generation. This study examines the variability of the southwest Western Australian (SWWA) shelf and nearshore wind wave climate and its relationship to southern hemisphere climate variability represented by various atmospheric indices: the southern oscillation index (SOI), the Southern Annular Mode (SAM), the Indian Ocean Dipole Mode Index (DMI), the Indian Ocean Subtropical Dipole (IOSD), the latitudinal position of the subtropical high-pressure ridge (STRP), and the corresponding intensity of the subtropical ridge (STRI). A 21-year wave hindcast (1994-2014) of the SWWA continental shelf was created using the third generation wave model Simulating WAves Nearshore (SWAN), to analyse the seasonal and inter-annual wave climate variability and its relationship to the atmospheric regime. Strong relationships between wave heights and the STRP and the STRI, a moderate correlation between the wave climate and the SAM, and no significant correlation between SOI, DMI, and IOSD and the wave climate were found. Strong spatial, seasonal, and inter-annual variability, as well as seasonal longer-term trends in the mean wave climate were studied and linked to the latitudinal changes in the subtropical high-pressure ridge and the Southern Ocean storm belt. As the Southern Ocean storm belt and the subtropical high-pressure ridge shifted southward (northward) wave heights on the SWWA shelf region decreased (increased). The wave height anomalies appear to be driven by the same atmospheric conditions that influence rainfall variability in SWWA.

Association between climate variability and malaria epidemics in the East African highlands.

PubMed

Zhou, Guofa; Minakawa, Noboru; Githeko, Andrew K; Yan, Guiyun

2004-02-24

The causes of the recent reemergence of Plasmodium falciparum epidemic malaria in the East African highlands are controversial. Regional climate changes have been invoked as a major factor; however, assessing the impact of climate in malaria resurgence is difficult due to high spatial and temporal climate variability and the lack of long-term data series on malaria cases from different sites. Climate variability, defined as short-term fluctuations around the mean climate state, may be epidemiologically more relevant than mean temperature change, but its effects on malaria epidemics have not been rigorously examined. Here we used nonlinear mixed-regression model to investigate the association between autoregression (number of malaria outpatients during the previous time period), seasonality and climate variability, and the number of monthly malaria outpatients of the past 10-20 years in seven highland sites in East Africa. The model explained 65-81% of the variance in the number of monthly malaria outpatients. Nonlinear and synergistic effects of temperature and rainfall on the number of malaria outpatients were found in all seven sites. The net variance in the number of monthly malaria outpatients caused by autoregression and seasonality varied among sites and ranged from 18 to 63% (mean=38.6%), whereas 12-63% (mean=36.1%) of variance is attributed to climate variability. Our results suggest that there was a high spatial variation in the sensitivity of malaria outpatient number to climate fluctuations in the highlands, and that climate variability played an important role in initiating malaria epidemics in the East African highlands.

Including climate variability in determination of the optimum rate of N fertilizer application using a crop model: A case study for rainfed corn in eastern Canada

NASA Astrophysics Data System (ADS)

Mesbah, M.; Pattey, E.; Jégo, G.; Geng, X.; Tremblay, N.; Didier, A.

2017-12-01

Identifying optimum nitrogen (N) application rate is essential for increasing agricultural production while limiting potential environmental contaminations caused by release of reactive N, especially for high demand N crops such as corn. The central question of N management is then how the optimum N rate is affected by climate variability for given soil. The experimental determination of optimum N rates involve the analyses of variance on the mean value of crop yield response to various N application rates used by factorial plot based experiments for a few years in several regions. This traditional approach has limitations to capture 1) the non-linear response of yield to N application rates due to large incremental N rates (often more than 40 kg N ha-1) and 2) the ecophysiological response of the crop to climate variability because of limited numbers of growing seasons considered. Modeling on the other hand, does not have such limitations and hence we use a crop model and propose a model-based methodology called Finding NEMO (N Ecophysiologically Modelled Optimum) to identify the optimum N rates for variable agro-climatic conditions and given soil properties. The performance of the methodology is illustrated using the STICS crop model adapted for rainfed corn in the Mixedwood Plains ecozone of eastern Canada (42.3oN 83oW-46.8oN 71oW) where more than 90% of Canadian corn is produced. The simulations were performed using small increment of preplant N application rate (10 kg N ha -1), long time series of daily climatic data (48 to 61 years) for 5 regions along the ecozone, and three contrasting soils per region. The results show that N recommendations should be region and soil specific. Soils with lower available water capacity required more N compared to soil with higher available water capacity. When N rates were at their ecophysiologically optimum level, 10 to 17 kg increase in dry yield could be achieved by adding 1 kg N. Expected yield also affected the optimum N rates for the region and soil. For instance, the probability to achieve a yield of 9.2 t ha-1 at 15% grain moisture on a loamy soil varied from 0 to 73% along the ecozone. For this level of expected yield, the recommended N rates ranged from 64 to 155 kg ha-1, which are relatively less than current provincial recommendations in Ontario and Quebec (120-170 kg ha-1).

CLIMATE CHANGE IN THE UPPER GREAT LAKES REGION: A WORKSHOP REPORT

EPA Science Inventory

This paper, "Coping With Climate Change", argues that adaptation is an important strategy for protecting human health, ecosystems, and economic activity as the climate changes. Adaptation is an essential component of any portfolio of actions that comprise U.S. climate change poli...

Climate variations of Central Asia on orbital to millennial timescales.

PubMed

Cheng, Hai; Spötl, Christoph; Breitenbach, Sebastian F M; Sinha, Ashish; Wassenburg, Jasper A; Jochum, Klaus Peter; Scholz, Denis; Li, Xianglei; Yi, Liang; Peng, Youbing; Lv, Yanbin; Zhang, Pingzhong; Votintseva, Antonina; Loginov, Vadim; Ning, Youfeng; Kathayat, Gayatri; Edwards, R Lawrence

2016-11-11

The extent to which climate variability in Central Asia is causally linked to large-scale changes in the Asian monsoon on varying timescales remains a longstanding question. Here we present precisely dated high-resolution speleothem oxygen-carbon isotope and trace element records of Central Asia's hydroclimate variability from Tonnel'naya cave, Uzbekistan, and Kesang cave, western China. On orbital timescales, the supra-regional climate variance, inferred from our oxygen isotope records, exhibits a precessional rhythm, punctuated by millennial-scale abrupt climate events, suggesting a close coupling with the Asian monsoon. However, the local hydroclimatic variability at both cave sites, inferred from carbon isotope and trace element records, shows climate variations that are distinctly different from their supra-regional modes. Particularly, hydroclimatic changes in both Tonnel'naya and Kesang areas during the Holocene lag behind the supra-regional climate variability by several thousand years. These observations may reconcile the apparent out-of-phase hydroclimatic variability, inferred from the Holocene lake proxy records, between Westerly Central Asia and Monsoon Asia.

Climate variations of Central Asia on orbital to millennial timescales

PubMed Central

Cheng, Hai; Spötl, Christoph; Breitenbach, Sebastian F. M.; Sinha, Ashish; Wassenburg, Jasper A.; Jochum, Klaus Peter; Scholz, Denis; Li, Xianglei; Yi, Liang; Peng, Youbing; Lv, Yanbin; Zhang, Pingzhong; Votintseva, Antonina; Loginov, Vadim; Ning, Youfeng; Kathayat, Gayatri; Edwards, R. Lawrence

2016-01-01

The extent to which climate variability in Central Asia is causally linked to large-scale changes in the Asian monsoon on varying timescales remains a longstanding question. Here we present precisely dated high-resolution speleothem oxygen-carbon isotope and trace element records of Central Asia’s hydroclimate variability from Tonnel’naya cave, Uzbekistan, and Kesang cave, western China. On orbital timescales, the supra-regional climate variance, inferred from our oxygen isotope records, exhibits a precessional rhythm, punctuated by millennial-scale abrupt climate events, suggesting a close coupling with the Asian monsoon. However, the local hydroclimatic variability at both cave sites, inferred from carbon isotope and trace element records, shows climate variations that are distinctly different from their supra-regional modes. Particularly, hydroclimatic changes in both Tonnel’naya and Kesang areas during the Holocene lag behind the supra-regional climate variability by several thousand years. These observations may reconcile the apparent out-of-phase hydroclimatic variability, inferred from the Holocene lake proxy records, between Westerly Central Asia and Monsoon Asia. PMID:27833133

Screening variability and change of soil moisture under wide-ranging climate conditions: Snow dynamics effects.

PubMed

Verrot, Lucile; Destouni, Georgia

2015-01-01

Soil moisture influences and is influenced by water, climate, and ecosystem conditions, affecting associated ecosystem services in the landscape. This paper couples snow storage-melting dynamics with an analytical modeling approach to screening basin-scale, long-term soil moisture variability and change in a changing climate. This coupling enables assessment of both spatial differences and temporal changes across a wide range of hydro-climatic conditions. Model application is exemplified for two major Swedish hydrological basins, Norrström and Piteälven. These are located along a steep temperature gradient and have experienced different hydro-climatic changes over the time period of study, 1950-2009. Spatially, average intra-annual variability of soil moisture differs considerably between the basins due to their temperature-related differences in snow dynamics. With regard to temporal change, the long-term average state and intra-annual variability of soil moisture have not changed much, while inter-annual variability has changed considerably in response to hydro-climatic changes experienced so far in each basin.

Human Responses to Climate Variability: The Case of South Africa

NASA Astrophysics Data System (ADS)

Oppenheimer, M.; Licker, R.; Mastrorillo, M.; Bohra-Mishra, P.; Estes, L. D.; Cai, R.

2014-12-01

Climate variability has been associated with a range of societal and individual outcomes including migration, violent conflict, changes in labor productivity, and health impacts. Some of these may be direct responses to changes in mean temperature or precipitation or extreme events, such as displacement of human populations by tropical cyclones. Others may be mediated by a variety of biological, social, or ecological factors such as migration in response to long-term changes in crops yields. Research is beginning to elucidate and distinguish the many channels through which climate variability may influence human behavior (ranging from the individual to the collective, societal level) in order to better understand how to improve resilience in the face of current variability as well as future climate change. Using a variety of data sets from South Africa, we show how climate variability has influenced internal (within country) migration in recent history. We focus on South Africa as it is a country with high levels of internal migration and dramatic temperature and precipitation changes projected for the 21st century. High poverty rates and significant levels of rain-fed, smallholder agriculture leave large portions of South Africa's population base vulnerable to future climate change. In this study, we utilize two complementary statistical models - one micro-level model, driven by individual and household level survey data, and one macro-level model, driven by national census statistics. In both models, we consider the effect of climate on migration both directly (with gridded climate reanalysis data) and indirectly (with agricultural production statistics). With our historical analyses of climate variability, we gain insights into how the migration decisions of South Africans may be influenced by future climate change. We also offer perspective on the utility of micro and macro level approaches in the study of climate change and human migration.

Vulnerability of breeding waterbirds to climate change in the Prairie Pothole Region, U.S.A.

PubMed

Steen, Valerie; Skagen, Susan K; Noon, Barry R

2014-01-01

The Prairie Pothole Region (PPR) of the north-central U.S. and south-central Canada contains millions of small prairie wetlands that provide critical habitat to many migrating and breeding waterbirds. Due to their small size and the relatively dry climate of the region, these wetlands are considered at high risk for negative climate change effects as temperatures increase. To estimate the potential impacts of climate change on breeding waterbirds, we predicted current and future distributions of species common in the PPR using species distribution models (SDMs). We created regional-scale SDMs for the U.S. PPR using Breeding Bird Survey occurrence records for 1971-2011 and wetland, upland, and climate variables. For each species, we predicted current distribution based on climate records for 1981-2000 and projected future distributions to climate scenarios for 2040-2049. Species were projected to, on average, lose almost half their current habitat (-46%). However, individual species projections varied widely, from +8% (Upland Sandpiper) to -100% (Wilson's Snipe). Variable importance ranks indicated that land cover (wetland and upland) variables were generally more important than climate variables in predicting species distributions. However, climate variables were relatively more important during a drought period. Projected distributions of species responses to climate change contracted within current areas of distribution rather than shifting. Given the large variation in species-level impacts, we suggest that climate change mitigation efforts focus on species projected to be the most vulnerable by enacting targeted wetland management, easement acquisition, and restoration efforts.

Climate controls the distribution of a widespread invasive species: Implications for future range expansion

USGS Publications Warehouse

McDowell, W.G.; Benson, A.J.; Byers, J.E.

2014-01-01

1. Two dominant drivers of species distributions are climate and habitat, both of which are changing rapidly. Understanding the relative importance of variables that can control distributions is critical, especially for invasive species that may spread rapidly and have strong effects on ecosystems. 2. Here, we examine the relative importance of climate and habitat variables in controlling the distribution of the widespread invasive freshwater clam Corbicula fluminea, and we model its future distribution under a suite of climate scenarios using logistic regression and maximum entropy modelling (MaxEnt). 3. Logistic regression identified climate variables as more important than habitat variables in controlling Corbicula distribution. MaxEnt modelling predicted Corbicula's range expansion westward and northward to occupy half of the contiguous United States. By 2080, Corbicula's potential range will expand 25–32%, with more than half of the continental United States being climatically suitable. 4. Our combination of multiple approaches has revealed the importance of climate over habitat in controlling Corbicula's distribution and validates the climate-only MaxEnt model, which can readily examine the consequences of future climate projections. 5. Given the strong influence of climate variables on Corbicula's distribution, as well as Corbicula's ability to disperse quickly and over long distances, Corbicula is poised to expand into New England and the northern Midwest of the United States. Thus, the direct effects of climate change will probably be compounded by the addition of Corbicula and its own influences on ecosystem function.

Development, malaria and adaptation to climate change: a case study from India.

PubMed

Garg, Amit; Dhiman, R C; Bhattacharya, Sumana; Shukla, P R

2009-05-01

India has reasons to be concerned about climate change. Over 650 million people depend on climate-sensitive sectors, such as rain-fed agriculture and forestry, for livelihood and over 973 million people are exposed to vector borne malarial parasites. Projection of climatic factors indicates a wider exposure to malaria for the Indian population in the future. If precautionary measures are not taken and development processes are not managed properly some developmental activities, such as hydro-electric dams and irrigation canal systems, may also exacerbate breeding grounds for malaria. This article integrates climate change and developmental variables in articulating a framework for integrated impact assessment and adaptation responses, with malaria incidence in India as a case study. The climate change variables include temperature, rainfall, humidity, extreme events, and other secondary variables. Development variables are income levels, institutional mechanisms to implement preventive measures, infrastructure development that could promote malarial breeding grounds, and other policies. The case study indicates that sustainable development variables may sometimes reduce the adverse impacts on the system due to climate change alone, while it may sometimes also exacerbate these impacts if the development variables are not managed well and therefore they produce a negative impact on the system. The study concludes that well crafted and well managed developmental policies could result in enhanced resilience of communities and systems, and lower health impacts due to climate change.

Development, Malaria and Adaptation to Climate Change: A Case Study from India

NASA Astrophysics Data System (ADS)

Garg, Amit; Dhiman, R. C.; Bhattacharya, Sumana; Shukla, P. R.

2009-05-01

India has reasons to be concerned about climate change. Over 650 million people depend on climate-sensitive sectors, such as rain-fed agriculture and forestry, for livelihood and over 973 million people are exposed to vector borne malarial parasites. Projection of climatic factors indicates a wider exposure to malaria for the Indian population in the future. If precautionary measures are not taken and development processes are not managed properly some developmental activities, such as hydro-electric dams and irrigation canal systems, may also exacerbate breeding grounds for malaria. This article integrates climate change and developmental variables in articulating a framework for integrated impact assessment and adaptation responses, with malaria incidence in India as a case study. The climate change variables include temperature, rainfall, humidity, extreme events, and other secondary variables. Development variables are income levels, institutional mechanisms to implement preventive measures, infrastructure development that could promote malarial breeding grounds, and other policies. The case study indicates that sustainable development variables may sometimes reduce the adverse impacts on the system due to climate change alone, while it may sometimes also exacerbate these impacts if the development variables are not managed well and therefore they produce a negative impact on the system. The study concludes that well crafted and well managed developmental policies could result in enhanced resilience of communities and systems, and lower health impacts due to climate change.

Holocene climate variability in Texas, USA: An integration of existing paleoclimate data and modeling with a new, high-resolution speleothem record

NASA Astrophysics Data System (ADS)

Wong, Corinne I.; Banner, Jay L.; Musgrove, MaryLynn

2015-11-01

Delineating the climate processes governing precipitation variability in drought-prone Texas is critical for predicting and mitigating climate change effects, and requires the reconstruction of past climate beyond the instrumental record. We synthesize existing paleoclimate proxy data and climate simulations to provide an overview of climate variability in Texas during the Holocene. Conditions became progressively warmer and drier transitioning from the early to mid Holocene, culminating between 7 and 3 ka (thousand years ago), and were more variable during the late Holocene. The timing and relative magnitude of Holocene climate variability, however, is poorly constrained owing to considerable variability among the different records. To help address this, we present a new speleothem (NBJ) reconstruction from a central Texas cave that comprises the highest resolution proxy record to date, spanning the mid to late Holocene. NBJ trace-element concentrations indicate variable moisture conditions with no clear temporal trend. There is a decoupling between NBJ growth rate, trace-element concentrations, and δ18O values, which indicate that (i) the often direct relation between speleothem growth rate and moisture availability is likely complicated by changes in the overlying ecosystem that affect subsurface CO2 production, and (ii) speleothem δ18O variations likely reflect changes in moisture source (i.e., proportion of Pacific-vs. Gulf of Mexico-derived moisture) that appear not to be linked to moisture amount.

Climate Variability and Ecosystem Response

Treesearch

David Greenland; Lloyd W. Swift; [Editors

1990-01-01

Nine papers describe studies of climate variability and ecosystem response. The studies were conducted at LTER (Long-Term Ecological Research) sites representing forest, agricultural, and aquatic ecosystems and systems in which extreme climates limit vegetational cover. An overview paper prepared by the LTER Climate Committee stresses the importance of (1) clear...

Application of Climate Assessment Tool (CAT) to estimate climate variability impacts on nutrient loading from local watersheds

Treesearch

Ying Ouyang; Prem B. Parajuli; Gary Feng; Theodor D. Leininger; Yongshan Wan; Padmanava Dash

2018-01-01

A vast amount of future climate scenario datasets, created by climate models such as general circulation models (GCMs), have been used in conjunction with watershed models to project future climate variability impact on hydrological processes and water quality. However, these low spatial-temporal resolution datasets are often difficult to downscale spatially and...

AMOC decadal variability in Earth system models: Mechanisms and climate impacts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fedorov, Alexey

This is the final report for the project titled "AMOC decadal variability in Earth system models: Mechanisms and climate impacts". The central goal of this one-year research project was to understand the mechanisms of decadal and multi-decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) within a hierarchy of climate models ranging from realistic ocean GCMs to Earth system models. The AMOC is a key element of ocean circulation responsible for oceanic transport of heat from low to high latitudes and controlling, to a large extent, climate variations in the North Atlantic. The questions of the AMOC stability, variability andmore » predictability, directly relevant to the questions of climate predictability, were at the center of the research work.« less

Change in the magnitude and mechanisms of global temperature variability with warming

PubMed Central

Brown, Patrick T.; Ming, Yi; Li, Wenhong; Hill, Spencer A.

2017-01-01

Natural unforced variability in global mean surface air temperature (GMST) can mask or exaggerate human-caused global warming, and thus a complete understanding of this variability is highly desirable. Significant progress has been made in elucidating the magnitude and physical origins of present-day unforced GMST variability, but it has remained unclear how such variability may change as the climate warms. Here we present modeling evidence that indicates that the magnitude of low-frequency GMST variability is likely to decline in a warmer climate and that its generating mechanisms may be fundamentally altered. In particular, a warmer climate results in lower albedo at high latitudes, which yields a weaker albedo feedback on unforced GMST variability. These results imply that unforced GMST variability is dependent on the background climatological conditions, and thus climate model control simulations run under perpetual preindustrial conditions may have only limited relevance for understanding the unforced GMST variability of the future. PMID:29391875

Centennial-scale Holocene climate variations amplified by Antarctic Ice Sheet discharge

NASA Astrophysics Data System (ADS)

Bakker, Pepijn; Clark, Peter U.; Golledge, Nicholas R.; Schmittner, Andreas; Weber, Michael E.

2017-01-01

Proxy-based indicators of past climate change show that current global climate models systematically underestimate Holocene-epoch climate variability on centennial to multi-millennial timescales, with the mismatch increasing for longer periods. Proposed explanations for the discrepancy include ocean-atmosphere coupling that is too weak in models, insufficient energy cascades from smaller to larger spatial and temporal scales, or that global climate models do not consider slow climate feedbacks related to the carbon cycle or interactions between ice sheets and climate. Such interactions, however, are known to have strongly affected centennial- to orbital-scale climate variability during past glaciations, and are likely to be important in future climate change. Here we show that fluctuations in Antarctic Ice Sheet discharge caused by relatively small changes in subsurface ocean temperature can amplify multi-centennial climate variability regionally and globally, suggesting that a dynamic Antarctic Ice Sheet may have driven climate fluctuations during the Holocene. We analysed high-temporal-resolution records of iceberg-rafted debris derived from the Antarctic Ice Sheet, and performed both high-spatial-resolution ice-sheet modelling of the Antarctic Ice Sheet and multi-millennial global climate model simulations. Ice-sheet responses to decadal-scale ocean forcing appear to be less important, possibly indicating that the future response of the Antarctic Ice Sheet will be governed more by long-term anthropogenic warming combined with multi-centennial natural variability than by annual or decadal climate oscillations.

Exploring the impact of climate variability during the Last Glacial Maximum on the pattern of human occupation of Iberia.

PubMed

Burke, Ariane; Levavasseur, Guillaume; James, Patrick M A; Guiducci, Dario; Izquierdo, Manuel Arturo; Bourgeon, Lauriane; Kageyama, Masa; Ramstein, Gilles; Vrac, Mathieu

2014-08-01

The Last Glacial Maximum (LGM) was a global climate event, which had significant repercussions for the spatial distribution and demographic history of prehistoric populations. In Eurasia, the LGM coincides with a potential bottleneck for modern humans and may mark the divergence date for Asian and European populations (Keinan et al., 2007). In this research, the impact of climate variability on human populations in the Iberian Peninsula during the Last Glacial Maximum (LGM) is examined with the aid of downscaled high-resolution (16 × 16 km) numerical climate experiments. Human sensitivity to short time-scale (inter-annual) climate variability during this key time period, which follows the initial modern human colonisation of Eurasia and the extinction of the Neanderthals, is tested using the spatial distribution of archaeological sites. Results indicate that anatomically modern human populations responded to small-scale spatial patterning in climate variability, specifically inter-annual variability in precipitation levels as measured by the standard precipitation index. Climate variability at less than millennial scale, therefore, is shown to be an important component of ecological risk, one that played a role in regulating the spatial behaviour of prehistoric human populations and consequently affected their social networks. Copyright © 2014 Elsevier Ltd. All rights reserved.

Communicating climate information to end-users: an experience driven by the understanding and anticipation of user needs.

NASA Astrophysics Data System (ADS)

Chaumont, Diane; Huard, David; Logan, Travis; Sottile, Marie-France; Brown, Ross; Gauvin St-Denis, Blaise; Grenier, Patrick; Braun, Marco

2013-04-01

Planning and adapting to a changing climate requires credible information about the magnitude and rate of projected changes. Ouranos, a consortium on regional climatology and adaptation to climate change was launched in the Province of Québec, Canada, ten years ago, with the objective of developing and providing climate information and expertise in support to adaption. Ouranos differs from most other climate service centers by integrating climate modeling activities, impacts and adaptation expertise and climate analysis services under one roof. The Climate Scenarios Group operates at the interface between climate modellers and users and is responsible for developing, producing and communicating climate scenarios to end-users in a consistent manner. This process requires close collaboration with users to define, understand and eventually anticipate their needs. The varied scientific expertise of climate scenarios specialists --who also act as communicators-- has proven to be a key element for successful communication. A large amount of effort is spent on the characterization and communication of the uncertainties involved in scenario construction. Two main activities have been put in place by the experts in climate modeling to address this: (1) a training course on climate models and (2) a fact-sheet summarizing the uncertainty and robustness of the climate change scenario provided for each I&A application. The latter tool ensures the transparency, traceability, and accountability of our products, and at the same time, encourages a sense of shared responsibility for the final choice of climate scenarios. In addition to uncertainty, two other main issues have been identified as essential in communication with users: 1) observed natural variability at relevant scales and 2) reconciliation of the projected trend with the recent observed trend. Our group has devoted substantial resources for the advancement of communication with end-users in these particular areas. This presentation will provide an overview of progress in communicating climate information at the Ouranos Consortium. We will discuss success and failures and future plans, in particular the extent to which Ouranos needs to work with users in decision-making activities.

Climatic variability effects on summer cropping systems of the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Capa-Morocho, M.; Rodríguez-Fonseca, B.; Ruiz-Ramos, M.

2012-04-01

Climate variability and changes in the frequency of extremes events have a direct impact on crop yield and damages. Climate anomalies projections at monthly and yearly timescale allows us for adapting a cropping system (crops, varieties and management) to take advantage of favorable conditions or reduce the effect of adverse conditions. The objective of this work is to develop indices to evaluate the effect of climatic variability in summer cropping systems of Iberian Peninsula, in an attempt of relating yield variability to climate variability, extending the work of Rodríguez-Puebla (2004). This paper analyses the evolution of the yield anomalies of irrigated maize in several representative agricultural locations in Spain with contrasting temperature and precipitation regimes and compare it to the evolution of different patterns of climate variability, extending the methodology of Porter and Semenov (2005). To simulate maize yields observed daily data of radiation, maximum and minimum temperature and precipitation were used. These data were obtained from the State Meteorological Agency of Spain (AEMET). Time series of simulated maize yields were computed with CERES-maize model for periods ranging from 22 to 49 years, depending on the observed climate data available for each location. The computed standardized anomalies yields were projected on different oceanic and atmospheric anomalous fields and the resulting patterns were compared with a set of documented patterns from the National Oceanic and Atmospheric Administration (NOAA). The results can be useful also for climate change impact assessment, providing a scientific basis for selection of climate change scenarios where combined natural and forced variability represent a hazard for agricultural production. Interpretation of impact projections would also be enhanced.

The relative impacts of climate and land-use change on conterminous United States bird species from 2001 to 2075

USGS Publications Warehouse

Sohl, Terry L.

2014-01-01

Species distribution models often use climate data to assess contemporary and/or future ranges for animal or plant species. Land use and land cover (LULC) data are important predictor variables for determining species range, yet are rarely used when modeling future distributions. In this study, maximum entropy modeling was used to construct species distribution maps for 50 North American bird species to determine relative contributions of climate and LULC for contemporary (2001) and future (2075) time periods. Species presence data were used as a dependent variable, while climate, LULC, and topographic data were used as predictor variables. Results varied by species, but in general, measures of model fit for 2001 indicated significantly poorer fit when either climate or LULC data were excluded from model simulations. Climate covariates provided a higher contribution to 2001 model results than did LULC variables, although both categories of variables strongly contributed. The area deemed to be "suitable" for 2001 species presence was strongly affected by the choice of model covariates, with significantly larger ranges predicted when LULC was excluded as a covariate. Changes in species ranges for 2075 indicate much larger overall range changes due to projected climate change than due to projected LULC change. However, the choice of study area impacted results for both current and projected model applications, with truncation of actual species ranges resulting in lower model fit scores and increased difficulty in interpreting covariate impacts on species range. Results indicate species-specific response to climate and LULC variables; however, both climate and LULC variables clearly are important for modeling both contemporary and potential future species ranges.

The Relative Impacts of Climate and Land-Use Change on Conterminous United States Bird Species from 2001 to 2075

PubMed Central

Sohl, Terry L.

2014-01-01

Species distribution models often use climate data to assess contemporary and/or future ranges for animal or plant species. Land use and land cover (LULC) data are important predictor variables for determining species range, yet are rarely used when modeling future distributions. In this study, maximum entropy modeling was used to construct species distribution maps for 50 North American bird species to determine relative contributions of climate and LULC for contemporary (2001) and future (2075) time periods. Species presence data were used as a dependent variable, while climate, LULC, and topographic data were used as predictor variables. Results varied by species, but in general, measures of model fit for 2001 indicated significantly poorer fit when either climate or LULC data were excluded from model simulations. Climate covariates provided a higher contribution to 2001 model results than did LULC variables, although both categories of variables strongly contributed. The area deemed to be “suitable” for 2001 species presence was strongly affected by the choice of model covariates, with significantly larger ranges predicted when LULC was excluded as a covariate. Changes in species ranges for 2075 indicate much larger overall range changes due to projected climate change than due to projected LULC change. However, the choice of study area impacted results for both current and projected model applications, with truncation of actual species ranges resulting in lower model fit scores and increased difficulty in interpreting covariate impacts on species range. Results indicate species-specific response to climate and LULC variables; however, both climate and LULC variables clearly are important for modeling both contemporary and potential future species ranges. PMID:25372571

Semi-arid vegetation response to antecedent climate and water balance windows

USGS Publications Warehouse

Thoma, David P.; Munson, Seth M.; Irvine, Kathryn M.; Witwicki, Dana L.; Bunting, Erin

2016-01-01

Questions Can we improve understanding of vegetation response to water availability on monthly time scales in semi-arid environments using remote sensing methods? What climatic or water balance variables and antecedent windows of time associated with these variables best relate to the condition of vegetation? Can we develop credible near-term forecasts from climate data that can be used to prepare for future climate change effects on vegetation? Location Semi-arid grasslands in Capitol Reef National Park, Utah, USA. Methods We built vegetation response models by relating the normalized difference vegetation index (NDVI) from MODIS imagery in Mar–Nov 2000–2013 to antecedent climate and water balance variables preceding the monthly NDVI observations. We compared how climate and water balance variables explained vegetation greenness and then used a multi-model ensemble of climate and water balance models to forecast monthly NDVI for three holdout years. Results Water balance variables explained vegetation greenness to a greater degree than climate variables for most growing season months. Seasonally important variables included measures of antecedent water input and storage in spring, switching to indicators of drought, input or use in summer, followed by antecedent moisture availability in autumn. In spite of similar climates, there was evidence the grazed grassland showed a response to drying conditions 1 mo sooner than the ungrazed grassland. Lead times were generally short early in the growing season and antecedent window durations increased from 3 mo early in the growing season to 1 yr or more as the growing season progressed. Forecast accuracy for three holdout years using a multi-model ensemble of climate and water balance variables outperformed forecasts made with a naïve NDVI climatology. Conclusions We determined the influence of climate and water balance on vegetation at a fine temporal scale, which presents an opportunity to forecast vegetation response with short lead times. This understanding was obtained through high-frequency vegetation monitoring using remote sensing, which reduces the costs and time necessary for field measurements and can lead to more rapid detection of vegetation changes that could help managers take appropriate actions.

Natural variability of essential oil and antioxidants in the medicinal plant Turnera diffusa.

PubMed

Urbizu-González, Ana Lucía; Castillo-Ruiz, Octelina; Martínez-Ávila, Guillermo Cristian Guadalupe; Torres-Castillo, Jorge Ariel

2017-02-01

To evaluate differences in yield and composition of the essential oil and antioxidant contents in Turnera diffusa plants from localities in central region of Tamaulipas. Samples were collected in Tamaulipas, Mexico in the arid zone. Essential oil was obtained through steam distillation and analyzed using GC-MS. Polyphenol contents, antioxidant activities using ABTS and ferric reducing antioxidant power (FRAP) methods also were evaluated. A total of 21 compounds were identified in the essential oils; nevertheless, only Eucalyptol, 1,4-Methanocycloocta[d]pyridazine, 1,4,4a,5,6,9,10,10a-octahydro-11,11-dimethyl-, (1à,4à,4aà,10aà) y Ethanone, 1-(1,3-dimethyl-3-cyclohexen-1-yl) were detected in the three sites. Highest contents were registered in the sample from Padrón y Juárez with phenolic content of 33.85 mg GAE/g of dry material and antioxidant activities with ABTS 72.32% and with FRAP 21.33 mg GAE/g of dry material. Statistical differences were observed in essential oil, phenolics and antioxidants contents between populations. Results suggest that climatic differences and origin influence the phytochemicals in the medicinal plant Turnera diffusa, and thus, it is worth to consider such effects for industrial and medicinal purposes. Copyright © 2017 Hainan Medical University. Production and hosting by Elsevier B.V. All rights reserved.

Error Estimation of Pathfinder Version 5.3 SST Level 3C Using Three-way Error Analysis

NASA Astrophysics Data System (ADS)

Saha, K.; Dash, P.; Zhao, X.; Zhang, H. M.

2017-12-01

One of the essential climate variables for monitoring as well as detecting and attributing climate change, is Sea Surface Temperature (SST). A long-term record of global SSTs are available with observations obtained from ships in the early days to the more modern observation based on in-situ as well as space-based sensors (satellite/aircraft). There are inaccuracies associated with satellite derived SSTs which can be attributed to the errors associated with spacecraft navigation, sensor calibrations, sensor noise, retrieval algorithms, and leakages due to residual clouds. Thus it is important to estimate accurate errors in satellite derived SST products to have desired results in its applications.Generally for validation purposes satellite derived SST products are compared against the in-situ SSTs which have inaccuracies due to spatio/temporal inhomogeneity between in-situ and satellite measurements. A standard deviation in their difference fields usually have contributions from both satellite as well as the in-situ measurements. A real validation of any geophysical variable must require the knowledge of the "true" value of the said variable. Therefore a one-to-one comparison of satellite based SST with in-situ data does not truly provide us the real error in the satellite SST and there will be ambiguity due to errors in the in-situ measurements and their collocation differences. A Triple collocation (TC) or three-way error analysis using 3 mutually independent error-prone measurements, can be used to estimate root-mean square error (RMSE) associated with each of the measurements with high level of accuracy without treating any one system a perfectly-observed "truth". In this study we are estimating the absolute random errors associated with Pathfinder Version 5.3 Level-3C SST product Climate Data record. Along with the in-situ SST data, the third source of dataset used for this analysis is the AATSR reprocessing of climate (ARC) dataset for the corresponding period. All three SST observations are collocated, and statistics of difference between each pair is estimated. Instead of using a traditional TC analysis we have implemented the Extended Triple Collocation (ETC) approach to estimate the correlation coefficient of each measurement system w.r.t. the unknown target variable along with their RMSE.

Airborne geophysics for mesoscale observations of polar sea ice in a changing climate

NASA Astrophysics Data System (ADS)

Hendricks, S.; Haas, C.; Krumpen, T.; Eicken, H.; Mahoney, A. R.

2016-12-01

Sea ice thickness is an important geophysical parameter with a significant impact on various processes of the polar energy balance. It is classified as Essential Climate Variable (ECV), however the direct observations of the large ice-covered oceans are limited due to the harsh environmental conditions and logistical constraints. Sea-ice thickness retrieval by the means of satellite remote sensing is an active field of research, but current observational capabilities are not able to capture the small scale variability of sea ice thickness and its evolution in the presence of surface melt. We present an airborne observation system based on a towed electromagnetic induction sensor that delivers long range measurements of sea ice thickness for a wide range of sea ice conditions. The purpose-built sensor equipment can be utilized from helicopters and polar research aircraft in multi-role science missions. While airborne EM induction sounding is used in sea ice research for decades, the future challenge is the development of unmanned aerial vehicle (UAV) platform that meet the requirements for low-level EM sea ice surveys in terms of range and altitude of operations. The use of UAV's could enable repeated sea ice surveys during the the polar night, when manned operations are too dangerous and the observational data base is presently very sparse.

Spatial variability and landscape controls of near-surface permafrost within the Alaskan Yukon River Basin

USGS Publications Warehouse

Pastick, Neal J.; Jorgenson, M. Torre; Wylie, Bruce K.; Rose, Joshua R.; Rigge, Matthew; Walvoord, Michelle Ann

2014-01-01

The distribution of permafrost is important to understand because of permafrost's influence on high-latitude ecosystem structure and functions. Moreover, near-surface (defined here as within 1 m of the Earth's surface) permafrost is particularly susceptible to a warming climate and is generally poorly mapped at regional scales. Subsequently, our objectives were to (1) develop the first-known binary and probabilistic maps of near-surface permafrost distributions at a 30 m resolution in the Alaskan Yukon River Basin by employing decision tree models, field measurements, and remotely sensed and mapped biophysical data; (2) evaluate the relative contribution of 39 biophysical variables used in the models; and (3) assess the landscape-scale factors controlling spatial variations in permafrost extent. Areas estimated to be present and absent of near-surface permafrost occupy approximately 46% and 45% of the Alaskan Yukon River Basin, respectively; masked areas (e.g., water and developed) account for the remaining 9% of the landscape. Strong predictors of near-surface permafrost include climatic indices, land cover, topography, and Landsat 7 Enhanced Thematic Mapper Plus spectral information. Our quantitative modeling approach enabled us to generate regional near-surface permafrost maps and provide essential information for resource managers and modelers to better understand near-surface permafrost distribution and how it relates to environmental factors and conditions.

Using global Climate Impact Indicators to assess water resource availability in a Mediterranean mountain catchment: the Sierra Nevada study case (Spain) in the SWICCA platform

NASA Astrophysics Data System (ADS)

José Pérez-Palazón, María; Pimentel, Rafael; Sáenz de Rodrigáñez, Marta; Gulliver, Zacarias; José Polo, María

2017-04-01

Climate services provide water resource managements and users with science-based information on the likely impacts associated to the future climate scenarios. Mountainous areas are especially vulnerable to climate variations due to the expected changes in the snow regime, among others; in Mediterranean regions, this shift involves significant effects on the river flow regime and water resource availability and management. The Guadalfeo River Basin is a 1345 km2 mountainous, coastal catchment in southern Spain, ranging from the Mediterranean Sea coastline to the Sierra Nevada mountains to the north (up to 3450 m a.s.l.) within a 40-km distance. The climate variability adds complexity to this abrupt topography and heterogeneous area. The uncertainty associated to snow occurrence and persistence for the next decades poses a challenge for the current and future water resource uses in the area. The development of easy-to-use local climate indicators and derived decision-making variables is key to assess and face the economic impact of the potential changes. The SWICCA (Service for Water Indicators in Climate Change Adaptation) Platform (http://swicca.climate.copernicus.eu/) has been developed under the Copernicus Climate Change Service (C3S) and provides global climate and hydrology indicators on a Pan-European scale. Different case studies are included to assess the platform development and contents, and analyse the indicators' performance from a proof-of-concept approach that includes end-users feedbacks. The Guadalfeo River Basin is one of these case studies. This work presents the work developed so far to analyse and use the SWICCA Climate Impact Indicators (CIIs) related to river flow in this mountainous area, and the first set of local indicators specifically designed to assess selected end-users on the potential impact associated to different climate scenarios. Different CIIs were extracted from the SWICCA interface and tested against the local information available in the case study. The Essential Climate Variables used were precipitation and flow daily values, obtained at different spatial scales. The analysis led to the use of SWICCA-river flow on a catchment scale as the most suitable global CIIs in this area. Further treatment included local downscaling by means of transfer functions and a final relative anomaly correction. Three final end-users (clients) were identified within the water resource management framework: 1) mini hydropower facilities at the head areas, 2) urban supply at the southern area, and 3) water management decision makers (reservoir operation). From the corrected CIIs, local indicators were defined from the interaction with each client, to tailor water services easily and readily usable. Knowledge brokering from this interaction resulted in a first identification of a set of 4, 3 and 4 indicators for hydropower generation, urban users and water resource decision-makers, respectively, with different time scales. The projections of three future climate scenarios were assessed for each indicator and presented to each client. Local indicators are an efficient tool to assess the potential range of water allocation possibilities in this area on an annual and decadal basis, and get a deeper insight of the seasonal future potential regime of water resource availability. The results are good examples of key information for decision making in the future, and show how to derive local indicators with impact in the short and medium term planning in heterogeneous catchments in this region.

Interactions of Climate Change and Nitrogen Management for Optimizing Crop Productivity and Food Security while Minimizing Nitrogen Pollution and Greenhouse Gas Emissions

NASA Astrophysics Data System (ADS)

Davidson, E. A.; Suddick, E. C.

2012-12-01

Producing food, transportation, and energy for seven billion people has led to huge increases in use of synthetic nitrogen (N) fertilizers and fossil fuels, resulting in large releases of N as air and water pollution. In its numerous chemical forms, N plays a critical role in all aspects of climate change, including mitigation, adaptation, and impacts. Here we report on a multi-authored, interdisciplinary technical report on climate-nitrogen interactions submitted to the US National Climate Assessment as part of a Research Coordination Network activity. Management of the N cycle not only affects emissions of nitrous oxide (N2O) and nitrogen oxides (NOX), but also impacts carbon dioxide (CO2) and methane (CH4), through effects on carbon cycling processes in forests and soils and the effects on atmospheric reactions of ozone (O3) and CH4. While some of these direct and indirect N effects have a short-term cooling effect, the warming effects of N2O dominate at long time scales. The challenges of mitigating N2O emissions are substantially different from those for CO2 and CH4, because N is essential for food production, and over 80% of anthropogenic N2O emissions are from the agricultural sector. On one hand, improved agricultural nutrient management can confer some adaptive capacity of crops to climatic variability, but, on the other hand, increased climatic variability will render the task more difficult to manage nutrients for the optimization of crop productivity while minimizing N losses to the environment. Higher air temperatures will result in a "climate penalty" for air quality mitigation efforts, because larger NOX emissions reductions will be needed to achieve the same reductions of O3 pollution under higher temperatures, thus imposing further challenges to avoid harmful impacts on human health and crop productivity. Changes in river discharge, due to summer drought and to extreme precipitation events, will affect the transport of N from agricultural fields to rivers and estuaries, potentially resulting in more eutrophication, including harmful algal blooms. Both climate change and N inputs from N deposition can provoke biodiversity loss in aquatic and terrestrial ecosystems, because nutrient enrichment of native ecosystems often favors fast-growing, often non-native species. Policies aimed at improving N-use efficiencies in agriculture and reducing emissions from transportation and energy sectors would have multiple interacting benefits for climate mitigation and adaptation and for minimizing climate change impacts on crop productivity, air and water quality, biodiversity, human health risks, and food security.

How are interannual modes of variability IOD, ENSO, SAM, AMO excited by natural and anthropogenic forcing?

NASA Astrophysics Data System (ADS)

Maher, Nicola; Marotzke, Jochem

2017-04-01

Natural climate variability is found in observations, paleo-proxies, and climate models. Such climate variability can be intrinsic internal variability or externally forced, for example by changes in greenhouse gases or large volcanic eruptions. There are still questions concerning how external forcing, both natural (e.g., volcanic eruptions and solar variability) and anthropogenic (e.g., greenhouse gases and ozone) may excite both interannual modes of variability in the climate system. This project aims to address some of these problems, utilising the large ensemble of the MPI-ESM-LR climate model. In this study we investigate the statistics of four modes of interannual variability, namely the North Atlantic Oscillation (NAO), the Indian Ocean Dipole (IOD), the Southern Annular Mode (SAM) and the El Niño Southern Oscillation (ENSO). Using the 100-member ensemble of MPI-ESM-LR the statistical properties of these modes (amplitude and standard deviation) can be assessed over time. Here we compare the properties in the pre-industrial control run, historical run and future scenarios (RCP4.5, RCP2.6) and present preliminary results.

Climate models predict increasing temperature variability in poor countries.

PubMed

Bathiany, Sebastian; Dakos, Vasilis; Scheffer, Marten; Lenton, Timothy M

2018-05-01

Extreme events such as heat waves are among the most challenging aspects of climate change for societies. We show that climate models consistently project increases in temperature variability in tropical countries over the coming decades, with the Amazon as a particular hotspot of concern. During the season with maximum insolation, temperature variability increases by ~15% per degree of global warming in Amazonia and Southern Africa and by up to 10%°C -1 in the Sahel, India, and Southeast Asia. Mechanisms include drying soils and shifts in atmospheric structure. Outside the tropics, temperature variability is projected to decrease on average because of a reduced meridional temperature gradient and sea-ice loss. The countries that have contributed least to climate change, and are most vulnerable to extreme events, are projected to experience the strongest increase in variability. These changes would therefore amplify the inequality associated with the impacts of a changing climate.

Climate models predict increasing temperature variability in poor countries

PubMed Central

Dakos, Vasilis; Scheffer, Marten

2018-01-01

Extreme events such as heat waves are among the most challenging aspects of climate change for societies. We show that climate models consistently project increases in temperature variability in tropical countries over the coming decades, with the Amazon as a particular hotspot of concern. During the season with maximum insolation, temperature variability increases by ~15% per degree of global warming in Amazonia and Southern Africa and by up to 10%°C−1 in the Sahel, India, and Southeast Asia. Mechanisms include drying soils and shifts in atmospheric structure. Outside the tropics, temperature variability is projected to decrease on average because of a reduced meridional temperature gradient and sea-ice loss. The countries that have contributed least to climate change, and are most vulnerable to extreme events, are projected to experience the strongest increase in variability. These changes would therefore amplify the inequality associated with the impacts of a changing climate. PMID:29732409

Towards the Prediction of Decadal to Centennial Climate Processes in the Coupled Earth System Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Zhengyu; Kutzbach, J.; Jacob, R.

2011-12-05

In this proposal, we have made major advances in the understanding of decadal and long term climate variability. (a) We performed a systematic study of multidecadal climate variability in FOAM-LPJ and CCSM-T31, and are starting exploring decadal variability in the IPCC AR4 models. (b) We develop several novel methods for the assessment of climate feedbacks in the observation. (c) We also developed a new initialization scheme DAI (Dynamical Analogue Initialization) for ensemble decadal prediction. (d) We also studied climate-vegetation feedback in the observation and models. (e) Finally, we started a pilot program using Ensemble Kalman Filter in CGCM for decadalmore » climate prediction.« less

Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part II: climate variability

NASA Astrophysics Data System (ADS)

Rackow, T.; Goessling, H. F.; Jung, T.; Sidorenko, D.; Semmler, T.; Barbi, D.; Handorf, D.

2018-04-01

This study forms part II of two papers describing ECHAM6-FESOM, a newly established global climate model with a unique multi-resolution sea ice-ocean component. While part I deals with the model description and the mean climate state, here we examine the internal climate variability of the model under constant present-day (1990) conditions. We (1) assess the internal variations in the model in terms of objective variability performance indices, (2) analyze variations in global mean surface temperature and put them in context to variations in the observed record, with particular emphasis on the recent warming slowdown, (3) analyze and validate the most common atmospheric and oceanic variability patterns, (4) diagnose the potential predictability of various climate indices, and (5) put the multi-resolution approach to the test by comparing two setups that differ only in oceanic resolution in the equatorial belt, where one ocean mesh keeps the coarse 1° resolution applied in the adjacent open-ocean regions and the other mesh is gradually refined to 0.25°. Objective variability performance indices show that, in the considered setups, ECHAM6-FESOM performs overall favourably compared to five well-established climate models. Internal variations of the global mean surface temperature in the model are consistent with observed fluctuations and suggest that the recent warming slowdown can be explained as a once-in-one-hundred-years event caused by internal climate variability; periods of strong cooling in the model (`hiatus' analogs) are mainly associated with ENSO-related variability and to a lesser degree also to PDO shifts, with the AMO playing a minor role. Common atmospheric and oceanic variability patterns are simulated largely consistent with their real counterparts. Typical deficits also found in other models at similar resolutions remain, in particular too weak non-seasonal variability of SSTs over large parts of the ocean and episodic periods of almost absent deep-water formation in the Labrador Sea, resulting in overestimated North Atlantic SST variability. Concerning the influence of locally (isotropically) increased resolution, the ENSO pattern and index statistics improve significantly with higher resolution around the equator, illustrating the potential of the novel unstructured-mesh method for global climate modeling.

Forced Atlantic Multidecadal Variability Over the Past Millennium

NASA Astrophysics Data System (ADS)

Halloran, P. R.; Reynolds, D.; Scourse, J. D.; Hall, I. R.

2016-02-01

Paul R. Halloran, David J. Reynolds, Ian R. Hall and James D. Scourse Multidecadal variability in Atlantic sea surface temperatures (SSTs) plays a first order role in determining regional atmospheric circulation and moisture transport, with major climatic consequences. These regional climate impacts range from drought in the Sahel and South America, though increased hurricane activity and temperature extremes, to modified monsoonal rainfall. Multidecadal Atlantic SST variability could arise through internal variability in the Atlantic Meridional Overturning Circulation (AMOC) (e.g., Knight et al., 2006), or through externally forced change (e.g. Booth et al., 2012). It is critical that we know whether internal or external forcing dominates if we are to provide useful near-term climate projections in the Atlantic region. A persuasive argument that internal variability plays an important role in Atlantic Multidecadal Variability is that periodic SST variability has been observed throughout much of the last millennium (Mann et al., 2009), and the hypothesized external forcing of historical Atlantic Multidecadal Variability (Booth et al., 2012) is largely anthropogenic in origin. Here we combine the first annually-resolved millennial marine reconstruction with multi-model analysis, to show that the Atlantic SST variability of the last millennium can be explained by a combination of direct volcanic forcing, and indirect, forced, AMOC variability. Our results indicate that whilst climate models capture the timing of both the directly forced SST and forced AMOC-mediated SST variability, the models fail to capture the magnitude of the forced AMOC change. Does this mean that models underestimate the 21st century reduction in AMOC strength? J. Knight, C. Folland and A. Scaife., Climate impacts of the Atlantic Multidecadal Oscillation, GRL, 2006 B.B.B Booth, N. Dunstone, P.R. Halloran et al., Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability, Nature, 2012 M.E. Mann, Z. Zhang, S. Rutherford et al., Global Signatures and Dynamical Origins of the Little Ice Age and Medieval Climate Anomaly, Science, 2009

Competition alters tree growth responses to climate at individual and stand scales

Treesearch

Kevin R. Ford; Ian K. Breckheimer; Jerry F. Franklin; James A. Freund; Steve J. Kroiss; Andrew J. Larson; Elinore J. Theobald; Janneke HilleRisLambers

2017-01-01

Understanding how climate affects tree growth is essential for assessing climate change impacts on forests but can be confounded by effects of competition, which strongly influences tree responses to climate. We characterized the joint influences of tree size, competition, and climate on diameter growth using hierarchical Bayesian methods applied to permanent sample...

Competition alters tree growth responses to climate at individual and stand scales

Treesearch

Kevin Ford; Ian K. Breckheimer; Jerry F. Franklin; James A. Freund; Steve J. Kroiss; Andrew J. Larson; Elinore J. Theobald; Janneke. HilleRisLambers

2015-01-01

Understanding how climate affects tree growth is essential for assessing climate change impacts on forests, but is complicated by the effects of competition, which strongly influences growth and could alter how forests respond to climate change. We characterized the joint effects of climate and competition on diameter growth in the mountain forests of Mount Rainier...

Climate and climate variability of the wind power resources in the Great Lakes region of the United States

Treesearch

X. Li; S. Zhong; X. Bian; W.E. Heilman

2010-01-01

The climate and climate variability of low-level winds over the Great Lakes region of the United States is examined using 30 year (1979-2008) wind records from the recently released North American Regional Reanalysis (NARR), a three-dimensional, high-spatial and temporal resolution, and dynamically consistent climate data set. The analyses focus on spatial distribution...

Skilful multi-year predictions of tropical trans-basin climate variability

PubMed Central

Chikamoto, Yoshimitsu; Timmermann, Axel; Luo, Jing-Jia; Mochizuki, Takashi; Kimoto, Masahide; Watanabe, Masahiro; Ishii, Masayoshi; Xie, Shang-Ping; Jin, Fei-Fei

2015-01-01

Tropical Pacific sea surface temperature anomalies influence the atmospheric circulation, impacting climate far beyond the tropics. The predictability of the corresponding atmospheric signals is typically limited to less than 1 year lead time. Here we present observational and modelling evidence for multi-year predictability of coherent trans-basin climate variations that are characterized by a zonal seesaw in tropical sea surface temperature and sea-level pressure between the Pacific and the other two ocean basins. State-of-the-art climate model forecasts initialized from a realistic ocean state show that the low-frequency trans-basin climate variability, which explains part of the El Niño Southern Oscillation flavours, can be predicted up to 3 years ahead, thus exceeding the predictive skill of current tropical climate forecasts for natural variability. This low-frequency variability emerges from the synchronization of ocean anomalies in all basins via global reorganizations of the atmospheric Walker Circulation. PMID:25897996

Skilful multi-year predictions of tropical trans-basin climate variability.

PubMed

Chikamoto, Yoshimitsu; Timmermann, Axel; Luo, Jing-Jia; Mochizuki, Takashi; Kimoto, Masahide; Watanabe, Masahiro; Ishii, Masayoshi; Xie, Shang-Ping; Jin, Fei-Fei

2015-04-21

Tropical Pacific sea surface temperature anomalies influence the atmospheric circulation, impacting climate far beyond the tropics. The predictability of the corresponding atmospheric signals is typically limited to less than 1 year lead time. Here we present observational and modelling evidence for multi-year predictability of coherent trans-basin climate variations that are characterized by a zonal seesaw in tropical sea surface temperature and sea-level pressure between the Pacific and the other two ocean basins. State-of-the-art climate model forecasts initialized from a realistic ocean state show that the low-frequency trans-basin climate variability, which explains part of the El Niño Southern Oscillation flavours, can be predicted up to 3 years ahead, thus exceeding the predictive skill of current tropical climate forecasts for natural variability. This low-frequency variability emerges from the synchronization of ocean anomalies in all basins via global reorganizations of the atmospheric Walker Circulation.

Qualitatively Assessing Randomness in SVD Results

NASA Astrophysics Data System (ADS)

Lamb, K. W.; Miller, W. P.; Kalra, A.; Anderson, S.; Rodriguez, A.

2012-12-01

Singular Value Decomposition (SVD) is a powerful tool for identifying regions of significant co-variability between two spatially distributed datasets. SVD has been widely used in atmospheric research to define relationships between sea surface temperatures, geopotential height, wind, precipitation and streamflow data for myriad regions across the globe. A typical application for SVD is to identify leading climate drivers (as observed in the wind or pressure data) for a particular hydrologic response variable such as precipitation, streamflow, or soil moisture. One can also investigate the lagged relationship between a climate variable and the hydrologic response variable using SVD. When performing these studies it is important to limit the spatial bounds of the climate variable to reduce the chance of random co-variance relationships being identified. On the other hand, a climate region that is too small may ignore climate signals which have more than a statistical relationship to a hydrologic response variable. The proposed research seeks to identify a qualitative method of identifying random co-variability relationships between two data sets. The research identifies the heterogeneous correlation maps from several past results and compares these results with correlation maps produced using purely random and quasi-random climate data. The comparison identifies a methodology to determine if a particular region on a correlation map may be explained by a physical mechanism or is simply statistical chance.

Effect of climate variables on cocoa black pod incidence in Sabah using ARIMAX model

NASA Astrophysics Data System (ADS)

Ling Sheng Chang, Albert; Ramba, Haya; Mohd. Jaaffar, Ahmad Kamil; Kim Phin, Chong; Chong Mun, Ho

2016-06-01

Cocoa black pod disease is one of the major diseases affecting the cocoa production in Malaysia and also around the world. Studies have shown that the climate variables have influenced the cocoa black pod disease incidence and it is important to quantify the black pod disease variation due to the effect of climate variables. Application of time series analysis especially auto-regressive moving average (ARIMA) model has been widely used in economics study and can be used to quantify the effect of climate variables on black pod incidence to forecast the right time to control the incidence. However, ARIMA model does not capture some turning points in cocoa black pod incidence. In order to improve forecasting performance, other explanatory variables such as climate variables should be included into ARIMA model as ARIMAX model. Therefore, this paper is to study the effect of climate variables on the cocoa black pod disease incidence using ARIMAX model. The findings of the study showed ARIMAX model using MA(1) and relative humidity at lag 7 days, RHt - 7 gave better R square value compared to ARIMA model using MA(1) which could be used to forecast the black pod incidence to assist the farmers determine timely application of fungicide spraying and culture practices to control the black pod incidence.

How does complex terrain influence responses of carbon and water cycle processes to climate variability and climate change?

EPA Science Inventory

We are pursuing the ambitious goal of understanding how complex terrain influences the responses of carbon and water cycle processes to climate variability and climate change. Our studies take place in H.J. Andrews Experimental Forest, an LTER (Long Term Ecological Research) site...

Compound extremes of summer temperature and precipitation leading to intensified departures from natural variability.

NASA Astrophysics Data System (ADS)

Mahony, C. R.; Cannon, A. J.

2017-12-01

Climate change can drive local climates outside the range of their historical year-to-year variability, straining the adaptive capacity of ecological and human communities. We demonstrate that interactions between climate variables can produce larger and earlier departures from natural variability than is detectable in individual variables. For example, summer temperature (Tx) and precipitation (Pr) are negatively correlated in most terrestrial regions, such that interannual variability lies along an axis from warm-and-dry to cool-and-wet conditions. A climate change trend perpendicular to this axis, towards warmer-wetter conditions, can depart more quickly from the range of natural variability than a warmer-drier trend. This multivariate "departure intensification" effect is evident in all six CMIP5 models that we examined: 23% (9-34%) of the land area of each model exhibits a pronounced increase in 2σ extremesin the Tx-Pr regime relative to Tx or Pr alone. Observational data suggest that Tx-Pr correlations are sufficient to produce departure intensification in distinct regions on all continents. Departures from the historical Tx-Pr regime may produce ecological disruptions, such as in plant-pathogen interactions and human diseases, that could offset the drought mitigation benefits of increased precipitation. Our study alerts researchers and adaptation practitioners to the presence of multivariate climate change signals and compound extremes that are not detectable in individual climate variables.

Linking the variability of atmospheric carbon monoxide to climate modes in the Southern Hemisphere

NASA Astrophysics Data System (ADS)

Buchholz, Rebecca; Monks, Sarah; Hammerling, Dorit; Worden, Helen; Deeter, Merritt; Emmons, Louisa; Edwards, David

2017-04-01

Biomass burning is a major driver of atmospheric carbon monoxide (CO) variability in the Southern Hemisphere. The magnitude of emissions, such as CO, from biomass burning is connected to climate through both the availability and dryness of fuel. We investigate the link between CO and climate using satellite measured CO and climate indices. Observations of total column CO from the satellite instrument MOPITT are used to build a record of interannual variability in CO since 2001. Four biomass burning regions in the Southern Hemisphere are explored. Data driven relationships are determined between CO and climate indices for the climate modes: El Niño Southern Oscillation (ENSO); the Indian Ocean Dipole (IOD); the Tropical Southern Atlantic (TSA); and the Southern Annular Mode (SAM). Stepwise forward and backward regression is used to select the best statistical model from combinations of lagged indices. We find evidence for the importance of first-order interaction terms of the climate modes when explaining CO variability. Implications of the model results are discussed for the Maritime Southeast Asia and Australasia regions. We also draw on the chemistry-climate model CAM-chem to explain the source contribution as well as the relative contributions of emissions and meteorology to CO variability.

Large-Scale Circulation and Climate Variability. Chapter 5

NASA Technical Reports Server (NTRS)

Perlwitz, J.; Knutson, T.; Kossin, J. P.; LeGrande, A. N.

2017-01-01

The causes of regional climate trends cannot be understood without considering the impact of variations in large-scale atmospheric circulation and an assessment of the role of internally generated climate variability. There are contributions to regional climate trends from changes in large-scale latitudinal circulation, which is generally organized into three cells in each hemisphere-Hadley cell, Ferrell cell and Polar cell-and which determines the location of subtropical dry zones and midlatitude jet streams. These circulation cells are expected to shift poleward during warmer periods, which could result in poleward shifts in precipitation patterns, affecting natural ecosystems, agriculture, and water resources. In addition, regional climate can be strongly affected by non-local responses to recurring patterns (or modes) of variability of the atmospheric circulation or the coupled atmosphere-ocean system. These modes of variability represent preferred spatial patterns and their temporal variation. They account for gross features in variance and for teleconnections which describe climate links between geographically separated regions. Modes of variability are often described as a product of a spatial climate pattern and an associated climate index time series that are identified based on statistical methods like Principal Component Analysis (PC analysis), which is also called Empirical Orthogonal Function Analysis (EOF analysis), and cluster analysis.

Growth-climate relationships across topographic gradients in the northern Great Lakes

Treesearch

S.F. Dymond; A.W. D' Amato; Randy Kolka; P.V. Bolstad; Stephen Sebestyen; J.B. Bradford

2016-01-01

Climatic conditions exert important control over the growth, productivity, and distribution of forests, and characterizing these relationships is essential for understanding how forest ecosystems will respond to climate change. We used dendrochronological methods to develop climate–growth relationships for two dominant species, Populus tremuloides...

The Space-Time Variation of Global Crop Yields, Detecting Simultaneous Outliers and Identifying the Teleconnections with Climatic Patterns

NASA Astrophysics Data System (ADS)

Najafi, E.; Devineni, N.; Pal, I.; Khanbilvardi, R.

2017-12-01

An understanding of the climate factors that influence the space-time variability of crop yields is important for food security purposes and can help us predict global food availability. In this study, we address how the crop yield trends of countries globally were related to each other during the last several decades and the main climatic variables that triggered high/low crop yields simultaneously across the world. Robust Principal Component Analysis (rPCA) is used to identify the primary modes of variation in wheat, maize, sorghum, rice, soybeans, and barley yields. Relations between these modes of variability and important climatic variables, especially anomalous sea surface temperature (SSTa), are examined from 1964 to 2010. rPCA is also used to identify simultaneous outliers in each year, i.e. systematic high/low crop yields across the globe. The results demonstrated spatiotemporal patterns of these crop yields and the climate-related events that caused them as well as the connection of outliers with weather extremes. We find that among climatic variables, SST has had the most impact on creating simultaneous crop yields variability and yield outliers in many countries. An understanding of this phenomenon can benefit global crop trade networks.

The false spring of 2012, earliest in North American record

USGS Publications Warehouse

Ault, T.R.; Henebry, G.M.; de Beurs, K. M.; Schwartz, M.D.; Betancourt, Julio L.; Moore, David

2013-01-01

Phenology - the study of recurring plant and animal life cycle stages, especially their timing and relationships with weather and climate - is becoming an essential tool for documenting, communicating, and anticipating the consequences of climate variability and change. For example, March 2012 broke numerous records for warm temperatures and early flowering in the United States [Karl et al., 2012; Elwood et al., 2013]. Many regions experienced a “false spring,” a period of weather in late winter or early spring sufficiently mild and long to bring vegetation out of dormancy prematurely, rendering it vulnerable to late frost and drought.As global climate warms, increasingly warmer springs may combine with the random climatological occurrence of advective freezes, which result from cold air moving from one region to another, to dramatically increase the future risk of false springs, with profound ecological and economic consequences [e.g., Gu et al., 2008; Marino et al., 2011; Augspurger, 2013]. For example, in the false spring of 2012, an event embedded in long-term trends toward earlier spring [e.g., Schwartz et al., 2006], the frost damage to fruit trees totaled half a billion dollars in Michigan alone, prompting the federal government to declare the state a disaster area [Knudson, 2012].

Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities

PubMed Central

Kwan, Paul; Welch, Mitchell

2017-01-01

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops. PMID:28875085

Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities.

PubMed

Al-Kindi, Khalifa M; Kwan, Paul; R Andrew, Nigel; Welch, Mitchell

2017-01-01

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus . An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops.

Utilizing Satellite Precipitation Products to Understand the Link Between Climate Variability and Malaria

NASA Astrophysics Data System (ADS)

Maggioni, V.; Mousam, A.; Delamater, P. L.; Cash, B. A.; Quispe, A.

2015-12-01

Malaria is a public health threat to people globally leading to 198 million cases and 584,000 deaths annually. Outbreaks of vector borne diseases such as malaria can be significantly impacted by climate variables such as precipitation. For example, an increase in rainfall has the potential to create pools of water that can serve as breeding locations for mosquitos. Peru is a country that is currently controlling malaria, but has not been able to completely eliminate the disease. Despite the various initiatives in order to control malaria - including regional efforts to improve surveillance, early detection, prompt treatment, and vector management - malaria cases in Peru have risen between 2011 and 2014. The purpose of this study is to test the hypothesis that climate variability plays a fundamental role in malaria occurrence over a 12-year period (2003-2014) in Peru. When analyzing climate variability, it is important to obtain high-quality, high-resolution data for a time series long enough to draw conclusion about how climate variables have been and are changing. Remote sensing is a powerful tool for measuring and monitoring climate variables continuously in time and space. A widely used satellite-based precipitation product, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), available globally since 1998, was used to obtain 3-hourly data with a spatial resolution of 0.25° x 0.25°. The precipitation data was linked to weekly (2003-2014) malaria cases collected by health centers and available at a district level all over Peru to investigate the relationship between precipitation and the seasonal and annual variations in malaria incidence. Further studies will incorporate additional climate variables such as temperature, humidity, soil moisture, and surface pressure from remote sensing data products and climate models. Ultimately, this research will help us to understand if climate variability impacts malaria incidence rates and to determine which regions of the country are most affected.

An 'Observational Large Ensemble' to compare observed and modeled temperature trend uncertainty due to internal variability.

NASA Astrophysics Data System (ADS)

Poppick, A. N.; McKinnon, K. A.; Dunn-Sigouin, E.; Deser, C.

2017-12-01

Initial condition climate model ensembles suggest that regional temperature trends can be highly variable on decadal timescales due to characteristics of internal climate variability. Accounting for trend uncertainty due to internal variability is therefore necessary to contextualize recent observed temperature changes. However, while the variability of trends in a climate model ensemble can be evaluated directly (as the spread across ensemble members), internal variability simulated by a climate model may be inconsistent with observations. Observation-based methods for assessing the role of internal variability on trend uncertainty are therefore required. Here, we use a statistical resampling approach to assess trend uncertainty due to internal variability in historical 50-year (1966-2015) winter near-surface air temperature trends over North America. We compare this estimate of trend uncertainty to simulated trend variability in the NCAR CESM1 Large Ensemble (LENS), finding that uncertainty in wintertime temperature trends over North America due to internal variability is largely overestimated by CESM1, on average by a factor of 32%. Our observation-based resampling approach is combined with the forced signal from LENS to produce an 'Observational Large Ensemble' (OLENS). The members of OLENS indicate a range of spatially coherent fields of temperature trends resulting from different sequences of internal variability consistent with observations. The smaller trend variability in OLENS suggests that uncertainty in the historical climate change signal in observations due to internal variability is less than suggested by LENS.

Trends and Controls of inter-annual Variability in the Carbon Budget of Terrestrial Ecosystems

NASA Astrophysics Data System (ADS)

Cescatti, A.; Marcolla, B.

2014-12-01

The climate sensitivity of the terrestrial carbon budget will substantially affect the sign and strength of the land-climate feedbacks and the future climate trajectories. Current trends in the inter-annual variability of terrestrial carbon fluxes (IAV) may contribute to clarify the relative role of physical and biological controls of ecosystem responses to climate change. For this purpose we investigated how recent climate variability has impacted the carbon fluxes at long-term FLUXNET sites. Using a novel method, the IAV has been factored out in climate induced variability (physical control), variability due to changes in ecosystem functioning (biological control) and the interaction of the two terms. The relative control of the main climatic drivers (temperature, water availability) on the physical and biological sources of IAV has been investigated using both site level fluxes and global gridded products generated from the up-scaling of flux data. Results of this analysis highlight the fundamental role of precipitation trends on the pattern of IAV in the last 30 years. Our findings on the spatial/temporal trends of IAV have been finally confirmed using the signal derived from the global network of atmospheric CO2 concentrations measurements.

Pollen-based continental climate reconstructions at 6 and 21 ka: a global synthesis

USGS Publications Warehouse

Bartlein, P.J.; Harrison, S.P.; Brewer, Sandra; Connor, S.; Davis, B.A.S.; Gajewski, K.; Guiot, J.; Harrison-Prentice, T. I.; Henderson, A.; Peyron, O.; Prentice, I.C.; Scholze, M.; Seppa, H.; Shuman, B.; Sugita, S.; Thompson, R.S.; Viau, A.E.; Williams, J.; Wu, H.

2010-01-01

Subfossil pollen and plant macrofossil data derived from 14C-dated sediment profiles can provide quantitative information on glacial and interglacial climates. The data allow climate variables related to growing-season warmth, winter cold, and plant-available moisture to be reconstructed. Continental-scale reconstructions have been made for the mid-Holocene (MH, around 6 ka) and Last Glacial Maximum (LGM, around 21 ka), allowing comparison with palaeoclimate simulations currently being carried out as part of the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change. The synthesis of the available MH and LGM climate reconstructions and their uncertainties, obtained using modern-analogue, regression and model-inversion techniques, is presented for four temperature variables and two moisture variables. Reconstructions of the same variables based on surface-pollen assemblages are shown to be accurate and unbiased. Reconstructed LGM and MH climate anomaly patterns are coherent, consistent between variables, and robust with respect to the choice of technique. They support a conceptual model of the controls of Late Quaternary climate change whereby the first-order effects of orbital variations and greenhouse forcing on the seasonal cycle of temperature are predictably modified by responses of the atmospheric circulation and surface energy balance.

Quantifying the increasing sensitivity of power systems to climate variability

NASA Astrophysics Data System (ADS)

Bloomfield, H. C.; Brayshaw, D. J.; Shaffrey, L. C.; Coker, P. J.; Thornton, H. E.

2016-12-01

Large quantities of weather-dependent renewable energy generation are expected in power systems under climate change mitigation policies, yet little attention has been given to the impact of long term climate variability. By combining state-of-the-art multi-decadal meteorological records with a parsimonious representation of a power system, this study characterises the impact of year-to-year climate variability on multiple aspects of the power system of Great Britain (including coal, gas and nuclear generation), demonstrating why multi-decadal approaches are necessary. All aspects of the example system are impacted by inter-annual climate variability, with the impacts being most pronounced for baseload generation. The impacts of inter-annual climate variability increase in a 2025 wind-power scenario, with a 4-fold increase in the inter-annual range of operating hours for baseload such as nuclear. The impacts on peak load and peaking-plant are comparably small. Less than 10 years of power supply and demand data are shown to be insufficient for providing robust power system planning guidance. This suggests renewable integration studies—widely used in policy, investment and system design—should adopt a more robust approach to climate characterisation.

The SATIRE-S model and why getting solar cycle spectral irradiance trends correct is so important

NASA Astrophysics Data System (ADS)

Ball, William; Haigh, Joanna; Krivova, Natalie; Unruh, Yvonne; Solanki, Sami

2014-05-01

There is currently a wide range of potential spectral solar irradiance (SSI) solar cycle (SC) amplitudes suggested by observations and models. Therefore, SSI SC changes are still not fully understood. The magnitude of the SC flux changes has a direct impact upon the temperature and chemistry of the Earth's atmosphere. To contribute to an understanding of the solar-climate connection, it is critical that we, as the solar community, communicate effectively with the climate community, providing uncertainties in SSI data and assessments of possible SSI options. We present the SATIRE-S reconstruction in the context of these SSI datasets. SATIRE-S is a physically based, consistent SSI reconstruction over the last three solar cycles. It shows different SC spectral variability at all wavelengths compared to the NRLSSI model, widely used in climate research. Most-importantly, SC changes in the ultra-violet (UV) can be twice as large in SATIRE-S as NRLSSI. Typically NRLSSI provides a lower limit of SC SSI UV variability. SORCE satellite observations provide SC magnitudes at the upper limit of variability, exceeding that of SATIRE-S by a factor of three at some UV wavelengths. There is currently no way to be certain if any of these three SSI datasets, or others, is correct. We also present the SSI datasets in terms of their impact on stratospheric ozone, within a 2D atmospheric model, as an example of why it is important to get SC changes correct. Using NRLSSI results in the 2D atmospheric model, we see a decrease in ozone concentration at all altitudes from solar maximum to minimum. SATIRE-S and SORCE/SOLSTICE observations instead show an increase in ozone concentration in the mesosphere. The magnitude of the increase in the mesosphere when using SOLSTICE also depends greatly upon the version of the data, which means that studies using different data versions of SOLSTICE may lead to different conclusions. These results highlight why an accurate understanding of SC SSI changes, and their uncertainties, are essential for the climate community that uses our work.

Climate variability and human impact in South America during the last 2000 years: synthesis and perspectives from pollen records

NASA Astrophysics Data System (ADS)

Flantua, S. G. A.; Hooghiemstra, H.; Vuille, M.; Behling, H.; Carson, J. F.; Gosling, W. D.; Hoyos, I.; Ledru, M. P.; Montoya, E.; Mayle, F.; Maldonado, A.; Rull, V.; Tonello, M. S.; Whitney, B. S.; González-Arango, C.

2016-02-01

An improved understanding of present-day climate variability and change relies on high-quality data sets from the past 2 millennia. Global efforts to model regional climate modes are in the process of being validated against, and integrated with, records of past vegetation change. For South America, however, the full potential of vegetation records for evaluating and improving climate models has hitherto not been sufficiently acknowledged due to an absence of information on the spatial and temporal coverage of study sites. This paper therefore serves as a guide to high-quality pollen records that capture environmental variability during the last 2 millennia. We identify 60 vegetation (pollen) records from across South America which satisfy geochronological requirements set out for climate modelling, and we discuss their sensitivity to the spatial signature of climate modes throughout the continent. Diverse patterns of vegetation response to climate change are observed, with more similar patterns of change in the lowlands and varying intensity and direction of responses in the highlands. Pollen records display local-scale responses to climate modes; thus, it is necessary to understand how vegetation-climate interactions might diverge under variable settings. We provide a qualitative translation from pollen metrics to climate variables. Additionally, pollen is an excellent indicator of human impact through time. We discuss evidence for human land use in pollen records and provide an overview considered useful for archaeological hypothesis testing and important in distinguishing natural from anthropogenically driven vegetation change. We stress the need for the palynological community to be more familiar with climate variability patterns to correctly attribute the potential causes of observed vegetation dynamics. This manuscript forms part of the wider LOng-Term multi-proxy climate REconstructions and Dynamics in South America - 2k initiative that provides the ideal framework for the integration of the various palaeoclimatic subdisciplines and palaeo-science, thereby jump-starting and fostering multidisciplinary research into environmental change on centennial and millennial timescales.

Quantifying climate-growth relationships at the stand level in a mature mixed-species conifer forest.

PubMed

Teets, Aaron; Fraver, Shawn; Weiskittel, Aaron R; Hollinger, David Y

2018-03-11

A range of environmental factors regulate tree growth; however, climate is generally thought to most strongly influence year-to-year variability in growth. Numerous dendrochronological (tree-ring) studies have identified climate factors that influence year-to-year variability in growth for given tree species and location. However, traditional dendrochronology methods have limitations that prevent them from adequately assessing stand-level (as opposed to species-level) growth. We argue that stand-level growth analyses provide a more meaningful assessment of forest response to climate fluctuations, as well as the management options that may be employed to sustain forest productivity. Working in a mature, mixed-species stand at the Howland Research Forest of central Maine, USA, we used two alternatives to traditional dendrochronological analyses by (1) selecting trees for coring using a stratified (by size and species), random sampling method that ensures a representative sample of the stand, and (2) converting ring widths to biomass increments, which once summed, produced a representation of stand-level growth, while maintaining species identities or canopy position if needed. We then tested the relative influence of seasonal climate variables on year-to-year variability in the biomass increment using generalized least squares regression, while accounting for temporal autocorrelation. Our results indicate that stand-level growth responded most strongly to previous summer and current spring climate variables, resulting from a combination of individualistic climate responses occurring at the species- and canopy-position level. Our climate models were better fit to stand-level biomass increment than to species-level or canopy-position summaries. The relative growth responses (i.e., percent change) predicted from the most influential climate variables indicate stand-level growth varies less from to year-to-year than species-level or canopy-position growth responses. By assessing stand-level growth response to climate, we provide an alternative perspective on climate-growth relationships of forests, improving our understanding of forest growth dynamics under a fluctuating climate. © 2018 John Wiley & Sons Ltd.

NUTRItion and CLIMate (NUTRICLIM): investigating the relationship between climate variables and childhood malnutrition through agriculture, an exploratory study in Burkina Faso.

PubMed

Sorgho, Raissa; Franke, Jonas; Simboro, Seraphin; Phalkey, Revati; Saeurborn, Rainer

Malnutrition remains a leading cause of death in children in low- and middle-income countries; this will be aggravated by climate change. Annually, 6.9 million deaths of children under 5 were attributable directly or indirectly to malnutrition. Although these figures have recently decreased, evidence shows that a world with a medium climate (local warming up to 3-4 °C) will create an additional 25.2 million malnourished children. This proof of concept study explores the relationships between childhood malnutrition (more specifically stunting), regional agricultural yields, and climate variables through the use of remote sensing (RS) satellite imaging along with algorithms to predict the effect of climate variability on agricultural yields and on malnutrition of children under 5. The success of this proof of purpose study, NUTRItion and CLIMate (NUTRICLIM), should encourage researchers to apply both concept and tools to study of the link between weather variability, crop yield, and malnutrition on a larger scale. It would also allow for linking such micro-level data to climate models and address the challenge of projecting the additional impact of childhood malnutrition from climate change to various policy relevant time horizons.

Vulnerability of Breeding Waterbirds to Climate Change in the Prairie Pothole Region, U.S.A

PubMed Central

Steen, Valerie; Skagen, Susan K.; Noon, Barry R.

2014-01-01

The Prairie Pothole Region (PPR) of the north-central U.S. and south-central Canada contains millions of small prairie wetlands that provide critical habitat to many migrating and breeding waterbirds. Due to their small size and the relatively dry climate of the region, these wetlands are considered at high risk for negative climate change effects as temperatures increase. To estimate the potential impacts of climate change on breeding waterbirds, we predicted current and future distributions of species common in the PPR using species distribution models (SDMs). We created regional-scale SDMs for the U.S. PPR using Breeding Bird Survey occurrence records for 1971–2011 and wetland, upland, and climate variables. For each species, we predicted current distribution based on climate records for 1981–2000 and projected future distributions to climate scenarios for 2040–2049. Species were projected to, on average, lose almost half their current habitat (-46%). However, individual species projections varied widely, from +8% (Upland Sandpiper) to -100% (Wilson's Snipe). Variable importance ranks indicated that land cover (wetland and upland) variables were generally more important than climate variables in predicting species distributions. However, climate variables were relatively more important during a drought period. Projected distributions of species responses to climate change contracted within current areas of distribution rather than shifting. Given the large variation in species-level impacts, we suggest that climate change mitigation efforts focus on species projected to be the most vulnerable by enacting targeted wetland management, easement acquisition, and restoration efforts. PMID:24927165

Vulnerability of breeding waterbirds to climate change in the Prairie Pothole Region, U.S.A.

USGS Publications Warehouse

Steen, Valerie; Skagen, Susan K.; Noon, Barry R.

2014-01-01

The Prairie Pothole Region (PPR) of the north-central U.S. and south-central Canada contains millions of small prairie wetlands that provide critical habitat to many migrating and breeding waterbirds. Due to their small size and the relatively dry climate of the region, these wetlands are considered at high risk for negative climate change effects as temperatures increase. To estimate the potential impacts of climate change on breeding waterbirds, we predicted current and future distributions of species common in the PPR using species distribution models (SDMs). We created regional-scale SDMs for the U.S. PPR using Breeding Bird Survey occurrence records for 1971–2011 and wetland, upland, and climate variables. For each species, we predicted current distribution based on climate records for 1981–2000 and projected future distributions to climate scenarios for 2040–2049. Species were projected to, on average, lose almost half their current habitat (-46%). However, individual species projections varied widely, from +8% (Upland Sandpiper) to -100% (Wilson's Snipe). Variable importance ranks indicated that land cover (wetland and upland) variables were generally more important than climate variables in predicting species distributions. However, climate variables were relatively more important during a drought period. Projected distributions of species responses to climate change contracted within current areas of distribution rather than shifting. Given the large variation in species-level impacts, we suggest that climate change mitigation efforts focus on species projected to be the most vulnerable by enacting targeted wetland management, easement acquisition, and restoration efforts.

Demonstration of SST value as EBVs descriptor in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Valentini, E.; Filipponi, F.; Nguyen Xuan, A.; Taramelli, A.

2017-12-01

Sea Surface Temperature is an Essential Climate and Ocean Variable (ECV - EOV) able to capture critical scales in the seascape warming patterns and to highlight the exceeding of thresholds. This presentation addresses the changes of the SST in the last three decades over the Mediterranean Sea, a "Large Marine Ecosystem (LME)", in order to speculate the value of such powerful variable, as proxy for the assessment of ecosystem state in terms of ecosystem structures, functions and composition key descriptor. Time series of daily SST for the period 1982-2016, estimated from multi-sensor satellite data and provided by Copernicus Marine Environment Monitoring Service (CMEMS-EU) are used to perform different statistical analysis on common fish species. Results highlight the critical conditions, the general trends as well as the spatial and temporal patterns, in terms of thermal growth, vitality and stress influence on selected fish species. Results confirm a constant increasing trend in SST with an average rise of 1.4° C in the past thirty years. The variance associated to the average trend is not constant across the entire Mediterranean Sea opening the way to multiple scenarios for fish growth and vitality in the diverse sub-basins. A major effort is oriented in addressing the cross-scale ecological interactions to assess the feasibility of using SST as descriptor for Essential Biodiversity Variables, able to prioritize areas and to feed operational tools for planning and management in the Mediterranean LME.

Future hotspots of increasing temperature variability in tropical countries

NASA Astrophysics Data System (ADS)

Bathiany, S.; Dakos, V.; Scheffer, M.; Lenton, T. M.

2017-12-01

Resolving how climate variability will change in future is crucial to determining how challenging it will be for societies and ecosystems to adapt to climate change. We show that the largest increases in temperature variability - that are robust between state-of-the art climate models - are concentrated in tropical countries. On average, temperature variability increases by 15% per degree of global warming in Amazonia and Southern Africa during austral summer, and by up to 10% °C-1 in the Sahel, India and South East Asia. Southern hemisphere changes can be explained by drying soils, whereas shifts in atmospheric structure play a more important role in the Northern hemisphere. These robust regional changes in variability are associated with monthly timescale events, whereas uncertain changes in inter-annual modes of variability make the response of global temperature variability uncertain. Our results suggest that regional changes in temperature variability will create new inequalities in climate change impacts between rich and poor nations.

Climate variability decreases species richness and community stability in a temperate grassland.

PubMed

Zhang, Yunhai; Loreau, Michel; He, Nianpeng; Wang, Junbang; Pan, Qingmin; Bai, Yongfei; Han, Xingguo

2018-06-26

Climate change involves modifications in both the mean and the variability of temperature and precipitation. According to global warming projections, both the magnitude and the frequency of extreme weather events are increasing, thereby increasing climate variability. The previous studies have reported that climate warming tends to decrease biodiversity and the temporal stability of community primary productivity (i.e., community stability), but the effects of the variability of temperature and precipitation on biodiversity, community stability, and their relationship have not been clearly explored. We used a long-term (from 1982 to 2014) field data set from a temperate grassland in northern China to explore the effects of the variability of mean temperature and total precipitation on species richness, community stability, and their relationship. Results showed that species richness promoted community stability through increases in asynchronous dynamics across species (i.e., species asynchrony). Both species richness and species asynchrony were positively associated with the residuals of community stability after controlling for its dependence on the variability of mean temperature and total precipitation. Furthermore, the variability of mean temperature reduced species richness, while the variability of total precipitation decreased species asynchrony and community stability. Overall, the present study revealed that species richness and species asynchrony promoted community stability, but increased climate variability may erode these positive effects and thereby threaten community stability.

Changes in climate variability with reference to land quality and agriculture in Scotland.

PubMed

Brown, Iain; Castellazzi, Marie

2015-06-01

Classification and mapping of land capability represents an established format for summarising spatial information on land quality and land-use potential. By convention, this information incorporates bioclimatic constraints through the use of a long-term average. However, climate change means that land capability classification should also have a dynamic temporal component. Using an analysis based upon Land Capability for Agriculture in Scotland, it is shown that this dynamism not only involves the long-term average but also shorter term spatiotemporal patterns, particularly through changes in interannual variability. Interannual and interdecadal variations occur both in the likelihood of land being in prime condition (top three capability class divisions) and in class volatility from year to year. These changing patterns are most apparent in relation to the west-east climatic gradient which is mainly a function of precipitation regime and soil moisture. Analysis is also extended into the future using climate results for the 2050s from a weather generator which show a complex interaction between climate interannual variability and different soil types for land quality. In some locations, variability of land capability is more likely to decrease because the variable climatic constraints are relaxed and the dominant constraint becomes intrinsic soil properties. Elsewhere, climatic constraints will continue to be influential. Changing climate variability has important implications for land-use planning and agricultural management because it modifies local risk profiles in combination with the current trend towards agricultural intensification and specialisation.

Intensified Indian Ocean climate variability during the Last Glacial Maximum

NASA Astrophysics Data System (ADS)

Thirumalai, K.; DiNezro, P.; Tierney, J. E.; Puy, M.; Mohtadi, M.

2017-12-01

Climate models project increased year-to-year climate variability in the equatorial Indian Ocean in response to greenhouse gas warming. This response has been attributed to changes in the mean climate of the Indian Ocean associated with the zonal sea-surface temperature (SST) gradient. According to these studies, air-sea coupling is enhanced due to a stronger SST gradient driving anomalous easterlies that shoal the thermocline in the eastern Indian Ocean. We propose that this relationship between the variability and the zonal SST gradient is consistent across different mean climate states. We test this hypothesis using simulations of past and future climate performed with the Community Earth System Model Version 1 (CESM1). We constrain the realism of the model for the Last Glacial Maximum (LGM) where CESM1 simulates a mean climate consistent with a stronger SST gradient, agreeing with proxy reconstructions. CESM1 also simulates a pronounced increase in seasonal and interannual variability. We develop new estimates of climate variability on these timescales during the LGM using δ18O analysis of individual foraminifera (IFA). IFA data generated from four different cores located in the eastern Indian Ocean indicate a marked increase in δ18O-variance during the LGM as compared to the late Holocene. Such a significant increase in the IFA-δ18O variance strongly supports the modeling simulations. This agreement further supports the dynamics linking year-to-year variability and an altered SST gradient, increasing our confidence in model projections.

Holocene climate variability in Texas, USA: An integration of existing paleoclimate data and modeling with a new, high-resolution speleothem record

USGS Publications Warehouse

Wong, Corinne I.; Banner, Jay L.; Musgrove, MaryLynn

2015-01-01

Delineating the climate processes governing precipitation variability in drought-prone Texas is critical for predicting and mitigating climate change effects, and requires the reconstruction of past climate beyond the instrumental record. We synthesize existing paleoclimate proxy data and climate simulations to provide an overview of climate variability in Texas during the Holocene. Conditions became progressively warmer and drier transitioning from the early to mid Holocene, culminating between 7 and 3 ka (thousand years ago), and were more variable during the late Holocene. The timing and relative magnitude of Holocene climate variability, however, is poorly constrained owing to considerable variability among the different records. To help address this, we present a new speleothem (NBJ) reconstruction from a central Texas cave that comprises the highest resolution proxy record to date, spanning the mid to late Holocene. NBJ trace-element concentrations indicate variable moisture conditions with no clear temporal trend. There is a decoupling between NBJ growth rate, trace-element concentrations, and δ18O values, which indicate that (i) the often direct relation between speleothem growth rate and moisture availability is likely complicated by changes in the overlying ecosystem that affect subsurface CO2 production, and (ii) speleothem δ18O variations likely reflect changes in moisture source (i.e., proportion of Pacific-vs. Gulf of Mexico-derived moisture) that appear not to be linked to moisture amount.

Incorporating abundance information and guiding variable selection for climate-based ensemble forecasting of species' distributional shifts.

PubMed

Tanner, Evan P; Papeş, Monica; Elmore, R Dwayne; Fuhlendorf, Samuel D; Davis, Craig A

2017-01-01

Ecological niche models (ENMs) have increasingly been used to estimate the potential effects of climate change on species' distributions worldwide. Recently, predictions of species abundance have also been obtained with such models, though knowledge about the climatic variables affecting species abundance is often lacking. To address this, we used a well-studied guild (temperate North American quail) and the Maxent modeling algorithm to compare model performance of three variable selection approaches: correlation/variable contribution (CVC), biological (i.e., variables known to affect species abundance), and random. We then applied the best approach to forecast potential distributions, under future climatic conditions, and analyze future potential distributions in light of available abundance data and presence-only occurrence data. To estimate species' distributional shifts we generated ensemble forecasts using four global circulation models, four representative concentration pathways, and two time periods (2050 and 2070). Furthermore, we present distributional shifts where 75%, 90%, and 100% of our ensemble models agreed. The CVC variable selection approach outperformed our biological approach for four of the six species. Model projections indicated species-specific effects of climate change on future distributions of temperate North American quail. The Gambel's quail (Callipepla gambelii) was the only species predicted to gain area in climatic suitability across all three scenarios of ensemble model agreement. Conversely, the scaled quail (Callipepla squamata) was the only species predicted to lose area in climatic suitability across all three scenarios of ensemble model agreement. Our models projected future loss of areas for the northern bobwhite (Colinus virginianus) and scaled quail in portions of their distributions which are currently areas of high abundance. Climatic variables that influence local abundance may not always scale up to influence species' distributions. Special attention should be given to selecting variables for ENMs, and tests of model performance should be used to validate the choice of variables.

Using physiology to understand climate-driven changes in disease and their implications for conservation.

PubMed

Rohr, Jason R; Raffel, Thomas R; Blaustein, Andrew R; Johnson, Pieter T J; Paull, Sara H; Young, Suzanne

2013-01-01

Controversy persists regarding the contributions of climate change to biodiversity losses, through its effects on the spread and emergence of infectious diseases. One of the reasons for this controversy is that there are few mechanistic studies that explore the links among climate change, infectious disease, and declines of host populations. Given that host-parasite interactions are generally mediated by physiological responses, we submit that physiological models could facilitate the prediction of how host-parasite interactions will respond to climate change, and might offer theoretical and terminological cohesion that has been lacking in the climate change-disease literature. We stress that much of the work on how climate influences host-parasite interactions has emphasized changes in climatic means, despite a hallmark of climate change being changes in climatic variability and extremes. Owing to this gap, we highlight how temporal variability in weather, coupled with non-linearities in responses to mean climate, can be used to predict the effects of climate on host-parasite interactions. We also discuss the climate variability hypothesis for disease-related declines, which posits that increased unpredictable temperature variability might provide a temporary advantage to pathogens because they are smaller and have faster metabolisms than their hosts, allowing more rapid acclimatization following a temperature shift. In support of these hypotheses, we provide case studies on the role of climatic variability in host population declines associated with the emergence of the infectious diseases chytridiomycosis, withering syndrome, and malaria. Finally, we present a mathematical model that provides the scaffolding to integrate metabolic theory, physiological mechanisms, and large-scale spatiotemporal processes to predict how simultaneous changes in climatic means, variances, and extremes will affect host-parasite interactions. However, several outstanding questions remain to be answered before investigators can accurately predict how changes in climatic means and variances will affect infectious diseases and the conservation status of host populations.

Using physiology to understand climate-driven changes in disease and their implications for conservation

PubMed Central

Rohr, Jason R.; Raffel, Thomas R.; Blaustein, Andrew R.; Johnson, Pieter T. J.; Paull, Sara H.; Young, Suzanne

2013-01-01

Controversy persists regarding the contributions of climate change to biodiversity losses, through its effects on the spread and emergence of infectious diseases. One of the reasons for this controversy is that there are few mechanistic studies that explore the links among climate change, infectious disease, and declines of host populations. Given that host–parasite interactions are generally mediated by physiological responses, we submit that physiological models could facilitate the prediction of how host–parasite interactions will respond to climate change, and might offer theoretical and terminological cohesion that has been lacking in the climate change–disease literature. We stress that much of the work on how climate influences host–parasite interactions has emphasized changes in climatic means, despite a hallmark of climate change being changes in climatic variability and extremes. Owing to this gap, we highlight how temporal variability in weather, coupled with non-linearities in responses to mean climate, can be used to predict the effects of climate on host–parasite interactions. We also discuss the climate variability hypothesis for disease-related declines, which posits that increased unpredictable temperature variability might provide a temporary advantage to pathogens because they are smaller and have faster metabolisms than their hosts, allowing more rapid acclimatization following a temperature shift. In support of these hypotheses, we provide case studies on the role of climatic variability in host population declines associated with the emergence of the infectious diseases chytridiomycosis, withering syndrome, and malaria. Finally, we present a mathematical model that provides the scaffolding to integrate metabolic theory, physiological mechanisms, and large-scale spatiotemporal processes to predict how simultaneous changes in climatic means, variances, and extremes will affect host–parasite interactions. However, several outstanding questions remain to be answered before investigators can accurately predict how changes in climatic means and variances will affect infectious diseases and the conservation status of host populations. PMID:27293606

Disease and thermal acclimation in a more variable and unpredictable climate

NASA Astrophysics Data System (ADS)

Raffel, Thomas R.; Romansic, John M.; Halstead, Neal T.; McMahon, Taegan A.; Venesky, Matthew D.; Rohr, Jason R.

2013-02-01

Global climate change is shifting the distribution of infectious diseases of humans and wildlife with potential adverse consequences for disease control. As well as increasing mean temperatures, climate change is expected to increase climate variability, making climate less predictable. However, few empirical or theoretical studies have considered the effects of climate variability or predictability on disease, despite it being likely that hosts and parasites will have differential responses to climatic shifts. Here we present a theoretical framework for how temperature variation and its predictability influence disease risk by affecting host and parasite acclimation responses. Laboratory experiments conducted in 80 independent incubators, and field data on disease-associated frog declines in Latin America, support the framework and provide evidence that unpredictable temperature fluctuations, on both monthly and diurnal timescales, decrease frog resistance to the pathogenic chytrid fungus Batrachochytrium dendrobatidis. Furthermore, the pattern of temperature-dependent growth of the fungus on frogs was opposite to the pattern of growth in culture, emphasizing the importance of accounting for the host-parasite interaction when predicting climate-dependent disease dynamics. If similar acclimation responses influence other host-parasite systems, as seems likely, then present models, which generally ignore small-scale temporal variability in climate, might provide poor predictions for climate effects on disease.

Forest productivity in southwestern Europe is controlled by coupled North Atlantic and Atlantic Multidecadal Oscillations.

PubMed

Madrigal-González, Jaime; Ballesteros-Cánovas, Juan A; Herrero, Asier; Ruiz-Benito, Paloma; Stoffel, Markus; Lucas-Borja, Manuel E; Andivia, Enrique; Sancho-García, Cesar; Zavala, Miguel A

2017-12-20

The North Atlantic Oscillation (NAO) depicts annual and decadal oscillatory modes of variability responsible for dry spells over the European continent. The NAO therefore holds a great potential to evaluate the role, as carbon sinks, of water-limited forests under climate change. However, uncertainties related to inconsistent responses of long-term forest productivity to NAO have so far hampered firm conclusions on its impacts. We hypothesize that, in part, such inconsistencies might have their origin in periodical sea surface temperature anomalies in the Atlantic Ocean (i.e., Atlantic Multidecadal Oscillation, AMO). Here we show strong empirical evidence in support of this hypothesis using 120 years of periodical inventory data from Iberian pine forests. Our results point to AMO + NAO + and AMO - NAO - phases as being critical for forest productivity, likely due to decreased winter water balance and abnormally low winter temperatures, respectively. Our findings could be essential for the evaluation of ecosystem functioning vulnerabilities associated with increased climatic anomalies under unprecedented warming conditions in the Mediterranean.

Integrated remote sensing for multi-temporal analysis of urban land cover-climate interactions

NASA Astrophysics Data System (ADS)

Savastru, Dan M.; Zoran, Maria A.; Savastru, Roxana S.

2016-08-01

Climate change is considered to be the biggest environmental threat in the future in the South- Eastern part of Europe. In frame of predicted global warming, urban climate is an important issue in scientific research. Surface energy processes have an essential role in urban weather, climate and hydrosphere cycles, as well in urban heat redistribution. This paper investigated the influences of urban growth on thermal environment in relationship with other biophysical variables in Bucharest metropolitan area of Romania. Remote sensing data from Landsat TM/ETM+ and time series MODIS Terra/Aqua sensors have been used to assess urban land cover- climate interactions over period between 2000 and 2015 years. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also analyzed in relation with the Normalized Difference Vegetation Index (NDVI) at city level. Based on these parameters, the urban growth, and urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. The correlation analyses revealed that, at the pixel-scale, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. This analysis provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.

Impacts of climate change on marine organisms and ecosystems.

PubMed

Brierley, Andrew S; Kingsford, Michael J

2009-07-28

Human activities are releasing gigatonnes of carbon to the Earth's atmosphere annually. Direct consequences of cumulative post-industrial emissions include increasing global temperature, perturbed regional weather patterns, rising sea levels, acidifying oceans, changed nutrient loads and altered ocean circulation. These and other physical consequences are affecting marine biological processes from genes to ecosystems, over scales from rock pools to ocean basins, impacting ecosystem services and threatening human food security. The rates of physical change are unprecedented in some cases. Biological change is likely to be commensurately quick, although the resistance and resilience of organisms and ecosystems is highly variable. Biological changes founded in physiological response manifest as species range-changes, invasions and extinctions, and ecosystem regime shifts. Given the essential roles that oceans play in planetary function and provision of human sustenance, the grand challenge is to intervene before more tipping points are passed and marine ecosystems follow less-buffered terrestrial systems further down a spiral of decline. Although ocean bioengineering may alleviate change, this is not without risk. The principal brake to climate change remains reduced CO(2) emissions that marine scientists and custodians of the marine environment can lobby for and contribute to. This review describes present-day climate change, setting it in context with historical change, considers consequences of climate change for marine biological processes now and in to the future, and discusses contributions that marine systems could play in mitigating the impacts of global climate change.

Internal Variability-Generated Uncertainty in East Asian Climate Projections Estimated with 40 CCSM3 Ensembles.

PubMed

Yao, Shuai-Lei; Luo, Jing-Jia; Huang, Gang

2016-01-01

Regional climate projections are challenging because of large uncertainty particularly stemming from unpredictable, internal variability of the climate system. Here, we examine the internal variability-induced uncertainty in precipitation and surface air temperature (SAT) trends during 2005-2055 over East Asia based on 40 member ensemble projections of the Community Climate System Model Version 3 (CCSM3). The model ensembles are generated from a suite of different atmospheric initial conditions using the same SRES A1B greenhouse gas scenario. We find that projected precipitation trends are subject to considerably larger internal uncertainty and hence have lower confidence, compared to the projected SAT trends in both the boreal winter and summer. Projected SAT trends in winter have relatively higher uncertainty than those in summer. Besides, the lower-level atmospheric circulation has larger uncertainty than that in the mid-level. Based on k-means cluster analysis, we demonstrate that a substantial portion of internally-induced precipitation and SAT trends arises from internal large-scale atmospheric circulation variability. These results highlight the importance of internal climate variability in affecting regional climate projections on multi-decadal timescales.

Satellite Sensed Skin Sea Surface Temperature

NASA Technical Reports Server (NTRS)

Donlon, Craig

1997-01-01

Quantitative predictions of spatial and temporal changes the global climate rely heavily on the use of computer models. Unfortunately, such models cannot provide the basis for climate prediction because key physical processes are inadequately treated. Consequently, fine tuning procedures are often used to optimize the fit between model output and observational data and the validation of climate models using observations is essential if model based predictions of climate change are to be treated with any degree of confidence. Satellite Sea Surface Temperature (SST) observations provide high spatial and temporal resolution data which is extremely well suited to the initialization, definition of boundary conditions and, validation of climate models. In the case of coupled ocean-atmosphere models, the SST (or more correctly the 'Skin' SST (SSST)) is a fundamental diagnostic variable to consider in the validation process. Daily global SST maps derived from satellite sensors also provide adequate data for the detection of global patterns of change which, unlike any other SST data set, repeatedly extend into the southern hemisphere extra-tropical regions. Such data are essential to the success of the spatial 'fingerprint' technique, which seeks to establish a north-south asymmetry where warming is suppressed in the high latitude Southern Ocean. Some estimates suggest that there is a greater than 80% chance of directly detecting significant change (97.5 % confidence level) after 10-12 years of consistent global observations of mean sea surface temperature. However, these latter statements should be qualified with the assumption that a negligible drift in the observing system exists and that biases between individual instruments required to derive a long term data set are small. Given that current estimates for the magnitude of global warming of 0.015 K yr(sup -1) - 0.025 K yr(sup -1), satellite SST data sets need to be both accurate and stable if such a warming trend is to be confidently detected. Some of these activities are focussed to develop and deploy instrumentation suitable for the collection of precise in situ measurements of the SSST which can be used to improve the accuracy of satellite measurements, while others develop techniques to generate improved global analyses of sea surface temperature using historical data.

Final Report: Closeout of the Award NO. DE-FG02-98ER62618 (M.S. Fox-Rabinovitz, P.I.)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fox-Rabinovitz, M. S.

The final report describes the study aimed at exploring the variable-resolution stretched-grid (SG) approach to decadal regional climate modeling using advanced numerical techniques. The obtained results have shown that variable-resolution SG-GCMs using stretched grids with fine resolution over the area(s) of interest, is a viable established approach to regional climate modeling. The developed SG-GCMs have been extensively used for regional climate experimentation. The SG-GCM simulations are aimed at studying the U.S. regional climate variability with an emphasis on studying anomalous summer climate events, the U.S. droughts and floods.

Impacts of uncertainties in European gridded precipitation observations on regional climate analysis

PubMed Central

Gobiet, Andreas

2016-01-01

ABSTRACT Gridded precipitation data sets are frequently used to evaluate climate models or to remove model output biases. Although precipitation data are error prone due to the high spatio‐temporal variability of precipitation and due to considerable measurement errors, relatively few attempts have been made to account for observational uncertainty in model evaluation or in bias correction studies. In this study, we compare three types of European daily data sets featuring two Pan‐European data sets and a set that combines eight very high‐resolution station‐based regional data sets. Furthermore, we investigate seven widely used, larger scale global data sets. Our results demonstrate that the differences between these data sets have the same magnitude as precipitation errors found in regional climate models. Therefore, including observational uncertainties is essential for climate studies, climate model evaluation, and statistical post‐processing. Following our results, we suggest the following guidelines for regional precipitation assessments. (1) Include multiple observational data sets from different sources (e.g. station, satellite, reanalysis based) to estimate observational uncertainties. (2) Use data sets with high station densities to minimize the effect of precipitation undersampling (may induce about 60% error in data sparse regions). The information content of a gridded data set is mainly related to its underlying station density and not to its grid spacing. (3) Consider undercatch errors of up to 80% in high latitudes and mountainous regions. (4) Analyses of small‐scale features and extremes are especially uncertain in gridded data sets. For higher confidence, use climate‐mean and larger scale statistics. In conclusion, neglecting observational uncertainties potentially misguides climate model development and can severely affect the results of climate change impact assessments. PMID:28111497

Range-wide effects of breeding- and nonbreeding-season climate on the abundance of a Neotropical migrant songbird.

PubMed

Wilson, Scott; LaDeau, Shannon L; Tøttrup, Anders P; Marra, Peter P

2011-09-01

Geographic variation in the population dynamics of a species can result from regional variability in climate and how it affects reproduction and survival. Identifying such effects for migratory birds requires the integration of population models with knowledge of migratory connectivity between breeding and nonbreeding areas. We used Bayesian hierarchical models with 26 years of Breeding Bird Survey data (1982-2007) to investigate the impacts of breeding- and nonbreeding-season climate on abundance of American Redstarts (Setophaga ruticilla) across the species range. We focused on 15 populations defined by Bird Conservation Regions, and we included variation across routes and observers as well as temporal trends and climate effects. American Redstart populations that breed in eastern North America showed increased abundance following winters with higher plant productivity in the Caribbean where they are expected to overwinter. In contrast, western breeding populations showed little response to conditions in their expected wintering areas in west Mexico, perhaps reflecting lower migratory connectivity or differential effects of winter rainfall on individuals across the species range. Unlike the case with winter climate, we found few effects of temperature prior to arrival in spring (March-April) or during the nesting period (May-June) on abundance the following year. Eight populations showed significant changes in abundance, with the steepest declines in the Atlantic Northern Forest (-3.4%/yr) and the greatest increases in the Prairie Hardwood Transition (4%/yr). This study emphasizes how the effects of climate on populations of migratory birds are context dependent and can vary depending on geographic location and the period of the annual cycle. Such knowledge is essential for predicting regional variation in how populations of a species might vary in their response to climate change.

The relationship between species richness and aboveground biomass in a primary Pinus kesiya forest of Yunnan, southwestern China.

PubMed

Li, Shuaifeng; Lang, Xuedong; Liu, Wande; Ou, Guanglong; Xu, Hui; Su, Jianrong

2018-01-01

The relationship between biodiversity and biomass is an essential element of the natural ecosystem functioning. Our research aims at assessing the effects of species richness on the aboveground biomass and the ecological driver of this relationship in a primary Pinus kesiya forest. We sampled 112 plots of the primary P. kesiya forests in Yunnan Province. The general linear model and the structural equation model were used to estimate relative effects of multivariate factors among aboveground biomass, species richness and the other explanatory variables, including climate moisture index, soil nutrient regime and stand age. We found a positive linear regression relationship between the species richness and aboveground biomass using ordinary least squares regressions. The species richness and soil nutrient regime had no direct significant effect on aboveground biomass. However, the climate moisture index and stand age had direct effects on aboveground biomass. The climate moisture index could be a better link to mediate the relationship between species richness and aboveground biomass. The species richness affected aboveground biomass which was mediated by the climate moisture index. Stand age had direct and indirect effects on aboveground biomass through the climate moisture index. Our results revealed that climate moisture index had a positive feedback in the relationship between species richness and aboveground biomass, which played an important role in a link between biodiversity maintenance and ecosystem functioning. Meanwhile, climate moisture index not only affected positively on aboveground biomass, but also indirectly through species richness. The information would be helpful in understanding the biodiversity-aboveground biomass relationship of a primary P. kesiya forest and for forest management.

The relationship between species richness and aboveground biomass in a primary Pinus kesiya forest of Yunnan, southwestern China

PubMed Central

Li, Shuaifeng; Lang, Xuedong; Liu, Wande; Ou, Guanglong; Xu, Hui

2018-01-01

The relationship between biodiversity and biomass is an essential element of the natural ecosystem functioning. Our research aims at assessing the effects of species richness on the aboveground biomass and the ecological driver of this relationship in a primary Pinus kesiya forest. We sampled 112 plots of the primary P. kesiya forests in Yunnan Province. The general linear model and the structural equation model were used to estimate relative effects of multivariate factors among aboveground biomass, species richness and the other explanatory variables, including climate moisture index, soil nutrient regime and stand age. We found a positive linear regression relationship between the species richness and aboveground biomass using ordinary least squares regressions. The species richness and soil nutrient regime had no direct significant effect on aboveground biomass. However, the climate moisture index and stand age had direct effects on aboveground biomass. The climate moisture index could be a better link to mediate the relationship between species richness and aboveground biomass. The species richness affected aboveground biomass which was mediated by the climate moisture index. Stand age had direct and indirect effects on aboveground biomass through the climate moisture index. Our results revealed that climate moisture index had a positive feedback in the relationship between species richness and aboveground biomass, which played an important role in a link between biodiversity maintenance and ecosystem functioning. Meanwhile, climate moisture index not only affected positively on aboveground biomass, but also indirectly through species richness. The information would be helpful in understanding the biodiversity-aboveground biomass relationship of a primary P. kesiya forest and for forest management. PMID:29324901

Evaluating the Contribution of Natural Variability and Climate Model Response to Uncertainty in Projections of Climate Change Impacts on U.S. Air Quality

EPA Science Inventory

We examine the effects of internal variability and model response in projections of climate impacts on U.S. ground-level ozone across the 21st century using integrated global system modeling and global atmospheric chemistry simulations. The impact of climate change on air polluti...

Anomalously weak Labrador Sea convection and Atlantic overturning during the past 150 years.

PubMed

Thornalley, David J R; Oppo, Delia W; Ortega, Pablo; Robson, Jon I; Brierley, Chris M; Davis, Renee; Hall, Ian R; Moffa-Sanchez, Paola; Rose, Neil L; Spooner, Peter T; Yashayaev, Igor; Keigwin, Lloyd D

2018-04-01

The Atlantic meridional overturning circulation (AMOC) is a system of ocean currents that has an essential role in Earth's climate, redistributing heat and influencing the carbon cycle 1, 2 . The AMOC has been shown to be weakening in recent years 1 ; this decline may reflect decadal-scale variability in convection in the Labrador Sea, but short observational datasets preclude a longer-term perspective on the modern state and variability of Labrador Sea convection and the AMOC 1, 3-5 . Here we provide several lines of palaeo-oceanographic evidence that Labrador Sea deep convection and the AMOC have been anomalously weak over the past 150 years or so (since the end of the Little Ice Age, LIA, approximately AD 1850) compared with the preceding 1,500 years. Our palaeoclimate reconstructions indicate that the transition occurred either as a predominantly abrupt shift towards the end of the LIA, or as a more gradual, continued decline over the past 150 years; this ambiguity probably arises from non-AMOC influences on the various proxies or from the different sensitivities of these proxies to individual components of the AMOC. We suggest that enhanced freshwater fluxes from the Arctic and Nordic seas towards the end of the LIA-sourced from melting glaciers and thickened sea ice that developed earlier in the LIA-weakened Labrador Sea convection and the AMOC. The lack of a subsequent recovery may have resulted from hysteresis or from twentieth-century melting of the Greenland Ice Sheet 6 . Our results suggest that recent decadal variability in Labrador Sea convection and the AMOC has occurred during an atypical, weak background state. Future work should aim to constrain the roles of internal climate variability and early anthropogenic forcing in the AMOC weakening described here.

The Copernicus programme and its Climate Change Service (C3S): a European answer to Climate Change

NASA Astrophysics Data System (ADS)

Pinty, Bernard; Thepaut, Jean-Noel; Dee, Dick

2016-07-01

In November 2014, The European Centre for Medium-range Weather Forecasts (ECMWF) signed an agreement with the European Commission to deliver two of the Copernicus Earth Observation Programme Services on the Commission's behalf. The ECMWF delivered services - the Copernicus Climate Change Service (C3S) and Atmosphere Monitoring Service (CAMS) - will bring a consistent standard to how we measure and predict atmospheric conditions and climate change. They will maximise the potential of past, current and future earth observations - ground, ocean, airborne, satellite - and analyse these to monitor and predict atmospheric conditions and in the future, climate change. With the wealth of free and open data that the services provide, they will help business users to assess the impact of their business decisions and make informed choices, delivering a more energy efficient and climate aware economy. These sound investment decisions now will not only stimulate growth in the short term, but reduce the impact of climate change on the economy and society in the future. C3S is in its proof of concept phase and through its climate data store will provide global and regional climate data reanalyses; multi-model seasonal forecasts; customisable visual data to enable examination of wide range of scenarios and model the impact of changes; access to all the underlying data, including climate data records from various satellite and in-situ observations. In addition, C3S will provide key indicators on climate change drivers (such as carbon dioxide) and impacts (such as reducing glaciers). The aim of these indicators will be to support European adaptation and mitigation policies in a number of economic sectors. The presentation will provide an overview of this newly created Service, its various components and activities, and a roadmap towards achieving a fully operational European Climate Service at the horizon 2019-2020. It will focus on the requirements for quality-assured Observation Gridded Products to establish an operational delivery of a series of gridded long-term Climate Data Records (CDRs) of Essential Climate Variables (ECVs), along with associated input data and uncertainty estimation.

An Addendum to "A New Tool for Climatic Analysis Using Köppen Climate Classification"

ERIC Educational Resources Information Center

Larson, Paul R.; Lohrengel, C. Frederick, II

2014-01-01

The Köppen climatic classification system in a modified format is the most widely applied system in use today. Mapping and analysis of hundreds of arid and semiarid climate stations has made the use of the additional fourth letter in BW/BS climates essential. The addition of "s," "w," or "f" to the standard…

Decomposition of the complex system into nonlinear spatio-temporal modes: algorithm and application to climate data mining

NASA Astrophysics Data System (ADS)

Feigin, Alexander; Gavrilov, Andrey; Loskutov, Evgeny; Mukhin, Dmitry

2015-04-01

Proper decomposition of the complex system into well separated "modes" is a way to reveal and understand the mechanisms governing the system behaviour as well as discover essential feedbacks and nonlinearities. The decomposition is also natural procedure that provides to construct adequate and concurrently simplest models of both corresponding sub-systems, and of the system in whole. In recent works two new methods of decomposition of the Earth's climate system into well separated modes were discussed. The first method [1-3] is based on the MSSA (Multichannel Singular Spectral Analysis) [4] for linear expanding vector (space-distributed) time series and makes allowance delayed correlations of the processes recorded in spatially separated points. The second one [5-7] allows to construct nonlinear dynamic modes, but neglects delay of correlations. It was demonstrated [1-3] that first method provides effective separation of different time scales, but prevent from correct reduction of data dimension: slope of variance spectrum of spatio-temporal empirical orthogonal functions that are "structural material" for linear spatio-temporal modes, is too flat. The second method overcomes this problem: variance spectrum of nonlinear modes falls essentially sharply [5-7]. However neglecting time-lag correlations brings error of mode selection that is uncontrolled and increases with growth of mode time scale. In the report we combine these two methods in such a way that the developed algorithm allows constructing nonlinear spatio-temporal modes. The algorithm is applied for decomposition of (i) multi hundreds years globally distributed data generated by the INM RAS Coupled Climate Model [8], and (ii) 156 years time series of SST anomalies distributed over the globe [9]. We compare efficiency of different methods of decomposition and discuss the abilities of nonlinear spatio-temporal modes for construction of adequate and concurrently simplest ("optimal") models of climate systems. 1. Feigin A.M., Mukhin D., Gavrilov A., Volodin E.M., and Loskutov E.M. (2013) "Separation of spatial-temporal patterns ("climatic modes") by combined analysis of really measured and generated numerically vector time series", AGU 2013 Fall Meeting, Abstract NG33A-1574. 2. Alexander Feigin, Dmitry Mukhin, Andrey Gavrilov, Evgeny Volodin, and Evgeny Loskutov (2014) "Approach to analysis of multiscale space-distributed time series: separation of spatio-temporal modes with essentially different time scales", Geophysical Research Abstracts, Vol. 16, EGU2014-6877. 3. Dmitry Mukhin, Dmitri Kondrashov, Evgeny Loskutov, Andrey Gavrilov, Alexander Feigin, and Michael Ghil (2014) "Predicting critical transitions in ENSO models, Part II: Spatially dependent models", Journal of Climate (accepted, doi: 10.1175/JCLI-D-14-00240.1). 4. Ghil, M., R. M. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, et al. (2002) "Advanced spectral methods for climatic time series", Rev. Geophys. 40(1), 3.1-3.41. 5. Dmitry Mukhin, Andrey Gavrilov, Evgeny M Loskutov and Alexander M Feigin (2014) "Nonlinear Decomposition of Climate Data: a New Method for Reconstruction of Dynamical Modes", AGU 2014 Fall Meeting, Abstract NG43A-3752. 6. Andrey Gavrilov, Dmitry Mukhin, Evgeny Loskutov, and Alexander Feigin (2015) "Empirical decomposition of climate data into nonlinear dynamic modes", Geophysical Research Abstracts, Vol. 17, EGU2015-627. 7. Dmitry Mukhin, Andrey Gavrilov, Evgeny Loskutov, Alexander Feigin, and Juergen Kurths (2015) "Reconstruction of principal dynamical modes from climatic variability: nonlinear approach", Geophysical Research Abstracts, Vol. 17, EGU2015-5729. 8. http://83.149.207.89/GCM_DATA_PLOTTING/GCM_INM_DATA_XY_en.htm. 9. http://iridl.ldeo.columbia.edu/SOURCES/.KAPLAN/.EXTENDED/.v2/.ssta/.

Effects of model spatial resolution on ecohydrologic predictions and their sensitivity to inter-annual climate variability

Treesearch

Kyongho Son; Christina Tague; Carolyn Hunsaker

2016-01-01

The effect of fine-scale topographic variability on model estimates of ecohydrologic responses to climate variability in California’s Sierra Nevada watersheds has not been adequately quantified and may be important for supporting reliable climate-impact assessments. This study tested the effect of digital elevation model (DEM) resolution on model accuracy and estimates...

The effects of precipitation variability on C4 photosynthesis, net primary production and soil respiration in a Chihuahuan desert grassland

Treesearch

Michell L. Thomey

2012-01-01

Although the Earth's climate system has always been inherently variable, the magnitude and rate of anthropogenic climate change is subjecting ecosystems and the populations that they contain to novel environmental conditions. Because water is the most limiting resource, arid-semiarid ecosystems are likely to be highly responsive to future climate variability. The...

Interannual and spatial variability of maple syrup yield as related to climatic factors

PubMed Central

Houle, Daniel

2014-01-01

Sugar maple syrup production is an important economic activity for eastern Canada and the northeastern United States. Since annual variations in syrup yield have been related to climate, there are concerns about the impacts of climatic change on the industry in the upcoming decades. Although the temporal variability of syrup yield has been studied for specific sites on different time scales or for large regions, a model capable of accounting for both temporal and regional differences in yield is still lacking. In the present study, we studied the factors responsible for interregional and interannual variability in maple syrup yield over the 2001–2012 period, by combining the data from 8 Quebec regions (Canada) and 10 U.S. states. The resulting model explained 44.5% of the variability in yield. It includes the effect of climatic conditions that precede the sapflow season (variables from the previous growing season and winter), the effect of climatic conditions during the current sapflow season, and terms accounting for intercountry and temporal variability. Optimal conditions for maple syrup production appear to be spatially restricted by less favourable climate conditions occurring during the growing season in the north, and in the south, by the warmer winter and earlier spring conditions. This suggests that climate change may favor maple syrup production northwards, while southern regions are more likely to be negatively affected by adverse spring conditions. PMID:24949244

Effects of climate change and variability on population dynamics in a long-lived shorebird.

PubMed

van de Pol, Martijn; Vindenes, Yngvild; Saether, Bernt-Erik; Engen, Steinar; Ens, Bruno J; Oosterbeek, Kees; Tinbergen, Joost M

2010-04-01

Climate change affects both the mean and variability of climatic variables, but their relative impact on the dynamics of populations is still largely unexplored. Based on a long-term study of the demography of a declining Eurasian Oystercatcher (Haematopus ostralegus) population, we quantify the effect of changes in mean and variance of winter temperature on different vital rates across the life cycle. Subsequently, we quantify, using stochastic stage-structured models, how changes in the mean and variance of this environmental variable affect important characteristics of the future population dynamics, such as the time to extinction. Local mean winter temperature is predicted to strongly increase, and we show that this is likely to increase the population's persistence time via its positive effects on adult survival that outweigh the negative effects that higher temperatures have on fecundity. Interannual variation in winter temperature is predicted to decrease, which is also likely to increase persistence time via its positive effects on adult survival that outweigh the negative effects that lower temperature variability has on fecundity. Overall, a 0.1 degrees C change in mean temperature is predicted to alter median time to extinction by 1.5 times as many years as would a 0.1 degrees C change in the standard deviation in temperature, suggesting that the dynamics of oystercatchers are more sensitive to changes in the mean than in the interannual variability of this climatic variable. Moreover, as climate models predict larger changes in the mean than in the standard deviation of local winter temperature, the effects of future climatic variability on this population's time to extinction are expected to be overwhelmed by the effects of changes in climatic means. We discuss the mechanisms by which climatic variability can either increase or decrease population viability and how this might depend both on species' life histories and on the vital rates affected. This study illustrates that, for making reliable inferences about population consequences in species in which life history changes with age or stage, it is crucial to investigate the impact of climate change on vital rates across the entire life cycle. Disturbingly, such data are unavailable for most species of conservation concern.

A strategic outlook for coordination of ground-based measurement networks of atmospheric state variables and atmospheric composition

NASA Astrophysics Data System (ADS)

Bodeker, G. E.; Thorne, P.; Braathen, G.; De Maziere, M.; Thompson, A. M.; Kurylo, M. J., III

2016-12-01

There are a number of ground-based global observing networks that collectively aim to make key measurements of atmospheric state variables and atmospheric chemical composition. These networks include, but are not limited to:NDACC: Network for the Detection of Atmospheric Composition Change GUAN: GCOS Upper Air Network GRUAN: GCOS Reference Upper Air Network EARLINET: the European Aerosol Research Lidar Network GAW: Global Atmosphere Watch SHADOZ: Southern Hemisphere ADditional OZonesondes TCCON: Total Carbon Column Observing Network BSRN: Baseline Surface Radiation Network While each network brings unique capabilities to the global observing system, there are many instances where the activities and capabilities of the networks overlap. These commonalities across multiple networks can confound funding agencies when allocating scarce financial resources. Overlaps between networks may also result in some duplication of effort and a resultant sub-optimal use of funding resource for the global observing system. While some degree of overlap is useful for quality assurance, it is essential to identify the degree to which one network can take on a specific responsibility on behalf of all other networks to avoid unnecessary duplication, to identify where expertise in any one network may serve other networks, and to develop a long-term strategy for the evolution of these networks that clarifies to funding agencies where new investment is required. This presentation will briefly summarise the key characteristics of each network listed above, adopt a matrix approach to identify commonalities and, in particular, where there may be a danger of duplication of effort, and where gaps between the networks may be compromising the services that these networks are expected to collectively deliver to the global atmospheric and climate science research communities. The presentation will also examine where sharing of data and tools between networks may result in a more efficient delivery of records of essential climate variables to the global research community. There are aspects of underpinning research that are needed across all of these networks, such as laboratory spectroscopy, that often do not receive the attention they deserve. The presentation will also seek to identify where that underpinning research is lacking.

Updating beliefs and combining evidence in adaptive forest management under climate change: a case study of Norway spruce (Picea abies L. Karst) in the Black Forest, Germany.

PubMed

Yousefpour, Rasoul; Temperli, Christian; Bugmann, Harald; Elkin, Che; Hanewinkel, Marc; Meilby, Henrik; Jacobsen, Jette Bredahl; Thorsen, Bo Jellesmark

2013-06-15

We study climate uncertainty and how managers' beliefs about climate change develop and influence their decisions. We develop an approach for updating knowledge and beliefs based on the observation of forest and climate variables and illustrate its application for the adaptive management of an even-aged Norway spruce (Picea abies L. Karst) forest in the Black Forest, Germany. We simulated forest development under a range of climate change scenarios and forest management alternatives. Our analysis used Bayesian updating and Dempster's rule of combination to simulate how observations of climate and forest variables may influence a decision maker's beliefs about climate development and thereby management decisions. While forest managers may be inclined to rely on observed forest variables to infer climate change and impacts, we found that observation of climate state, e.g. temperature or precipitation is superior for updating beliefs and supporting decision-making. However, with little conflict among information sources, the strongest evidence would be offered by a combination of at least two informative variables, e.g., temperature and precipitation. The success of adaptive forest management depends on when managers switch to forward-looking management schemes. Thus, robust climate adaptation policies may depend crucially on a better understanding of what factors influence managers' belief in climate change. Copyright © 2013 Elsevier Ltd. All rights reserved.

Pollen-based reconstruction of Holocene climate variability in the Eifel region evaluated with stable isotopes

NASA Astrophysics Data System (ADS)

Kühl, Norbert; Moschen, Robert; Wagner, Stefanie

2010-05-01

Pollen as well as stable isotopes have great potential as climate proxy data. While variability in these proxy data is frequently assumed to reflect climate variability, other factors than climate, including human impact and statistical noise, can often not be excluded as primary cause for the observed variability. Multiproxy studies offer the opportunity to test different drivers by providing different lines of evidence for environmental change such as climate variability and human impact. In this multiproxy study we use pollen and peat humification to evaluate to which extent stable oxygen and carbon isotope series from the peat bog "Dürres Maar" reflect human impact rather than climate variability. For times before strong anthropogenic vegetation change, isotope series from Dürres Maar were used to validate quantitative reconstructions based on pollen. Our study site is the kettle hole peat bog "Dürres Maar" in the Eifel low mountain range, Germany (450m asl), which grew 12m during the last 10,000 years. Pollen was analysed with a sum of at least 1000 terrestrial pollen grains throughout the profile to minimize statistical effects on the reconstructions. A recently developed probabilistic indicator taxa method ("pdf-method") was used for the quantitative climate estimates (January and July temperature) based on pollen. For isotope analysis, attention was given to use monospecific Sphagnum leaves whenever possible, reducing the potential of a species effect and any potential artefact that can originate from selective degradation of different morphological parts of Sphagnum plants (Moschen et al., 2009). Pollen at "Dürres Maar" reflect the variable and partly strong human impact on vegetation during the last 4000 years. Stable isotope time series were apparently not influenced by human impact at this site. This highlights the potential of stable isotope investigations from peat for climatic interpretation, because stable isotope series from lacustrine sediments might strongly react to anthropogenic deforestation, as carbon isotope time series from the adjacent Lake Holzmaar suggest. Reconstructions based on pollen with the pdf-method are robust to the human impact during the last 4000 years, but do not reproduce the fine scale climate variability that can be derived from the stable isotope series (Kühl et al., in press). In contrast, reconstructions on the basis of pollen data show relatively pronounced climate variability (here: January temperature) during the Mid-Holocene, which is known from many other European records. The oxygen isotope time series as available now indicate that at least some of the observed variability indeed reflects climate variability. However, stable carbon isotopes show little concordance. At this stage our results point in the direction that 1) the isotopic composition might reflect a shift in influencing factors during the Holocene, 2) climate trends can robustly be reconstructed with the pdf method and 3) fine scale climate variability can potentially be reconstructed using the pdf-method, given that climate sensitive taxa at their distribution limit are present. The latter two conclusions are of particular importance for the reconstruction of climatic trends and variability of interglacials older than the Holocene, when sites are rare and pollen is often the only suitable proxy in terrestrial records. Kühl, N., Moschen, R., Wagner, S., Brewer, S., Peyron, O., in press. A multiproxy record of Late Holocene natural and anthropogenic environmental change from the Sphagnum peat bog Dürres Maar, Germany: implications for quantitative climate reconstructions based on pollen. J. Quat. Sci., DOI: 10.1002/jqs.1342. Available online. Moschen, R., Kühl, N., Rehberger, I., Lücke, A., 2009. Stable carbon and oxygen isotopes in sub-fossil Sphagnum: Assessment of their applicability for palaeoclimatology. Chemical Geology 259, 262-272.

Short-term climate change impacts on Mara basin hydrology

NASA Astrophysics Data System (ADS)

Demaria, E. M.; Roy, T.; Valdés, J. B.; Lyon, B.; Valdés-Pineda, R.; Serrat-Capdevila, A.; Durcik, M.; Gupta, H.

2017-12-01

The predictability of climate diminishes significantly at shorter time scales (e.g. decadal). Both natural variability as well as sampling variability of climate can obscure or enhance climate change signals in these shorter scales. Therefore, in order to assess the impacts of climate change on basin hydrology, it is important to design climate projections with exhaustive climate scenarios. In this study, we first create seasonal climate scenarios by combining (1) synthetic precipitation projections generated from a Vector Auto-Regressive (VAR) model using the University of East Anglia Climate Research Unit (UEA-CRU) data with (2) seasonal trends calculated from 31 models in the Coupled Model Intercomparison Project Phase 5 (CMIP). The seasonal climate projections are then disaggregated to daily level using the Agricultural Modern-Era Retrospective Analysis for Research and Applications (AgMERRA) data. The daily climate data are then bias-corrected and used as forcings to the land-surface model, Variable Infiltration Capacity (VIC), to generate different hydrological projections for the Mara River basin in East Africa, which are then evaluated to study the hydrologic changes in the basin in the next three decades (2020-2050).

Contributions of meteorology to the phenology of cyanobacterial blooms: implications for future climate change.

PubMed

Zhang, Min; Duan, Hongtao; Shi, Xiaoli; Yu, Yang; Kong, Fanxiang

2012-02-01

Cyanobacterial blooms are often a result of eutrophication. Recently, however, their expansion has also been found to be associated with changes in climate. To elucidate the effects of climatic variables on the expansion of cyanobacterial blooms in Taihu, China, we analyzed the relationships between climatic variables and bloom events which were retrieved by satellite images. We then assessed the contribution of each climate variable to the phenology of blooms using multiple regression models. Our study demonstrates that retrieving ecological information from satellite images is meritorious for large-scale and long-term ecological research in freshwater ecosystems. Our results show that the phenological changes of blooms at an inter-annual scale are strongly linked to climate in Taihu during the past 23 yr. Cyanobacterial blooms occur earlier and last longer with the increase of temperature, sunshine hours, and global radiation and the decrease of wind speed. Furthermore, the duration increases when the daily averages of maximum, mean, and minimum temperature each exceed 20.3 °C, 16.7 °C, and 13.7 °C, respectively. Among these factors, sunshine hours and wind speed are the primary contributors to the onset of the blooms, explaining 84.6% of their variability over the past 23 yr. These factors are also good predictors of the variability in the duration of annual blooms and determined 58.9% of the variability in this parameter. Our results indicate that when nutrients are in sufficiently high quantities to sustain the formation of cyanobacterial blooms, climatic variables become crucial in predicting cyanobacterial bloom events. Climate changes should be considered when we evaluate how much the amount of nutrients should be reduced in Taihu for lake management. Copyright © 2011 Elsevier Ltd. All rights reserved.

Multiple stressors, nonlinear effects and the implications of climate change impacts on marine coastal ecosystems.

PubMed

Hewitt, Judi E; Ellis, Joanne I; Thrush, Simon F

2016-08-01

Global climate change will undoubtedly be a pressure on coastal marine ecosystems, affecting not only species distributions and physiology but also ecosystem functioning. In the coastal zone, the environmental variables that may drive ecological responses to climate change include temperature, wave energy, upwelling events and freshwater inputs, and all act and interact at a variety of spatial and temporal scales. To date, we have a poor understanding of how climate-related environmental changes may affect coastal marine ecosystems or which environmental variables are likely to produce priority effects. Here we use time series data (17 years) of coastal benthic macrofauna to investigate responses to a range of climate-influenced variables including sea-surface temperature, southern oscillation indices (SOI, Z4), wind-wave exposure, freshwater inputs and rainfall. We investigate responses from the abundances of individual species to abundances of functional traits and test whether species that are near the edge of their tolerance to another stressor (in this case sedimentation) may exhibit stronger responses. The responses we observed were all nonlinear and some exhibited thresholds. While temperature was most frequently an important predictor, wave exposure and ENSO-related variables were also frequently important and most ecological variables responded to interactions between environmental variables. There were also indications that species sensitive to another stressor responded more strongly to weaker climate-related environmental change at the stressed site than the unstressed site. The observed interactions between climate variables, effects on key species or functional traits, and synergistic effects of additional anthropogenic stressors have important implications for understanding and predicting the ecological consequences of climate change to coastal ecosystems. © 2015 John Wiley & Sons Ltd.

Relationships between northern Adriatic Sea mucilage events and climate variability.

PubMed

Deserti, Marco; Cacciamani, Carlo; Chiggiato, Jacopo; Rinaldi, Attilio; Ferrari, Carla R

2005-12-15

A long term analysis (1865-2002) of meteorological data collected in the Po Valley and Northern Adriatic Basin have been analysed to find possible links between variability in the climatic parameters and the phenomenon of mucilage. Seasonal anomalies of temperature, calculated as spatial mean over the Po Valley area, and anomalies of North Atlantic Oscillation were compared with the historical record of mucilage episodes. Both climatic indices were found to be positively correlated with mucilage events, suggesting a possible relationship between climatic variability and the increased appearance of mucilage aggregates.

Plausible Effect of Weather on Atlantic Meridional Overturning Circulation with a Coupled General Circulation Model

NASA Astrophysics Data System (ADS)

Liu, Zedong; Wan, Xiuquan

2018-04-01

The Atlantic meridional overturning circulation (AMOC) is a vital component of the global ocean circulation and the heat engine of the climate system. Through the use of a coupled general circulation model, this study examines the role of synoptic systems on the AMOC and presents evidence that internally generated high-frequency, synoptic-scale weather variability in the atmosphere could play a significant role in maintaining the overall strength and variability of the AMOC, thereby affecting climate variability and change. Results of a novel coupling technique show that the strength and variability of the AMOC are greatly reduced once the synoptic weather variability is suppressed in the coupled model. The strength and variability of the AMOC are closely linked to deep convection events at high latitudes, which could be strongly affected by the weather variability. Our results imply that synoptic weather systems are important in driving the AMOC and its variability. Thus, interactions between atmospheric weather variability and AMOC may be an important feedback mechanism of the global climate system and need to be taken into consideration in future climate change studies.

Elementary Student Perceptions of School Climate and Associations with Individual and School Factors

ERIC Educational Resources Information Center

La Salle, Tamika P.; Zabek, Faith; Meyers, Joel

2016-01-01

School climate has increasingly been recognized as an essential component of school improvement owing to the established associations between a positive school climate and academic outcomes for students. Our study examines associations among a brief measure of school climate assessing elementary student perceptions and the College and Career Ready…

Undergraduate Climate Education: Motivations, Strategies, Successes, and Support

ERIC Educational Resources Information Center

Kirk, Karin B.; Gold, Anne U.; Ledley, Tamara Shapiro; Sullivan, Susan Buhr; Manduca, Cathryn A.; Mogk, David W.; Wiese, Katryn

2014-01-01

Climate literacy is an essential component of a strategy to comprehend and confront the grand challenge of global climate change. However, scientific complexity, societal implications, and political associations make climate change a difficult but important topic to teach. In this paper we report on the results of a survey of undergraduate faculty…

Parent, Student, and Teacher Perceptions of School Climate at Suburban High

ERIC Educational Resources Information Center

Steiner, Cory J.

2009-01-01

School climate has a major impact on the school setting. In order to manage climate, it is essential to assess and understand the perceptions of teachers, students, and parents. This study identified the differences between teachers, students, and parents relative to their perceptions concerning school climate at Suburban High. The instrument…