How will climate novelty influence ecological forecasts? Using the Quaternary to assess future reliability.

PubMed

Fitzpatrick, Matthew C; Blois, Jessica L; Williams, John W; Nieto-Lugilde, Diego; Maguire, Kaitlin C; Lorenz, David J

2018-03-23

Future climates are projected to be highly novel relative to recent climates. Climate novelty challenges models that correlate ecological patterns to climate variables and then use these relationships to forecast ecological responses to future climate change. Here, we quantify the magnitude and ecological significance of future climate novelty by comparing it to novel climates over the past 21,000 years in North America. We then use relationships between model performance and climate novelty derived from the fossil pollen record from eastern North America to estimate the expected decrease in predictive skill of ecological forecasting models as future climate novelty increases. We show that, in the high emissions scenario (RCP 8.5) and by late 21st century, future climate novelty is similar to or higher than peak levels of climate novelty over the last 21,000 years. The accuracy of ecological forecasting models is projected to decline steadily over the coming decades in response to increasing climate novelty, although models that incorporate co-occurrences among species may retain somewhat higher predictive skill. In addition to quantifying future climate novelty in the context of late Quaternary climate change, this work underscores the challenges of making reliable forecasts to an increasingly novel future, while highlighting the need to assess potential avenues for improvement, such as increased reliance on geological analogs for future novel climates and improving existing models by pooling data through time and incorporating assemblage-level information. © 2018 John Wiley & Sons Ltd.

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

NASA Astrophysics Data System (ADS)

Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

2018-01-01

Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

Now, Here's the Weather Forecast...

ERIC Educational Resources Information Center

Richardson, Mathew

2013-01-01

The Met Office has a long history of weather forecasting, creating tailored weather forecasts for customers across the world. Based in Exeter, the Met Office is also home to the Met Office Hadley Centre, a world-leading centre for the study of climate change and its potential impacts. Climate information from the Met Office Hadley Centre is used…

Global assessment of experimental climate warming on tundra vegetation: heterogeneity over space and time

Treesearch

Sarah C. Elmendorf; Gregory H.R. Henry; Robert D. Hollister; Robert G. Björk; Anne D. Bjorkman; Terry V. Callaghan; [and others] NO-VALUE; William Gould; Joel Mercado

2012-01-01

Understanding the sensitivity of tundra vegetation to climate warming is critical to forecasting future biodiversity and vegetation feedbacks to climate. In situ warming experiments accelerate climate change on a small scale to forecast responses of local plant communities. Limitations of this approach include the apparent site-specificity of results and uncertainty...

Integrating bioclimate with population models to improve forecasts of species extinctions under climate change.

PubMed

Brook, Barry W; Akçakaya, H Resit; Keith, David A; Mace, Georgina M; Pearson, Richard G; Araújo, Miguel B

2009-12-23

Climate change is already affecting species worldwide, yet existing methods of risk assessment have not considered interactions between demography and climate and their simultaneous effect on habitat distribution and population viability. To address this issue, an international workshop was held at the University of Adelaide in Australia, 25-29 May 2009, bringing leading species distribution and population modellers together with plant ecologists. Building on two previous workshops in the UK and Spain, the participants aimed to develop methodological standards and case studies for integrating bioclimatic and metapopulation models, to provide more realistic forecasts of population change, habitat fragmentation and extinction risk under climate change. The discussions and case studies focused on several challenges, including spatial and temporal scale contingencies, choice of predictive climate, land use, soil type and topographic variables, procedures for ensemble forecasting of both global climate and bioclimate models and developing demographic structures that are realistic and species-specific and yet allow generalizations of traits that make species vulnerable to climate change. The goal is to provide general guidelines for assessing the Red-List status of large numbers of species potentially at risk, owing to the interactions of climate change with other threats such as habitat destruction, overexploitation and invasive species.

Communicating Uncertainties in Weather and Climate Information: Results of a National Academies Workshop

NASA Astrophysics Data System (ADS)

Friday, E.; Barron, E. J.; Elfring, C.; Geller, L.

2002-12-01

When a major East Coast snowstorm was forecast during the winter of 2001, people began preparing - both the public and the decision-makers responsible for public services. There was an air of urgency, heightened because just the previous year the region had been hit hard by a storm of unpredicted strength. But this time, the storm never materialized and people were left wondering what went "wrong" with the forecast. Did something go wrong or did forecasters just fail to communicate their information in an effective way? Did they convey a sense of the likelihood of the event and keep people up to date as information changed? In the summer of 2001, the National Academies' Board on Atmospheric Sciences and Climate hosted a workshop designed to explore the communication of uncertainty in weather and climate information. Workshop participants examined five case studies that were chosen to illustrate a range of forecast timescales and certainty levels. The cases were: Red River Flood, Grand Forks, April 1997; East Coast Winter Storm, March 2001; Oklahoma-Kansas Tornado Outbreak, May 3, 1999; El Nino 1997-1998, and Climate Change Science, a report issued in 2001. In each of these cases, participants examined who said what, when, to whom, how, and with what effect. The last two cases specifically address climate-related topics. This paper summarizes the final workshop report (Communicating Uncertainties in Weather and Climate Information: Summary of a Workshop, NRC 2002), including an overview of the five cases and lessons learned about communicating uncertainties in weather and climate forecasts. Among other findings, the report stresses that communication and appropriate dissemination of information, including information about uncertainty in the forecasts and the forecaster's confidence in the product, should be an integral, ongoing part of the forecasting process, not an afterthought. Explaining uncertainty should be an integral part of what weather and climate forecasters do and is essential to delivering accurate and useful information.

Aviation Turbulence: Dynamics, Forecasting, and Response to Climate Change

NASA Astrophysics Data System (ADS)

Storer, Luke N.; Williams, Paul D.; Gill, Philip G.

2018-03-01

Atmospheric turbulence is a major hazard in the aviation industry and can cause injuries to passengers and crew. Understanding the physical and dynamical generation mechanisms of turbulence aids with the development of new forecasting algorithms and, therefore, reduces the impact that it has on the aviation industry. The scope of this paper is to review the dynamics of aviation turbulence, its response to climate change, and current forecasting methods at the cruising altitude of aircraft. Aviation-affecting turbulence comes from three main sources: vertical wind shear instabilities, convection, and mountain waves. Understanding these features helps researchers to develop better turbulence diagnostics. Recent research suggests that turbulence will increase in frequency and strength with climate change, and therefore, turbulence forecasting may become more important in the future. The current methods of forecasting are unable to predict every turbulence event, and research is ongoing to find the best solution to this problem by combining turbulence predictors and using ensemble forecasts to increase skill. The skill of operational turbulence forecasts has increased steadily over recent decades, mirroring improvements in our understanding. However, more work is needed—ideally in collaboration with the aviation industry—to improve observations and increase forecast skill, to help maintain and enhance aviation safety standards in the future.

Variance analysis of forecasted streamflow maxima in a wet temperate climate

NASA Astrophysics Data System (ADS)

Al Aamery, Nabil; Fox, James F.; Snyder, Mark; Chandramouli, Chandra V.

2018-05-01

Coupling global climate models, hydrologic models and extreme value analysis provides a method to forecast streamflow maxima, however the elusive variance structure of the results hinders confidence in application. Directly correcting the bias of forecasts using the relative change between forecast and control simulations has been shown to marginalize hydrologic uncertainty, reduce model bias, and remove systematic variance when predicting mean monthly and mean annual streamflow, prompting our investigation for maxima streamflow. We assess the variance structure of streamflow maxima using realizations of emission scenario, global climate model type and project phase, downscaling methods, bias correction, extreme value methods, and hydrologic model inputs and parameterization. Results show that the relative change of streamflow maxima was not dependent on systematic variance from the annual maxima versus peak over threshold method applied, albeit we stress that researchers strictly adhere to rules from extreme value theory when applying the peak over threshold method. Regardless of which method is applied, extreme value model fitting does add variance to the projection, and the variance is an increasing function of the return period. Unlike the relative change of mean streamflow, results show that the variance of the maxima's relative change was dependent on all climate model factors tested as well as hydrologic model inputs and calibration. Ensemble projections forecast an increase of streamflow maxima for 2050 with pronounced forecast standard error, including an increase of +30(±21), +38(±34) and +51(±85)% for 2, 20 and 100 year streamflow events for the wet temperate region studied. The variance of maxima projections was dominated by climate model factors and extreme value analyses.

Do we need demographic data to forecast plant population dynamics?

USGS Publications Warehouse

Tredennick, Andrew T.; Hooten, Mevin B.; Adler, Peter B.

2017-01-01

Rapid environmental change has generated growing interest in forecasts of future population trajectories. Traditional population models built with detailed demographic observations from one study site can address the impacts of environmental change at particular locations, but are difficult to scale up to the landscape and regional scales relevant to management decisions. An alternative is to build models using population-level data that are much easier to collect over broad spatial scales than individual-level data. However, it is unknown whether models built using population-level data adequately capture the effects of density-dependence and environmental forcing that are necessary to generate skillful forecasts.Here, we test the consequences of aggregating individual responses when forecasting the population states (percent cover) and trajectories of four perennial grass species in a semi-arid grassland in Montana, USA. We parameterized two population models for each species, one based on individual-level data (survival, growth and recruitment) and one on population-level data (percent cover), and compared their forecasting accuracy and forecast horizons with and without the inclusion of climate covariates. For both models, we used Bayesian ridge regression to weight the influence of climate covariates for optimal prediction.In the absence of climate effects, we found no significant difference between the forecast accuracy of models based on individual-level data and models based on population-level data. Climate effects were weak, but increased forecast accuracy for two species. Increases in accuracy with climate covariates were similar between model types.In our case study, percent cover models generated forecasts as accurate as those from a demographic model. For the goal of forecasting, models based on aggregated individual-level data may offer a practical alternative to data-intensive demographic models. Long time series of percent cover data already exist for many plant species. Modelers should exploit these data to predict the impacts of environmental change.

Statistical Forecasting of Current and Future Circum-Arctic Ground Temperatures and Active Layer Thickness

NASA Astrophysics Data System (ADS)

Aalto, J.; Karjalainen, O.; Hjort, J.; Luoto, M.

2018-05-01

Mean annual ground temperature (MAGT) and active layer thickness (ALT) are key to understanding the evolution of the ground thermal state across the Arctic under climate change. Here a statistical modeling approach is presented to forecast current and future circum-Arctic MAGT and ALT in relation to climatic and local environmental factors, at spatial scales unreachable with contemporary transient modeling. After deploying an ensemble of multiple statistical techniques, distance-blocked cross validation between observations and predictions suggested excellent and reasonable transferability of the MAGT and ALT models, respectively. The MAGT forecasts indicated currently suitable conditions for permafrost to prevail over an area of 15.1 ± 2.8 × 106 km2. This extent is likely to dramatically contract in the future, as the results showed consistent, but region-specific, changes in ground thermal regime due to climate change. The forecasts provide new opportunities to assess future Arctic changes in ground thermal state and biogeochemical feedback.

Analyses of historical and projected climates to support climate adaptation in the northern Rocky Mountains: Chapter 4

USGS Publications Warehouse

Gross, John E.; Tercek, Michael; Guay, Kevin; Chang, Tony; Talbert, Marian; Rodman, Ann; Thoma, David; Jantz, Patrick; Morisette, Jeffrey T.

2016-01-01

Most of the western United States is experiencing the effects of rapid and directional climate change (Garfin et al. 2013). These effects, along with forecasts of profound changes in the future, provide strong motivation for resource managers to learn about and prepare for future changes. Climate adaptation plans are based on an understanding of historic climate variation and their effects on ecosystems and on forecasts of future climate trends. Frameworks for climate adaptation thus universally identify the importance of a summary of historical, current, and projected climates (Glick, Stein, and Edelson 2011; Cross et al. 2013; Stein et al. 2014). Trends in physical climate variables are usually the basis for evaluating the exposure component in vulnerability assessments. Thus, this chapter focuses on step 2 of the Climate-Smart Conservation framework (chap. 2): vulnerability assessment. We present analyses of historical and current observations of temperature, precipitation, and other key climate measurements to provide context and a baseline for interpreting the ecological impacts of projected climate changes.

Global Warming: Understanding and Teaching the Forecast.

ERIC Educational Resources Information Center

Andrews, Bill

1994-01-01

A resource for the teaching of the history and causes of climate change. Discusses evidence of climate change from the Viking era, early ice ages, the most recent ice age, natural causes of climate change, human-made causes of climate change, projections of global warming, and unequal warming. (LZ)

Types of Forecast and Weather-Related Information Used among Tourism Businesses in Coastal North Carolina

NASA Astrophysics Data System (ADS)

Ayscue, Emily P.

This study profiles the coastal tourism sector, a large and diverse consumer of climate and weather information. It is crucial to provide reliable, accurate and relevant resources for the climate and weather-sensitive portions of this stakeholder group in order to guide them in capitalizing on current climate and weather conditions and to prepare them for potential changes. An online survey of tourism business owners, managers and support specialists was conducted within the eight North Carolina oceanfront counties asking respondents about forecasts they use and for what purposes as well as why certain forecasts are not used. Respondents were also asked about their perceived dependency of their business on climate and weather as well as how valuable different forecasts are to their decision-making. Business types represented include: Agriculture, Outdoor Recreation, Accommodations, Food Services, Parks and Heritage, and Other. Weekly forecasts were the most popular forecasts with Monthly and Seasonal being the least used. MANOVA and ANOVA analyses revealed outdoor-oriented businesses (Agriculture and Outdoor Recreation) as perceiving themselves significantly more dependent on climate and weather than indoor-oriented ones (Food Services and Accommodations). Outdoor businesses also valued short-range forecasts significantly more than indoor businesses. This suggests a positive relationship between perceived climate and weather dependency and forecast value. The low perceived dependency and value of short-range forecasts of indoor businesses presents an opportunity to create climate and weather information resources directed at how they can capitalize on positive climate and weather forecasts and how to counter negative effects with forecasted adverse conditions. The low use of long-range forecasts among all business types can be related to the low value placed on these forecasts. However, these forecasts are still important in that they are used to make more financially risky decisions such as investment decisions.

Winter Precipitation Forecast in the European and Mediterranean Regions Using Cluster Analysis

NASA Astrophysics Data System (ADS)

Totz, Sonja; Tziperman, Eli; Coumou, Dim; Pfeiffer, Karl; Cohen, Judah

2017-12-01

The European climate is changing under global warming, and especially the Mediterranean region has been identified as a hot spot for climate change with climate models projecting a reduction in winter rainfall and a very pronounced increase in summertime heat waves. These trends are already detectable over the historic period. Hence, it is beneficial to forecast seasonal droughts well in advance so that water managers and stakeholders can prepare to mitigate deleterious impacts. We developed a new cluster-based empirical forecast method to predict precipitation anomalies in winter. This algorithm considers not only the strength but also the pattern of the precursors. We compare our algorithm with dynamic forecast models and a canonical correlation analysis-based prediction method demonstrating that our prediction method performs better in terms of time and pattern correlation in the Mediterranean and European regions.

Communicating Climate Change - Weather Forecast Need Assessment and Information Dissemination Mechanism to Farmers in Nepal

NASA Astrophysics Data System (ADS)

Panthi, J., Sr.

2014-12-01

Climate Change is becoming one of the major threats to the fragile Himalayan ecosystem. It is affecting all sectors mainly fresh water, agriculture, forest, biodiversity and species. The subsistence agriculture system of Nepal is mainly rain-fed; therefore, climate change and climate extremes do have direct impacts on it. Weather extremes like droughts, floods and landslides long-lasting fog, hot and cold waves are affecting the agriculture sectors of Nepal. As human-induced climate change has already showing its impacts and it is going to be there for a long time to come, it is paramount importance to move towards the adaptation. Early warning system is an effective way for reducing the impacts of disasters. Forecasting of weather parameters (temperature, precipitation, and wind) helps farmers for their preparedness activities. With consultation with farmers and other relevant institutions, a research project was carried out, for the first time in Nepal, to identify the forecast information need to farmers and their dissemination mechanism. Community consultation workshops, key informant survey, and field observations were the techniques used for this research. Two ecological locations: Bageshwori VDC in Banke (plain) and Dhaibung VDC in Rasuwa (mountain) were taken as the pilot sites for this assessment. People in both the districts are dependent highly on agriculture and the weather extremes like hailstone, untimely rainfall; droughts are affecting their agriculture practices. They do not have confidence in the weather forecast information disseminated by the government of Nepal currently being done because it is a general forecast not done for a smaller domain and the forecast is valid only for 24 hours. The weather forecast need to the farmers in both the sites are: rainfall (intensity, duration and time), drought, and hailstone but in Banke, people wished to have the information of heat and cold waves too as they are affecting their wheat and tomato crops respectively the most. The mechanism of dissemination of the forecast information has been identified and agreed as local radio/FM, mobile telephoning to community leader and displaying and daily updating the forecast information in community hoarding boards.

Global Impacts and Regional Actions: Preparing for the 1997-98 El Niño.

NASA Astrophysics Data System (ADS)

Buizer, James L.; Foster, Josh; Lund, David

2000-09-01

It has been estimated that severe El Niño-related flooding and droughts in Africa, Latin America, North America, and Southeast Asia resulted in more than 22 000 lives lost and in excess of $36 billion in damages during 1997-98. As one of the most severe events this century, the 1997-98 El Niño was unique not only in terms of physical magnitude, but also in terms of human response. This response was made possible by recent advances in climate-observing and forecasting systems, creation and dissemination of forecast information by institutions such as the International Research Institute for Climate Prediction and NOAA's Climate Prediction Center, and individuals in climate-sensitive sectors willing to act on forecast information by incorporating it into their decision-making. The supporting link between the forecasts and their practical application was a product of efforts by several national and international organizations, and a primary focus of the United States National Oceanic and Atmospheric Administration Office of Global Programs (NOAA/OGP).NOAA/OGP over the last decade has supported pilot projects in Latin America, the Caribbean, the South Pacific, Southeast Asia, and Africa to improve transfer of forecast information to climate sensitive sectors, study linkages between climate and human health, and distribute climate information products in certain areas. Working with domestic and international partners, NOAA/OGP helped organize a total of 11 Climate Outlook Fora around the world during the 1997-98 El Niño. At each Outlook Forum, climatologists and meteorologists created regional, consensus-based, seasonal precipitation forecasts and representatives from climate-sensitive sectors discussed options for applying forecast information. Additional ongoing activities during 1997-98 included research programs focused on the social and economic impacts of climate change and the regional manifestations of global-scale climate variations and their effect on decision-making in climate-sensitive sectors in the United States.The overall intent of NOAA/OGP's activities was to make experimental forecast information broadly available to potential users, and to foster a learning process on how seasonal-to-interannual forecasts could be applied in sectors susceptible to climate variability. This process allowed users to explore the capabilities and limitations of climate forecasts currently available, and forecast producers to receive feedback on the utility of their products. Through activities in which NOAA/OGP and its partners were involved, it became clear that further application of forecast information will be aided by improved forecast accuracy and detail, creation of common validation techniques, continued training in forecast generation and application, alternate methods for presenting forecast information, and a systematic strategy for creation and dissemination of forecast products.The overall intent of NOAA/OGP's activities was to make experimental forecast information broadly available to potential users, and to foster a learning process on how seasonal-to-interannual forecasts could be applied in sectors susceptible to climate variability. This process allowed users to explore the capabilities and limitations of climate forecasts currently available, and forecast producers to receive feedback on the utility of their products. Through activities in which NOAA/OGP and its partners were involved, it became clear that further application of forecast information will be aided by improved forecast accuracy and detail, creation of common validation techniques, continued training in forecast generation and application, alternate methods for presenting forecast information, and a systematic strategy for creation and dissemination of forecast products.

Global Climate Change: Threat Multiplier for AFRICOM?

DTIC Science & Technology

2007-11-06

climate change , stability for Africa hinges upon mitigating the effects of global climate change to prevent future conflicts such as Darfur, and the...instability that fosters terrorism. The National Security Act of 2010 will formally address climate change and the planning requirement for the threat...of Responsibility (AOR). He will need to integrate multinational and multiagency cooperation to address climate change forecasts. The author

Value of the GENS Forecast Ensemble as a Tool for Adaptation of Economic Activity to Climate Change

NASA Astrophysics Data System (ADS)

Hancock, L. O.; Alpert, J. C.; Kordzakhia, M.

2009-12-01

In an atmosphere of uncertainty as to the magnitude and direction of climate change in upcoming decades, one adaptation mechanism has emerged with consensus support: the upgrade and dissemination of spatially-resolved, accurate forecasts tailored to the needs of users. Forecasting can facilitate the changeover from dependence on climatology that is increasingly out of date. The best forecasters are local, but local forecasters face great constraints in some countries. Indeed, it is no coincidence that some areas subject to great weather variability and strong processes of climate change are economically vulnerable: mountainous regions, for example, where heavy and erratic flooding can destroy the value built up by households over years. It follows that those best placed to benefit from forecasting upgrades may not be those who have invested in the greatest capacity to date. More-flexible use of the global forecasts may contribute to adaptation. NOAA anticipated several years ago that their forecasts could be used in new ways in the future, and accordingly prepared sockets for easy access to their archives. These could be used to empower various national and regional capacities. Verification to identify practical lead times for the economically important variables is a needed first step. This presentation presents the verification that our team has undertaken, a pilot effort in which we considered variables of interest to economic actors in several lower income countries, cf. shepherds in a remote area of Central Asia, and verified the ensemble forecasts of those variables.

Agroclimate.Org: Tools and Information for a Climate Resilient Agriculture in the Southeast USA

NASA Astrophysics Data System (ADS)

Fraisse, C.

2014-12-01

AgroClimate (http://agroclimate.org) is a web-based system developed to help the agricultural industry in the southeastern USA reduce risks associated with climate variability and change. It includes climate related information and dynamic application tools that interact with a climate and crop database system. Information available includes climate monitoring and forecasts combined with information about crop management practices that help increase the resiliency of the agricultural industry in the region. Recently we have included smartphone apps in the AgroClimate suite of tools, including irrigation management and crop disease alert systems. Decision support tools available in AgroClimate include: (a) Climate risk: expected (probabilistic) and historical climate information and freeze risk; (b) Crop yield risk: expected yield based on soil type, planting date, and basic management practices for selected commodities and historical county yield databases; (c) Crop diseases: disease risk monitoring and forecasting for strawberry and citrus; (d) Crop development: monitoring and forecasting of growing degree-days and chill accumulation; (e) Drought: monitoring and forecasting of selected drought indices, (f) Footprints: Carbon and water footprint calculators. The system also provides background information about the main drivers of climate variability and basic information about climate change in the Southeast USA. AgroClimate has been widely used as an educational tool by the Cooperative Extension Services in the region and also by producers. It is now being replicated internationally with version implemented in Mozambique and Paraguay.

Forecasted coral reef decline in marine biodiversity hotspots under climate change.

PubMed

Descombes, Patrice; Wisz, Mary S; Leprieur, Fabien; Parravicini, Valerianio; Heine, Christian; Olsen, Steffen M; Swingedouw, Didier; Kulbicki, Michel; Mouillot, David; Pellissier, Loïc

2015-01-21

Coral bleaching events threaten coral reef habitats globally and cause severe declines of local biodiversity and productivity. Related to high sea surface temperatures (SST), bleaching events are expected to increase as a consequence of future global warming. However, response to climate change is still uncertain as future low-latitude climatic conditions have no present-day analogue. Sea surface temperatures during the Eocene epoch were warmer than forecasted changes for the coming century, and distributions of corals during the Eocene may help to inform models forecasting the future of coral reefs. We coupled contemporary and Eocene coral occurrences with information on their respective climatic conditions to model the thermal niche of coral reefs and its potential response to projected climate change. We found that under the RCP8.5 climate change scenario, the global suitability for coral reefs may increase up to 16% by 2100, mostly due to improved suitability of higher latitudes. In contrast, in its current range, coral reef suitability may decrease up to 46% by 2100. Reduction in thermal suitability will be most severe in biodiversity hotspots, especially in the Indo-Australian Archipelago. Our results suggest that many contemporary hotspots for coral reefs, including those that have been refugia in the past, spatially mismatch with future suitable areas for coral reefs posing challenges to conservation actions under climate change. © 2015 John Wiley & Sons Ltd.

Can Climate Information be relevant to decision making for Agriculture on the 1-10 year timescale? Case studies from southern Africa

NASA Astrophysics Data System (ADS)

Fujisawa, Mariko

2016-04-01

Climate forecasts have been developed to assist decision making in sectors averse to, and affected by, climate risks, and agriculture is one of those. In agriculture and food security, climate information is now used on a range of timescales, from days (weather), months (seasonal outlooks) to decades (climate change scenarios). Former researchers have shown that when seasonal climate forecast information was provided to farmers prior to decision making, farmers adapted by changing their choice of planting seeds and timing or area planted. However, it is not always clear that the end-users' needs for climate information are met and there might be a large gap between information supplied and needed. It has been pointed out that even when forecasts were available, they were often not utilized by farmers and extension services because of lack of trust in the forecast or the forecasts did not reach the targeted farmers. Many studies have focused on the use of either seasonal forecasts or longer term climate change prediction, but little research has been done on the medium term, that is, 1 to 10 year future climate information. The agriculture and food system sector is one potential user of medium term information, as land use policy and cropping systems selection may fall into this time scale and may affect farmers' decision making process. Assuming that reliable information is provided and it is utilized by farmers for decision making, it might contribute to resilient farming and indeed to longer term food security. To this end, we try to determine the effect of medium term climate information on farmers' strategic decision making process. We explored the end-users' needs for climate information and especially the possible role of medium term information in agricultural system, by conducting interview surveys with farmers and agricultural experts. In this study, the cases of apple production in South Africa, maize production in Malawi and rice production in Tanzania will be presented. With case studies of various crops, we also aim to identify what climatic factors and timescale of prediction may be critical to what crop types of farmers, which may be of value to climate prediction community to further develop climate prediction useful for agricultural system.

Effects of Changing Climate During the Snow Ablation Season on Seasonal Streamflow Forecasts

NASA Astrophysics Data System (ADS)

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

2017-12-01

Seasonal forecasts of total surface runoff (Q) in snowmelt-dominated watersheds derive most of their prediction skill from the historical relationship between late winter snowpack (SWE) and subsequent snowmelt runoff. Across the western US, however, the relationship between SWE and Q is weakening as temperatures rise. We describe the effects of climate variability and change during the springtime snow ablation season on water supply outlooks (forecasts of Q) for southwestern rivers. As snow melts earlier, the importance of post-snow rainfall increases: interannual variability of spring season precipitation accounts for an increasing fraction of the variability of Q in recent decades. The results indicate that improvements to the skill of S2S forecasts of spring season temperature and precipitation would contribute very significantly to water supply outlooks that are now based largely on observed SWE. We assess this hypothesis using historical data from several snowpack-dominated basins in the American Southwest (Rio Grande, Pecos, and Gila Rivers) which are undergoing rapid climate change.

Forecasting wildlife response to rapid warming in the Alaskan Arctic

USGS Publications Warehouse

Van Hemert, Caroline R.; Flint, Paul L.; Udevitz, Mark S.; Koch, Joshua C.; Atwood, Todd C.; Oakley, Karen L.; Pearce, John M.

2015-01-01

Arctic wildlife species face a dynamic and increasingly novel environment because of climate warming and the associated increase in human activity. Both marine and terrestrial environments are undergoing rapid environmental shifts, including loss of sea ice, permafrost degradation, and altered biogeochemical fluxes. Forecasting wildlife responses to climate change can facilitate proactive decisions that balance stewardship with resource development. In this article, we discuss the primary and secondary responses to physical climate-related drivers in the Arctic, associated wildlife responses, and additional sources of complexity in forecasting wildlife population outcomes. Although the effects of warming on wildlife populations are becoming increasingly well documented in the scientific literature, clear mechanistic links are often difficult to establish. An integrated science approach and robust modeling tools are necessary to make predictions and determine resiliency to change. We provide a conceptual framework and introduce examples relevant for developing wildlife forecasts useful to management decisions.

Climate change and the possible health effects on older Australians.

PubMed

Saniotis, Arthur; Irvine, Rod

2010-01-01

Climate change is an important issue for Australia. Climate change research forecasts that Australia will experience accelerated warming due to anthrogenic activities. Australia's aging society will face special challenges that demand current attention. This paper discusses two issues in relation to climate change and older Australians: first, pharmacology and autoregulation; and second, mental health among older Australians.

Climate Change Adaptation in the Western U.S.: the Case for Dynamic Rule Curves in Water Resources Management

NASA Astrophysics Data System (ADS)

Lee, S.; Hamlet, A. F.; Burges, S. J.

2008-12-01

Climate change in the Western U.S. will bring systematic hydrologic changes affecting many water resources systems. Successful adaptation to these changes, which will be ongoing through the 21st century, will require the 'rebalancing' of competing system objectives such as water supply, flood control, hydropower production, and environmental services in response to hydrologic (and other) changes. Although fixed operating policies for the operation of reservoirs has been a traditional approach to water management in the 20th century, the rapid pace of projected climate shifts (~0.5 F per decade), and the prohibitive costs of recursive policy intervention to mitigate impacts, suggest that more sophisticated approaches will be needed to cope with climate change on a long term basis. The use of 'dynamic rule curves' is an approach that maintains some of the key characteristics of current water management practice (reservoir rule curves) while avoiding many of the fundamental drawbacks of traditional water resources management strategies in a non-stationary climate. In this approach, water resources systems are optimized for each operational period using ensemble streamflow and/or water demand forecasts. The ensemble of optimized reservoir storage traces are then analyzed to produce a set of unique reservoir rule curves for each operational period reflecting the current state of the system. The potential advantage of this approach is that hydrologic changes associated with climate change (such as systematically warmer temperatures) can be captured explicitly in operational hydrologic forecasts, which would in turn inform the optimized reservoir management solutions, creating water resources systems that are largely 'self tending' as the climate system evolves. Furthermore, as hydrologic forecasting systems improve (e.g. in response to improved ENSO forecasting or other scientific advances), so does the performance of reservoir operations. An example of the approach is given for flood control in the Columbia River basin.

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.

Fine-resolution conservation planning with limited climate-change information.

PubMed

Shah, Payal; Mallory, Mindy L; Ando, Amy W; Guntenspergen, Glenn R

2017-04-01

Climate-change induced uncertainties in future spatial patterns of conservation-related outcomes make it difficult to implement standard conservation-planning paradigms. A recent study translates Markowitz's risk-diversification strategy from finance to conservation settings, enabling conservation agents to use this diversification strategy for allocating conservation and restoration investments across space to minimize the risk associated with such uncertainty. However, this method is information intensive and requires a large number of forecasts of ecological outcomes associated with possible climate-change scenarios for carrying out fine-resolution conservation planning. We developed a technique for iterative, spatial portfolio analysis that can be used to allocate scarce conservation resources across a desired level of subregions in a planning landscape in the absence of a sufficient number of ecological forecasts. We applied our technique to the Prairie Pothole Region in central North America. A lack of sufficient future climate information prevented attainment of the most efficient risk-return conservation outcomes in the Prairie Pothole Region. The difference in expected conservation returns between conservation planning with limited climate-change information and full climate-change information was as large as 30% for the Prairie Pothole Region even when the most efficient iterative approach was used. However, our iterative approach allowed finer resolution portfolio allocation with limited climate-change forecasts such that the best possible risk-return combinations were obtained. With our most efficient iterative approach, the expected loss in conservation outcomes owing to limited climate-change information could be reduced by 17% relative to other iterative approaches. © 2016 Society for Conservation Biology.

Uncertainty in weather and climate prediction

PubMed Central

Slingo, Julia; Palmer, Tim

2011-01-01

Following Lorenz's seminal work on chaos theory in the 1960s, probabilistic approaches to prediction have come to dominate the science of weather and climate forecasting. This paper gives a perspective on Lorenz's work and how it has influenced the ways in which we seek to represent uncertainty in forecasts on all lead times from hours to decades. It looks at how model uncertainty has been represented in probabilistic prediction systems and considers the challenges posed by a changing climate. Finally, the paper considers how the uncertainty in projections of climate change can be addressed to deliver more reliable and confident assessments that support decision-making on adaptation and mitigation. PMID:22042896

An approach to consider behavioral plasticity as a source of uncertainty when forecasting species' response to climate change

PubMed Central

Muñoz, Antonio-Román; Márquez, Ana Luz; Real, Raimundo

2015-01-01

The rapid ecological shifts that are occurring due to climate change present major challenges for managers and policymakers and, therefore, are one of the main concerns for environmental modelers and evolutionary biologists. Species distribution models (SDM) are appropriate tools for assessing the relationship between species distribution and environmental conditions, so being customarily used to forecast the biogeographical response of species to climate change. A serious limitation of species distribution models when forecasting the effects of climate change is that they normally assume that species behavior and climatic tolerances will remain constant through time. In this study, we propose a new methodology, based on fuzzy logic, useful for incorporating the potential capacity of species to adapt to new conditions into species distribution models. Our results demonstrate that it is possible to include different behavioral responses of species when predicting the effects of climate change on species distribution. Favorability models offered in this study show two extremes: one considering that the species will not modify its present behavior, and another assuming that the species will take full advantage of the possibilities offered by an increase in environmental favorability. This methodology may mean a more realistic approach to the assessment of the consequences of global change on species' distribution and conservation. Overlooking the potential of species' phenotypical plasticity may under- or overestimate the predicted response of species to changes in environmental drivers and its effects on species distribution. Using this approach, we could reinforce the science behind conservation planning in the current situation of rapid climate change. PMID:26120426

An approach to consider behavioral plasticity as a source of uncertainty when forecasting species' response to climate change.

PubMed

Muñoz, Antonio-Román; Márquez, Ana Luz; Real, Raimundo

2015-06-01

The rapid ecological shifts that are occurring due to climate change present major challenges for managers and policymakers and, therefore, are one of the main concerns for environmental modelers and evolutionary biologists. Species distribution models (SDM) are appropriate tools for assessing the relationship between species distribution and environmental conditions, so being customarily used to forecast the biogeographical response of species to climate change. A serious limitation of species distribution models when forecasting the effects of climate change is that they normally assume that species behavior and climatic tolerances will remain constant through time. In this study, we propose a new methodology, based on fuzzy logic, useful for incorporating the potential capacity of species to adapt to new conditions into species distribution models. Our results demonstrate that it is possible to include different behavioral responses of species when predicting the effects of climate change on species distribution. Favorability models offered in this study show two extremes: one considering that the species will not modify its present behavior, and another assuming that the species will take full advantage of the possibilities offered by an increase in environmental favorability. This methodology may mean a more realistic approach to the assessment of the consequences of global change on species' distribution and conservation. Overlooking the potential of species' phenotypical plasticity may under- or overestimate the predicted response of species to changes in environmental drivers and its effects on species distribution. Using this approach, we could reinforce the science behind conservation planning in the current situation of rapid climate change.

Probabilistic accounting of uncertainty in forecasts of species distributions under climate change

Treesearch

Seth J. Wenger; Nicholas A. Som; Daniel C. Dauwalter; Daniel J. Isaak; Helen M. Neville; Charles H. Luce; Jason B. Dunham; Michael K. Young; Kurt D. Fausch; Bruce E. Rieman

2013-01-01

Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing...

Forecasting the effects of land-use and climate change on wildlife communities and habitats in the lower Mississippi Valley

USGS Publications Warehouse

Faulkner, Stephen P.

2010-01-01

Landscape patterns and processes reflect both natural ecosystem attributes and the policy and management decisions of individual Federal, State, county, and private organizations. Land-use regulation, water management, and habitat conservation and restoration efforts increasingly rely on landscape-level approaches that incorporate scientific information into the decision-making process. Since management actions are implemented to affect future conditions, decision-support models are necessary to forecast potential future conditions resulting from these decisions. Spatially explicit modeling approaches enable testing of different scenarios and help evaluate potential outcomes of management actions in conjunction with natural processes such as climate change. The ability to forecast the effects of changing land use and climate is critically important to land and resource managers since their work is inherently site specific, yet conservation strategies and practices are expressed at higher spatial and temporal scales that must be considered in the decisionmaking process.

Communicating uncertainty in seasonal and interannual climate forecasts in Europe.

PubMed

Taylor, Andrea L; Dessai, Suraje; de Bruin, Wändi Bruine

2015-11-28

Across Europe, organizations in different sectors are sensitive to climate variability and change, at a range of temporal scales from the seasonal to the interannual to the multi-decadal. Climate forecast providers face the challenge of communicating the uncertainty inherent in these forecasts to these decision-makers in a way that is transparent, understandable and does not lead to a false sense of certainty. This article reports the findings of a user-needs survey, conducted with 50 representatives of organizations in Europe from a variety of sectors (e.g. water management, forestry, energy, tourism, health) interested in seasonal and interannual climate forecasts. We find that while many participating organizations perform their own 'in house' risk analysis most require some form of processing and interpretation by forecast providers. However, we also find that while users tend to perceive seasonal and interannual forecasts to be useful, they often find them difficult to understand, highlighting the need for communication formats suitable for both expert and non-expert users. In addition, our results show that people tend to prefer familiar formats for receiving information about uncertainty. The implications of these findings for both the providers and users of climate information are discussed. © 2015 The Authors.

Communicating uncertainty in seasonal and interannual climate forecasts in Europe

PubMed Central

Taylor, Andrea L.; Dessai, Suraje; de Bruin, Wändi Bruine

2015-01-01

Across Europe, organizations in different sectors are sensitive to climate variability and change, at a range of temporal scales from the seasonal to the interannual to the multi-decadal. Climate forecast providers face the challenge of communicating the uncertainty inherent in these forecasts to these decision-makers in a way that is transparent, understandable and does not lead to a false sense of certainty. This article reports the findings of a user-needs survey, conducted with 50 representatives of organizations in Europe from a variety of sectors (e.g. water management, forestry, energy, tourism, health) interested in seasonal and interannual climate forecasts. We find that while many participating organizations perform their own ‘in house’ risk analysis most require some form of processing and interpretation by forecast providers. However, we also find that while users tend to perceive seasonal and interannual forecasts to be useful, they often find them difficult to understand, highlighting the need for communication formats suitable for both expert and non-expert users. In addition, our results show that people tend to prefer familiar formats for receiving information about uncertainty. The implications of these findings for both the providers and users of climate information are discussed. PMID:26460115

Impacts of fire and climate change on long-term nitrogen availability and forest productivity in the New Jersey Pine Barrens

Treesearch

Melissa S. Lucash; Robert M. Scheller; Alec M. Kretchun; Kenneth L. Clark; John Hom

2014-01-01

Increased wildfires and temperatures due to climate change are expected to have profound effects on forest productivity and nitrogen (N) cycling. Forecasts about how wildfire and climate change will affect forests seldom consider N availability, which may limit forest response to climate change, particularly in fire-prone landscapes. The overall objective of this study...

COP21 climate negotiators' responses to climate model forecasts

NASA Astrophysics Data System (ADS)

Bosetti, Valentina; Weber, Elke; Berger, Loïc; Budescu, David V.; Liu, Ning; Tavoni, Massimo

2017-02-01

Policymakers involved in climate change negotiations are key users of climate science. It is therefore vital to understand how to communicate scientific information most effectively to this group. We tested how a unique sample of policymakers and negotiators at the Paris COP21 conference update their beliefs on year 2100 global mean temperature increases in response to a statistical summary of climate models' forecasts. We randomized the way information was provided across participants using three different formats similar to those used in Intergovernmental Panel on Climate Change reports. In spite of having received all available relevant scientific information, policymakers adopted such information very conservatively, assigning it less weight than their own prior beliefs. However, providing individual model estimates in addition to the statistical range was more effective in mitigating such inertia. The experiment was repeated with a population of European MBA students who, despite starting from similar priors, reported conditional probabilities closer to the provided models' forecasts than policymakers. There was also no effect of presentation format in the MBA sample. These results highlight the importance of testing visualization tools directly on the population of interest.

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

Branstator, Grant

The overall aim of our project was to quantify and characterize predictability of the climate as it pertains to decadal time scale predictions. By predictability we mean the degree to which a climate forecast can be distinguished from the climate that exists at initial forecast time, taking into consideration the growth of uncertainty that occurs as a result of the climate system being chaotic. In our project we were especially interested in predictability that arises from initializing forecasts from some specific state though we also contrast this predictability with predictability arising from forecasting the reaction of the system to externalmore » forcing – for example changes in greenhouse gas concentration. Also, we put special emphasis on the predictability of prominent intrinsic patterns of the system because they often dominate system behavior. Highlights from this work include: • Development of novel methods for estimating the predictability of climate forecast models. • Quantification of the initial value predictability limits of ocean heat content and the overturning circulation in the Atlantic as they are represented in various state of the art climate models. These limits varied substantially from model to model but on average were about a decade with North Atlantic heat content tending to be more predictable than North Pacific heat content. • Comparison of predictability resulting from knowledge of the current state of the climate system with predictability resulting from estimates of how the climate system will react to changes in greenhouse gas concentrations. It turned out that knowledge of the initial state produces a larger impact on forecasts for the first 5 to 10 years of projections. • Estimation of the predictability of dominant patterns of ocean variability including well-known patterns of variability in the North Pacific and North Atlantic. For the most part these patterns were predictable for 5 to 10 years. • Determination of especially predictable patterns in the North Atlantic. The most predictable of these retain predictability substantially longer than generic patterns, with some being predictable for two decades.« less

The Age of Consequences: The Foreign Policy and National Security Implications of Global Climate Change

DTIC Science & Technology

2007-11-01

Climate skeptics The climate change-conflict nexus has its fair share of skeptics. Many observers remain unconvinced that climate change, whether due...implications of climate change remain specula- tive. In addition, they observe that none of the consequences forecast in the authoritative reports of the...modern practices and relocate to the few remaining habitable regions at the extreme north- ern and southern hemispheres.33 Essam El Hinnawi first

Contrasting growth forecasts across the geographical range of Scots pine due to altitudinal and latitudinal differences in climatic sensitivity.

PubMed

Matías, Luis; Linares, Juan C; Sánchez-Miranda, Ángela; Jump, Alistair S

2017-10-01

Ongoing changes in global climate are altering ecological conditions for many species. The consequences of such changes are typically most evident at the edge of a species' geographical distribution, where differences in growth or population dynamics may result in range expansions or contractions. Understanding population responses to different climatic drivers along wide latitudinal and altitudinal gradients is necessary in order to gain a better understanding of plant responses to ongoing increases in global temperature and drought severity. We selected Scots pine (Pinus sylvestris L.) as a model species to explore growth responses to climatic variability (seasonal temperature and precipitation) over the last century through dendrochronological methods. We developed linear models based on age, climate and previous growth to forecast growth trends up to year 2100 using climatic predictions. Populations were located at the treeline across a latitudinal gradient covering the northern, central and southernmost populations and across an altitudinal gradient at the southern edge of the distribution (treeline, medium and lower elevations). Radial growth was maximal at medium altitude and treeline of the southernmost populations. Temperature was the main factor controlling growth variability along the gradients, although the timing and strength of climatic variables affecting growth shifted with latitude and altitude. Predictive models forecast a general increase in Scots pine growth at treeline across the latitudinal distribution, with southern populations increasing growth up to year 2050, when it stabilizes. The highest responsiveness appeared at central latitude, and moderate growth increase is projected at the northern limit. Contrastingly, the model forecasted growth declines at lowland-southern populations, suggesting an upslope range displacement over the coming decades. Our results give insight into the geographical responses of tree species to climate change and demonstrate the importance of incorporating biogeographical variability into predictive models for an accurate prediction of species dynamics as climate changes. © 2017 John Wiley & Sons Ltd.

Climate change, ecosystem impacts, and management for Pacific salmon

Treesearch

D.E. Schindler; X. Augerot; E. Fleishman; N.J. Mantua; B. Riddell; M. Ruckelshaus; J. Seeb; M. Webster

2008-01-01

As climate change intensifies, there is increasing interest in developing models that reduce uncertainties in projections of global climate and refine these projections to finer spatial scales. Forecasts of climate impacts on ecosystems are far more challenging and their uncertainties even larger because of a limited understanding of physical controls on biological...

"Near-term" Natural Catastrophe Risk Management and Risk Hedging in a Changing Climate

NASA Astrophysics Data System (ADS)

Michel, Gero; Tiampo, Kristy

2014-05-01

Competing with analytics - Can the insurance market take advantage of seasonal or "near-term" forecasting and temporal changes in risk? Natural perils (re)insurance has been based on models following climatology i.e. the long-term "historical" average. This is opposed to considering the "near-term" and forecasting hazard and risk for the seasons or years to come. Variability and short-term changes in risk are deemed abundant for almost all perils. In addition to hydrometeorological perils whose changes are vastly discussed, earthquake activity might also change over various time-scales affected by earlier local (or even global) events, regional changes in the distribution of stresses and strains and more. Only recently has insurance risk modeling of (stochastic) hurricane-years or extratropical-storm-years started considering our ability to forecast climate variability herewith taking advantage of apparent correlations between climate indicators and the activity of storm events. Once some of these "near-term measures" were in the market, rating agencies and regulators swiftly adopted these concepts demanding companies to deploy a selection of more conservative "time-dependent" models. This was despite the fact that the ultimate effect of some of these measures on insurance risk was not well understood. Apparent short-term success over the last years in near-term seasonal hurricane forecasting was brought to a halt in 2013 when these models failed to forecast the exceptional shortage of hurricanes herewith contradicting an active-year forecast. The focus of earthquake forecasting has in addition been mostly on high rather than low temporal and regional activity despite the fact that avoiding losses does not by itself create a product. This presentation sheds light on new risk management concepts for over-regional and global (re)insurance portfolios that take advantage of forecasting changes in risk. The presentation focuses on the "upside" and on new opportunities in risk-taking rather than the "downside" and the general notion that catastrophes will get worse. The focus will be on the industry's ability to hedge and optimize risk more efficiently in a changing environment.

Research Summary: Projecting Vegetation and Wildfire Response to Changing Climate and Fire Management in Interior Alaska

DTIC Science & Technology

2016-08-21

USER GUIDE Research Summary: Projecting Vegetation and Wildfire Response to Changing Climate and Fire Management in Interior Alaska SERDP Project...Summary: Projecting Vegetation and Wildfire Response to Changing Climate and Fire Management in Interior Alaska 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER...forecast landscape change in response to projected changes in climate , fire regime, and fire management. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF

Integrated Drought Monitoring and Forecasts for Decision Making in Water and Agricultural Sectors over the Southeastern US under Changing Climate

NASA Astrophysics Data System (ADS)

Arumugam, S.; Mazrooei, A.; Ward, R.

2017-12-01

Changing climate arising from structured oscillations such as ENSO and rising temperature poses challenging issues in meeting the increasing water demand (due to population growth) for public supply and agriculture over the Southeast US. This together with infrastructural (e.g., most reservoirs being within-year systems) and operational (e.g., static rule curves) constraints requires an integrated approach that seamlessly monitors and forecasts water and soil moisture conditions to support adaptive decision making in water and agricultural sectors. In this talk, we discuss the utility of an integrated drought management portal that both monitors and forecasts streamflow and soil moisture over the southeast US. The forecasts are continuously developed and updated by forcing monthly-to-seasonal climate forecasts with a land surface model for various target basins. The portal also houses a reservoir allocation model that allows water managers to explore different release policies in meeting the system constraints and target storages conditioned on the forecasts. The talk will also demonstrate how past events (e.g., 2007-2008 drought) could be proactively monitored and managed to improve decision making in water and agricultural sectors over the Southeast US. Challenges in utilizing the portal information from institutional and operational perspectives will also be presented.

Climatic Forecasting of Net Infiltration at Yucca Montain Using Analogue Meteororological Data

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

B. Faybishenko

At Yucca Mountain, Nevada, future changes in climatic conditions will most likely alter net infiltration, or the drainage below the bottom of the evapotranspiration zone within the soil profile or flow across the interface between soil and the densely welded part of the Tiva Canyon Tuff. The objectives of this paper are to: (a) develop a semi-empirical model and forecast average net infiltration rates, using the limited meteorological data from analogue meteorological stations, for interglacial (present day), and future monsoon, glacial transition, and glacial climates over the Yucca Mountain region, and (b) corroborate the computed net-infiltration rates by comparing themmore » with the empirically and numerically determined groundwater recharge and percolation rates through the unsaturated zone from published data. In this paper, the author presents an approach for calculations of net infiltration, aridity, and precipitation-effectiveness indices, using a modified Budyko's water-balance model, with reference-surface potential evapotranspiration determined from the radiation-based Penman (1948) formula. Results of calculations show that net infiltration rates are expected to generally increase from the present-day climate to monsoon climate, to glacial transition climate, and then to the glacial climate. The forecasting results indicate the overlap between the ranges of net infiltration for different climates. For example, the mean glacial net-infiltration rate corresponds to the upper-bound glacial transition net infiltration, and the lower-bound glacial net infiltration corresponds to the glacial transition mean net infiltration. Forecasting of net infiltration for different climate states is subject to numerous uncertainties-associated with selecting climate analogue sites, using relatively short analogue meteorological records, neglecting the effects of vegetation and surface runoff and runon on a local scale, as well as possible anthropogenic climate changes.« less

Stakeholders' perceptions of social-ecological systems and the information they use in the management of freshwater resources in Guanacaste, Costa Rica

NASA Astrophysics Data System (ADS)

Wong-Parodi, G.; Babcock, M.; Small, M.; Grossmann, I.

2014-12-01

Climate change is expected to increase the chances of drought, and shift precipitation patterns in seasonally dry places. In some places, the heuristics or "rules of thumb" that stakeholders use may no longer be reliable for the effective management of water resources. This can have dire consequences for social and ecological systems, especially in developing countries. Scientists and policymakers view climate forecasts as one way for improving informed decision-making about freshwater resources. However, successful communication requires that stakeholders understand and are able to use such information. To develop effective communications, it is critical to characterize stakeholders' understanding of social-ecological systems as related to water, the type of information used to inform management decisions, and the perceived value of forecast information. To achieve our objective, we conducted 40 semi-structured interviews with farmers, water managers, hydroelectric utilities, local climate experts, tourism industry representatives, and members of the general public in the semi-arid region of Guanacaste, Costa Rica. People believe that they have enough water at this time however they believe that the region will become much drier in the future, which they attribute to climate change, El Nino/La Nina, and deforestation. With respect to the value of forecast information, we found that the scale of decision-making (e.g., irrigation district versus small farmer) was associated with a stakeholders' level of "technical sophistication" and trust in government. In future work, we will evaluate the prevalence of these beliefs and practices in the larger population in order to identify effective ways to tailor the presentation of forecast information for different audiences. This work provides insight into the development of forecast communications to improve the management of resources in development countries in the face of a changing climate.

Can we use Earth Observations to improve monthly water level forecasts?

NASA Astrophysics Data System (ADS)

Slater, L. J.; Villarini, G.

2017-12-01

Dynamical-statistical hydrologic forecasting approaches benefit from different strengths in comparison with traditional hydrologic forecasting systems: they are computationally efficient, can integrate and `learn' from a broad selection of input data (e.g., General Circulation Model (GCM) forecasts, Earth Observation time series, teleconnection patterns), and can take advantage of recent progress in machine learning (e.g. multi-model blending, post-processing and ensembling techniques). Recent efforts to develop a dynamical-statistical ensemble approach for forecasting seasonal streamflow using both GCM forecasts and changing land cover have shown promising results over the U.S. Midwest. Here, we use climate forecasts from several GCMs of the North American Multi Model Ensemble (NMME) alongside 15-minute stage time series from the National River Flow Archive (NRFA) and land cover classes extracted from the European Space Agency's Climate Change Initiative 300 m annual Global Land Cover time series. With these data, we conduct systematic long-range probabilistic forecasting of monthly water levels in UK catchments over timescales ranging from one to twelve months ahead. We evaluate the improvement in model fit and model forecasting skill that comes from using land cover classes as predictors in the models. This work opens up new possibilities for combining Earth Observation time series with GCM forecasts to predict a variety of hazards from space using data science techniques.

Evaluating the sources of potential migrant species: implications under climate change

Treesearch

Ines Ibanez; James S. Clark; Michael C. Dietze

2008-01-01

As changes in climate become more apparent, ecologists face the challenge of predicting species responses to the new conditions. Most forecasts are based on climate envelopes (CE), correlative approaches that project future distributions on the basis of the current climate often assuming some dispersal lag. One major caveat with this approach is that it ignores the...

Grazing impacts on infiltration rates at Vernal Pools in the Modoc Plateau

USDA-ARS?s Scientific Manuscript database

Vernal pools are depressions of land that are seasonally inundated with water. They host rare and endemic plant and animal species and are sensitive to livestock grazing management and climate change impacts on hydrology and vegetation. Climate change forecasts predicting a hotter, drier climate sug...

The origins of computer weather prediction and climate modeling

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

Lynch, Peter

2008-03-20

Numerical simulation of an ever-increasing range of geophysical phenomena is adding enormously to our understanding of complex processes in the Earth system. The consequences for mankind of ongoing climate change will be far-reaching. Earth System Models are capable of replicating climate regimes of past millennia and are the best means we have of predicting the future of our climate. The basic ideas of numerical forecasting and climate modeling were developed about a century ago, long before the first electronic computer was constructed. There were several major practical obstacles to be overcome before numerical prediction could be put into practice. Amore » fuller understanding of atmospheric dynamics allowed the development of simplified systems of equations; regular radiosonde observations of the free atmosphere and, later, satellite data, provided the initial conditions; stable finite difference schemes were developed; and powerful electronic computers provided a practical means of carrying out the prodigious calculations required to predict the changes in the weather. Progress in weather forecasting and in climate modeling over the past 50 years has been dramatic. In this presentation, we will trace the history of computer forecasting through the ENIAC integrations to the present day. The useful range of deterministic prediction is increasing by about one day each decade, and our understanding of climate change is growing rapidly as Earth System Models of ever-increasing sophistication are developed.« less

The origins of computer weather prediction and climate modeling

NASA Astrophysics Data System (ADS)

Lynch, Peter

2008-03-01

Numerical simulation of an ever-increasing range of geophysical phenomena is adding enormously to our understanding of complex processes in the Earth system. The consequences for mankind of ongoing climate change will be far-reaching. Earth System Models are capable of replicating climate regimes of past millennia and are the best means we have of predicting the future of our climate. The basic ideas of numerical forecasting and climate modeling were developed about a century ago, long before the first electronic computer was constructed. There were several major practical obstacles to be overcome before numerical prediction could be put into practice. A fuller understanding of atmospheric dynamics allowed the development of simplified systems of equations; regular radiosonde observations of the free atmosphere and, later, satellite data, provided the initial conditions; stable finite difference schemes were developed; and powerful electronic computers provided a practical means of carrying out the prodigious calculations required to predict the changes in the weather. Progress in weather forecasting and in climate modeling over the past 50 years has been dramatic. In this presentation, we will trace the history of computer forecasting through the ENIAC integrations to the present day. The useful range of deterministic prediction is increasing by about one day each decade, and our understanding of climate change is growing rapidly as Earth System Models of ever-increasing sophistication are developed.

Operational seasonal forecasting of crop performance.

PubMed

Stone, Roger C; Meinke, Holger

2005-11-29

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.

Operational seasonal forecasting of crop performance

PubMed Central

Stone, Roger C; Meinke, Holger

2005-01-01

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production. PMID:16433097

Beyond Climate and Weather Science: Expanding the Forecasting Family to Serve Societal Needs

NASA Astrophysics Data System (ADS)

Barron, E. J.

2009-05-01

The ability to "anticipate" the future is what makes information from the Earth sciences valuable to society - whether it is the prediction of severe weather or the future availability of water resources in response to climate change. An improved ability to anticipate or forecast has the potential to serve society by simultaneously improving our ability to (1) promote economic vitality, (2) enable environmental stewardship, (3) protect life and property, as well as (4) improve our fundamental knowledge of the earth system. The potential is enormous, yet many appear ready to move quickly toward specific mitigation and adaptation strategies assuming that the science is settled. Five important weakness must be addressed first: (1) the formation of a true "climate services" function and capability, (2) the deliberate investment in expanding the family of forecasting elements to incorporate a broader array of environmental factors and impacts, (3) the investment in the sciences that connect climate to society, (4) a deliberate focus on the problems associated with scale, in particular the difference between the scale of predictive models and the scale associated with societal decisions, and (5) the evolution from climate services and model predictions to the equivalent of "environmental intelligence centers." The objective is to bring the discipline of forecasting to a broader array of environmental challenges. Assessments of the potential impacts of global climate change on societal sectors such as water, human health, and agriculture provide good examples of this challenge. We have the potential to move from a largely reactive mode in addressing adverse health outcomes, for example, to one in which the ties between climate, land cover, infectious disease vectors, and human health are used to forecast and predict adverse human health conditions. The potential exists for a revolution in forecasting, that entrains a much broader set of societal needs and solutions. The argument is made that (for example) the current capabilities in the prediction of environmental health is similar to the capabilities (and potential) of weather forecasting in the 1960's.

A seamless global hydrological monitoring and forecasting system for water resources assessment and hydrological hazard early warning

NASA Astrophysics Data System (ADS)

Sheffield, Justin; He, Xiaogang; Wood, Eric; Pan, Ming; Wanders, Niko; Zhan, Wang; Peng, Liqing

2017-04-01

Sustainable management of water resources and mitigation of the impacts of hydrological hazards are becoming ever more important at large scales because of inter-basin, inter-country and inter-continental connections in water dependent sectors. These include water resources management, food production, and energy production, whose needs must be weighed against the water needs of ecosystems and preservation of water resources for future generations. The strains on these connections are likely to increase with climate change and increasing demand from burgeoning populations and rapid development, with potential for conflict over water. At the same time, network connections may provide opportunities to alleviate pressures on water availability through more efficient use of resources such as trade in water dependent goods. A key constraint on understanding, monitoring and identifying solutions to increasing competition for water resources and hazard risk is the availability of hydrological data for monitoring and forecasting water resources and hazards. We present a global online system that provides continuous and consistent water products across time scales, from the historic instrumental period, to real-time monitoring, short-term and seasonal forecasts, and climate change projections. The system is intended to provide data and tools for analysis of historic hydrological variability and trends, water resources assessment, monitoring of evolving hazards and forecasts for early warning, and climate change scale projections of changes in water availability and extreme events. The system is particular useful for scientists and stakeholders interested in regions with less available in-situ data, and where forecasts have the potential to help decision making. The system is built on a database of high-resolution climate data from 1950 to present that merges available observational records with bias-corrected reanalysis and satellite data, which then drives a coupled land surface model-flood inundation model to produce hydrological variables and indices at daily, 0.25-degree resolution, globally. The system is updated in near real-time (< 2 days) using satellite precipitation and weather model data, and produces forecasts at short-term (out to 7 days) based on the Global Forecast System (GFS) and seasonal (up to 6 months) based on U.S. National Multi-Model Ensemble (NMME) seasonal forecasts. Climate change projections are based on bias-corrected and downscaled CMIP5 climate data that is used to force the hydrological model. Example products from the system include real-time and forecast drought indices for precipitation, soil moisture, and streamflow, and flood magnitude and extent indices. The model outputs are complemented by satellite based products and indices based on satellite data for vegetation health (MODIS NDVI) and soil moisture (SMAP). We show examples of the validation of the system at regional scales, including how local information can significantly improve predictions, and examples of how the system can be used to understand large-scale water resource issues, and in real-world contexts for early warning, decision making and planning.

Forecasting Impacts of Climate Change on Indicators of British Columbia's Biodiversity

NASA Astrophysics Data System (ADS)

Holmes, Keith Richard

Understanding the relationships between biodiversity and climate is essential for predicting the impact of climate change on broad-scale landscape processes. Utilizing indirect indicators of biodiversity derived from remotely sensed imagery, we present an approach to forecast shifts in the spatial distribution of biodiversity. Indirect indicators, such as remotely sensed plant productivity metrics, representing landscape seasonality, minimum growth, and total greenness have been linked to species richness over broad spatial scales, providing unique capacity for biodiversity modeling. Our goal is to map future spatial distributions of plant productivity metrics based on expected climate change and to quantify anticipated change to park habitat in British Columbia. Using an archival dataset sourced from the Advanced Very High Resolution Radiometer (AVHRR) satellite from the years 1987 to 2007 at 1km spatial resolution, corresponding historical climate data, and regression tree modeling, we developed regional models of the relationships between climate and annual productivity growth. Historical interconnections between climate and annual productivity were coupled with three climate change scenarios modeled by the Canadian Centre for Climate Modeling and Analysis (CCCma) to predict and map productivity components to the year 2065. Results indicate we can expect a warmer and wetter environment, which may lead to increased productivity in the north and higher elevations. Overall, seasonality is expected to decrease and greenness productivity metrics are expected to increase. The Coastal Mountains and high elevation edge habitats across British Columbia are forecasted to experience the greatest amount of change. In the future, protected areas may have potential higher greenness and lower seasonality as represented by indirect biodiversity indicators. The predictive model highlights potential gaps in protection along the central interior and Rocky Mountains. Protected areas are expected to experience the greatest change with indirect indicators located along mountainous elevations of British Columbia. Our indirect indicator approach to predict change in biodiversity provides resource managers with information to mitigate and adapt to future habitat dynamics. Spatially specific recommendations from our dataset provide information necessary for management. For instance, knowing there is a projected depletion of habitat representation in the East Rocky Mountains, sensitive species in the threatened Mountain Hemlock ecozone, or preservation of rare habitats in the decreasing greenness of the southern interior region is essential information for managers tasked with long term biodiversity conservation. Forecasting productivity levels, linked to the distribution of species richness, presents a novel approach for understanding the future implications of climate change on broad scale biodiversity.

Forecasting effects of climate change on Great Lakes fisheries: models that link habitat supply to population dynamics can help

USGS Publications Warehouse

Jones, Michael L.; Shuter, Brian J.; Zhao, Yingming; Stockwell, Jason D.

2006-01-01

Future changes to climate in the Great Lakes may have important consequences for fisheries. Evidence suggests that Great Lakes air and water temperatures have risen and the duration of ice cover has lessened during the past century. Global circulation models (GCMs) suggest future warming and increases in precipitation in the region. We present new evidence that water temperatures have risen in Lake Erie, particularly during summer and winter in the period 1965–2000. GCM forecasts coupled with physical models suggest lower annual runoff, less ice cover, and lower lake levels in the future, but the certainty of these forecasts is low. Assessment of the likely effects of climate change on fish stocks will require an integrative approach that considers several components of habitat rather than water temperature alone. We recommend using mechanistic models that couple habitat conditions to population demographics to explore integrated effects of climate-caused habitat change and illustrate this approach with a model for Lake Erie walleye (Sander vitreum). We show that the combined effect on walleye populations of plausible changes in temperature, river hydrology, lake levels, and light penetration can be quite different from that which would be expected based on consideration of only a single factor.

Effects of Solar UV Radiation and Climate Change on Biogeochemical Cycling: Interactions and Feedbacks

EPA Science Inventory

Solar UV radiation, climate and other drivers of global change are undergoing significant changes and models forecast that these changes will continue for the remainder of this century. Here we assess the effects of solar UV radiation on biogeochemical cycles and the interactions...

Predicting ecosystem shifts requires new approaches that integrate the effects of climate change across entire systems

PubMed Central

Russell, Bayden D.; Harley, Christopher D. G.; Wernberg, Thomas; Mieszkowska, Nova; Widdicombe, Stephen; Hall-Spencer, Jason M.; Connell, Sean D.

2012-01-01

Most studies that forecast the ecological consequences of climate change target a single species and a single life stage. Depending on climatic impacts on other life stages and on interacting species, however, the results from simple experiments may not translate into accurate predictions of future ecological change. Research needs to move beyond simple experimental studies and environmental envelope projections for single species towards identifying where ecosystem change is likely to occur and the drivers for this change. For this to happen, we advocate research directions that (i) identify the critical species within the target ecosystem, and the life stage(s) most susceptible to changing conditions and (ii) the key interactions between these species and components of their broader ecosystem. A combined approach using macroecology, experimentally derived data and modelling that incorporates energy budgets in life cycle models may identify critical abiotic conditions that disproportionately alter important ecological processes under forecasted climates. PMID:21900317

Potential economic value of drought information to support early warning in Africa

NASA Astrophysics Data System (ADS)

Quiroga, S.; Iglesias, A.; Diz, A.; Garrote, L.

2012-04-01

We present a methodology to estimate the economic value of advanced climate information for food production in Africa under climate change scenarios. The results aim to facilitate better choices in water resources management. The methodology includes 4 sequential steps. First two contrasting management strategies (with and without early warning) are defined. Second, the associated impacts of the management actions are estimated by calculating the effect of drought in crop productivity under climate change scenarios. Third, the optimal management option is calculated as a function of the drought information and risk aversion of potential information users. Finally we use these optimal management simulations to compute the economic value of enhanced water allocation rules to support stable food production in Africa. Our results show how a timely response to climate variations can help reduce loses in food production. The proposed framework is developed within the Dewfora project (Early warning and forecasting systems to predict climate related drought vulnerability and risk in Africa) that aims to improve the knowledge on drought forecasting, warning and mitigation, and advance the understanding of climate related vulnerability to drought and to develop a prototype operational forecasting.

WRF Test on IBM BG/L:Toward High Performance Application to Regional Climate Research

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

Chin, H S

The effects of climate change will mostly be felt on local to regional scales (Solomon et al., 2007). To develop better forecast skill in regional climate change, an integrated multi-scale modeling capability (i.e., a pair of global and regional climate models) becomes crucially important in understanding and preparing for the impacts of climate change on the temporal and spatial scales that are critical to California's and nation's future environmental quality and economical prosperity. Accurate knowledge of detailed local impact on the water management system from climate change requires a resolution of 1km or so. To this end, a high performancemore » computing platform at the petascale appears to be an essential tool in providing such local scale information to formulate high quality adaptation strategies for local and regional climate change. As a key component of this modeling system at LLNL, the Weather Research and Forecast (WRF) model is implemented and tested on the IBM BG/L machine. The objective of this study is to examine the scaling feature of WRF on BG/L for the optimal performance, and to assess the numerical accuracy of WRF solution on BG/L.« less

Forecasting regional to global plant migration in response to climate change.

Treesearch

Ronald P. Neilson; Louis F. Pitelka; Allen M. Solomon; Ran Nathan; Guy F. Midgley; Jóse M. Fragoso; Heike Lischke; Ken Thompson

2005-01-01

The rate of future climate change is likely to exceed the migration rates of most plant species. The replacement of dominant species by locally rare species may require decades, and extinctions may occur when plant species cannot migrate fast enough to escape the consequences of climate change. Such lags may impair ecosystem services, such as carbon sequestration and...

Climate forecasts in disaster management: Red Cross flood operations in West Africa, 2008.

PubMed

Braman, Lisette Martine; van Aalst, Maarten Krispijn; Mason, Simon J; Suarez, Pablo; Ait-Chellouche, Youcef; Tall, Arame

2013-01-01

In 2008, the International Federation of Red Cross and Red Crescent Societies (IFRC) used a seasonal forecast for West Africa for the first time to implement an Early Warning, Early Action strategy for enhanced flood preparedness and response. Interviews with disaster managers suggest that this approach improved their capacity and response. Relief supplies reached flood victims within days, as opposed to weeks in previous years, thereby preventing further loss of life, illness, and setbacks to livelihoods, as well as augmenting the efficiency of resource use. This case demonstrates the potential benefits to be realised from the use of medium-to-long-range forecasts in disaster management, especially in the context of potential increases in extreme weather and climate-related events due to climate variability and change. However, harnessing the full potential of these forecasts will require continued effort and collaboration among disaster managers, climate service providers, and major humanitarian donors. © 2013 The Author(s). Journal compilation © Overseas Development Institute, 2013.

Integration of climate change considerations in statewide and regional transportation planning processes

DOT National Transportation Integrated Search

2009-07-01

This report is part on on-going work for the US Department of Transportations Center for Climate Change and Environmental Forecasting and the Federal Highway Administration to highlight innovative actions and initiatives undertaken by states and m...

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.

Forecasting of Seasonal Rainfall using ENSO and IOD teleconnection with Classification Models

NASA Astrophysics Data System (ADS)

De Silva, T.; Hornberger, G. M.

2017-12-01

Seasonal to annual forecasts of precipitation patterns are very important for water infrastructure management. In particular, such forecasts can be used to inform decisions about the operation of multipurpose reservoir systems in the face of changing climate conditions. Success in making useful forecasts often is achieved by considering climate teleconnections such as the El-Nino-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) as related to sea surface temperature variations. We present an analysis to explore the utility of using rainfall relationships in Sri Lanka with ENSO and IOD to predict rainfall to the Mahaweli, river basin. Forecasting of rainfall as classes - above normal, normal, and below normal - can be useful for water resource management decision making. Quadratic discrimination analysis (QDA) and random forest models are used to identify the patterns of rainfall classes with respect to ENSO and IOD indices. These models can be used to forecast the likelihood of areal rainfall anomalies using predicted climate indices. Results can be used for decisions regarding allocation of water for agriculture and electricity generation within the Mahaweli project of Sri Lanka.

Adaptive Regulation of the Northern California Reservoir System for Water, Energy, and Environmental Management

NASA Astrophysics Data System (ADS)

Georgakakos, A. P.; Kistenmacher, M.; Yao, H.; Georgakakos, K. P.

2014-12-01

The 2014 National Climate Assessment of the US Global Change Research Program emphasizes that water resources managers and planners in most US regions will have to cope with new risks, vulnerabilities, and opportunities, and recommends the development of adaptive capacity to effectively respond to the new water resources planning and management challenges. In the face of these challenges, adaptive reservoir regulation is becoming all the more ncessary. Water resources management in Northern California relies on the coordinated operation of several multi-objective reservoirs on the Trinity, Sacramento, American, Feather, and San Joaquin Rivers. To be effective, reservoir regulation must be able to (a) account for forecast uncertainty; (b) assess changing tradeoffs among water uses and regions; and (c) adjust management policies as conditions change; and (d) evaluate the socio-economic and environmental benefits and risks of forecasts and policies for each region and for the system as a whole. The Integrated Forecast and Reservoir Management (INFORM) prototype demonstration project operated in Northern California through the collaboration of several forecast and management agencies has shown that decision support systems (DSS) with these attributes add value to stakeholder decision processes compared to current, less flexible management practices. Key features of the INFORM DSS include: (a) dynamically downscaled operational forecasts and climate projections that maintain the spatio-temporal coherence of the downscaled land surface forcing fields within synoptic scales; (b) use of ensemble forecast methodologies for reservoir inflows; (c) assessment of relevant tradeoffs among water uses on regional and local scales; (d) development and evaluation of dynamic reservoir policies with explicit consideration of hydro-climatic forecast uncertainties; and (e) focus on stakeholder information needs.This article discusses the INFORM integrated design concept, underlying methodologies, and selected applications with the California water resources system.

The costs of climate change: ecosystem services and wildland fires

EPA Science Inventory

In this paper we use Habitat Equivalency Analysis (HEA) to monetize the avoided ecosystem services losses due to climate change-induced wildland fires in the U.S. Specifically, we use the U.S. Forest Service’s MC1 dynamic global vegetation model to forecast changes in wildland fi...

Water resources adaptation to climate and demand change in the Potomac river

USDA-ARS?s Scientific Manuscript database

The effects of climate change are increasingly considered in conjunction with changes in water demand and reservoir sedimentation in forecasts of water supply vulnerability. Here, the relative effects of these factors are evaluated for the Washington, DC metropolitan area water supply for the near f...

Forecasting regional grassland and shrubland responses to directional changes in climate using multi-year dry or wet periods

USDA-ARS?s Scientific Manuscript database

Ecologists are being challenged to predict ecosystem responses under changing climatic conditions. Although air temperatures are increasing, the magnitude and direction of change in precipitation (increase or decrease) are uncertain for many sites. Given that water availability is the primary driver...

Forecasting regional grassland and shrubland responses to directional changes in climate using multi-year dry or wet period

USDA-ARS?s Scientific Manuscript database

Ecologists are being challenged to predict ecosystem responses under changing climatic conditions. Although air temperatures are increasing, the magnitude and direction of change in precipitation (increase or decrease) are uncertain for many sites. Given that water availability is the primary driver...

Jump-Diffusion models and structural changes for asset forecasting in hydrology

NASA Astrophysics Data System (ADS)

Tranquille Temgoua, André Guy; Martel, Richard; Chang, Philippe J. J.; Rivera, Alfonso

2017-04-01

Impacts of climate change on surface water and groundwater are of concern in many regions of the world since water is an essential natural resource. Jump-Diffusion models are generally used in economics and other related fields but not in hydrology. The potential application could be made for hydrologic data series analysis and forecast. The present study uses Jump-Diffusion models by adding structural changes to detect fluctuations in hydrologic processes in relationship with climate change. The model implicitly assumes that modifications in rivers' flowrates can be divided into three categories: (a) normal changes due to irregular precipitation events especially in tropical regions causing major disturbance in hydrologic processes (this component is modelled by a discrete Brownian motion); (b) abnormal, sudden and non-persistent modifications in hydrologic proceedings are handled by Poisson processes; (c) the persistence of hydrologic fluctuations characterized by structural changes in hydrological data related to climate variability. The objective of this paper is to add structural changes in diffusion models with jumps, in order to capture the persistence of hydrologic fluctuations. Indirectly, the idea is to observe if there are structural changes of discharge/recharge over the study area, and to find an efficient and flexible model able of capturing a wide variety of hydrologic processes. Structural changes in hydrological data are estimated using the method of nonlinear discrete filters via Method of Simulated Moments (MSM). An application is given using sensitive parameters such as baseflow index and recession coefficient to capture discharge/recharge. Historical dataset are examined by the Volume Spread Analysis (VSA) to detect real time and random perturbations in hydrologic processes. The application of the method allows establishing more accurate hydrologic parameters. The impact of this study is perceptible in forecasting floods and groundwater recession. Keywords: hydrologic processes, Jump-Diffusion models, structural changes, forecast, climate change

Observed forest sensitivity to climate implies large changes in 21st century North American forest growth.

PubMed

Charney, Noah D; Babst, Flurin; Poulter, Benjamin; Record, Sydne; Trouet, Valerie M; Frank, David; Enquist, Brian J; Evans, Margaret E K

2016-09-01

Predicting long-term trends in forest growth requires accurate characterisation of how the relationship between forest productivity and climatic stress varies across climatic regimes. Using a network of over two million tree-ring observations spanning North America and a space-for-time substitution methodology, we forecast climate impacts on future forest growth. We explored differing scenarios of increased water-use efficiency (WUE) due to CO2 -fertilisation, which we simulated as increased effective precipitation. In our forecasts: (1) climate change negatively impacted forest growth rates in the interior west and positively impacted forest growth along the western, southeastern and northeastern coasts; (2) shifting climate sensitivities offset positive effects of warming on high-latitude forests, leaving no evidence for continued 'boreal greening'; and (3) it took a 72% WUE enhancement to compensate for continentally averaged growth declines under RCP 8.5. Our results highlight the importance of locally adapted forest management strategies to handle regional differences in growth responses to climate change. © 2016 John Wiley & Sons Ltd/CNRS.

Short Term Weather Forecasting and Long Term Climate Predictions in Mesoamerica

NASA Astrophysics Data System (ADS)

Hardin, D. M.; Daniel, I.; Mecikalski, J.; Graves, S.

2008-05-01

The SERVIR project utilizes several predictive models to support regional monitoring and decision support in Mesoamerica. Short term forecasts ranging from a few hours to several days produce more than 30 data products that are used daily by decision makers, as well as news organizations in the region. The forecast products can be visualized in both two and three dimensional viewers such as Google Maps and Google Earth. Other viewers developed specifically for the Mesoamerican region by the University of Alabama in Huntsville and the Institute for the Application of Geospatial Technologies in Auburn New York can also be employed. In collaboration with the NASA Short Term Prediction Research and Transition (SpoRT) Center SERVIR utilizes the Weather Research and Forecast (WRF) model to produce short-term (24 hr) regional weather forecasts twice a day. Temperature, precipitation, wind, and other variables are forecast in 10km and 30km grids over the Mesoamerica region. Using the PSU/NCAR Mesoscale Model, known as MM5, SERVIR produces 48 hour- forecasts of soil temperature, two meter surface temperature, three hour accumulated precipitation, winds at different heights, and other variables. These are forecast hourly in 9km grids. Working in collaboration with the Atmospheric Science Department of the University of Alabama in Huntsville produces a suite of short-term (0-6 hour) weather prediction products are generated. These "convective initiation" products predict the onset of thunderstorm rainfall and lightning within a 1-hour timeframe. Models are also employed for long term predictions. The SERVIR project, under USAID funding, has developed comprehensive regional climate change scenarios of Mesoamerica for future years: 2010, 2015, 2025, 2050, and 2099. These scenarios were created using the Pennsylvania State University/National Center for Atmospheric Research (MM5) model and processed on the Oak Ridge National Laboratory Cheetah supercomputer. The goal of these Mesoamerican climate change scenarios is to better understand the regional climate, the major controls, and how it might be expected to change in the future. This presentation will present a summary of the model results and show the application of these data in preparation for and response to recent tropical storms.

Observed Impacts. Chapter 19

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia

1999-01-01

Agricultural applications of El Nino forecasts are already underway in some countries and need to be evaluated or re-evaluated. For example, in Peru, El Nino forecasts have been incorporated into national planning for the agricultural sector, and areas planted with rice and cotton (cotton being the more drought-tolerant crop) are adjusted accordingly. How well are this and other such programs working? Such evaluations will contribute to the governmental and intergovernmental institutions, including the Inter-American Institute for Global Change Research and the US National Ocean and Atmospheric Agency that are fostering programs to aid the effective use of forecasts. This research involves expanding, deepening, and applying the understanding of physical climate to the fields of agronomy and social science; and the reciprocal understanding of crop growth and farm economics to climatology. Delivery of a regional climate forecast with no information about how the climate forecast was derived limits its effectiveness. Explanation of a region's major climate driving forces helps to place a seasonal forecast in context. Then, a useful approach is to show historical responses to previous El Nino events, and projections, with uncertainty intervals, of crop response from dynamic process crop growth models. Regional forecasts should be updated with real-time weather conditions. Since every El Nino event is different, it is important to track, report and advise on each new event as it unfolds.

Abrupt shifts in phenology and vegetation productivity under climate extremes

USDA-ARS?s Scientific Manuscript database

Amplification of the hydrologic cycle as a consequence of global warming is predicted to increase climate variability and the frequency and severity of droughts. Predicting how ecosystems will be affected by climate change requires not only reliable forecasts of future climate, but also observationa...

Simulation of climate change effects on streamflow, groundwater, and stream temperature using GSFLOW and SNTEMP in the Black Earth Creek Watershed, Wisconsin

USGS Publications Warehouse

Hunt, Randall J.; Westenbroek, Stephen M.; Walker, John F.; Selbig, William R.; Regan, R. Steven; Leaf, Andrew T.; Saad, David A.

2016-08-23

Potential future changes in air temperature drivers were consistently upward regardless of General Circulation Model and emission scenario selected; thus, simulated stream temperatures are forecast to increase appreciably with future climate. However, the amount of temperature increase was variable. Such uncertainty is reflected in temperature model results, along with uncertainty in the groundwater/surface-water interaction itself. The estimated increase in annual average temperature ranged from approximately 3 to 6 degrees Celsius by 2100 in the upper reaches of Black Earth Creek and 2 to 4 degrees Celsius in reaches farther downstream. As with all forecasts that rely on projections of an unknowable future, the results are best considered to approximate potential outcomes of climate change given the underlying uncertainty.

The global climate change effect on the Altai region's climate in the first half of XXI century

NASA Astrophysics Data System (ADS)

Lagutin, Anatoly A.; Volkov, Nikolai V.; Makushev, Konstantin M.; Mordvin, Egor Yu.

2017-11-01

We investigate an effect of global climate system change on climate of Altai region. It is shown that a data of the RegCM4 regional climate model, obtained for contemporary and future periods, within an approach which is based on standard Euclidean distance, allows to define specific zones in which climate change is forecasted. Such zones have been defined for the Altai region territory within the framework of global radiative forcing scenarios RCP 4.5 and RCP 8.5 for the middle of XXI century.

Enhancing Famine Early Warning Systems with Improved Forecasts, Satellite Observations and Hydrologic Simulations

NASA Astrophysics Data System (ADS)

Funk, C. C.; Verdin, J.; Thiaw, W. M.; Hoell, A.; Korecha, D.; McNally, A.; Shukla, S.; Arsenault, K. R.; Magadzire, T.; Novella, N.; Peters-Lidard, C. D.; Robjohn, M.; Pomposi, C.; Galu, G.; Rowland, J.; Budde, M. E.; Landsfeld, M. F.; Harrison, L.; Davenport, F.; Husak, G. J.; Endalkachew, E.

2017-12-01

Drought early warning science, in support of famine prevention, is a rapidly advancing field that is helping to save lives and livelihoods. In 2015-2017, a series of extreme droughts afflicted Ethiopia, Southern Africa, Eastern Africa in OND and Eastern Africa in MAM, pushing more than 50 million people into severe food insecurity. Improved drought forecasts and monitoring tools, however, helped motivate and target large and effective humanitarian responses. Here we describe new science being developed by a long-established early warning system - the USAID Famine Early Warning Systems Network (FEWS NET). FEWS NET is a leading provider of early warning and analysis on food insecurity. FEWS NET research is advancing rapidly on several fronts, providing better climate forecasts and more effective drought monitoring tools that are being used to support enhanced famine early warning. We explore the philosophy and science underlying these successes, suggesting that a modal view of climate change can support enhanced seasonal prediction. Under this modal perspective, warming of the tropical oceans may interact with natural modes of variability, like the El Niño-Southern Oscillation, to enhance Indo-Pacific sea surface temperature gradients during both El Niño and La Niña-like climate states. Using empirical data and climate change simulations, we suggest that a sequence of droughts may commence in northern Ethiopia and Southern Africa with the advent of a moderate-to-strong El Niño, and then continue with La Niña/West Pacific related droughts in equatorial eastern East Africa. Scientifically, we show that a new hybrid statistical-dynamic precipitation forecast system, the FEWS NET Integrated Forecast System (FIFS), based on reformulations of the Global Ensemble Forecast System weather forecasts and National Multi-Model Ensemble (NMME) seasonal climate predictions, can effectively anticipate recent East and Southern African drought events. Using cross-validation, we evaluate FIFS' skill and compare it to the NMME and the International Research Institute forecasts. Our study concludes with an overview of the satellite observations provided by FEWS NET partners at NOAA, NASA, USGS, and UC Santa Barbara, and the assimilation of these products within the FEWS NET Land Data Assimilation System (FLDAS).

Water and Power Systems Co-optimization under a High Performance Computing Framework

NASA Astrophysics Data System (ADS)

Xuan, Y.; Arumugam, S.; DeCarolis, J.; Mahinthakumar, K.

2016-12-01

Water and energy systems optimizations are traditionally being treated as two separate processes, despite their intrinsic interconnections (e.g., water is used for hydropower generation, and thermoelectric cooling requires a large amount of water withdrawal). Given the challenges of urbanization, technology uncertainty and resource constraints, and the imminent threat of climate change, a cyberinfrastructure is needed to facilitate and expedite research into the complex management of these two systems. To address these issues, we developed a High Performance Computing (HPC) framework for stochastic co-optimization of water and energy resources to inform water allocation and electricity demand. The project aims to improve conjunctive management of water and power systems under climate change by incorporating improved ensemble forecast models of streamflow and power demand. First, by downscaling and spatio-temporally disaggregating multimodel climate forecasts from General Circulation Models (GCMs), temperature and precipitation forecasts are obtained and input into multi-reservoir and power systems models. Extended from Optimus (Optimization Methods for Universal Simulators), the framework drives the multi-reservoir model and power system model, Temoa (Tools for Energy Model Optimization and Analysis), and uses Particle Swarm Optimization (PSO) algorithm to solve high dimensional stochastic problems. The utility of climate forecasts on the cost of water and power systems operations is assessed and quantified based on different forecast scenarios (i.e., no-forecast, multimodel forecast and perfect forecast). Analysis of risk management actions and renewable energy deployments will be investigated for the Catawba River basin, an area with adequate hydroclimate predicting skill and a critical basin with 11 reservoirs that supplies water and generates power for both North and South Carolina. Further research using this scalable decision supporting framework will provide understanding and elucidate the intricate and interdependent relationship between water and energy systems and enhance the security of these two critical public infrastructures.

EcoPAD, an interactive platform for near real-time ecological forecasting by assimilating data into model

NASA Astrophysics Data System (ADS)

MA, S.; Huang, Y.; Stacy, M.; Jiang, J.; Sundi, N.; Ricciuto, D. M.; Hanson, P. J.; Luo, Y.; Saruta, V.

2017-12-01

Ecological forecasting is critical in various aspects of our coupled human-nature systems, such as disaster risk reduction, natural resource management and climate change mitigation. Novel advancements are in urgent need to deepen our understandings of ecosystem dynamics, boost the predictive capacity of ecology, and provide timely and effective information for decision-makers in a rapidly changing world. Our study presents a smart system - Ecological Platform for Assimilation of Data (EcoPAD) - which streamlines web request-response, data management, model execution, result storage and visualization. EcoPAD allows users to (i) estimate model parameters or state variables, (ii) quantify uncertainty of estimated parameters and projected states of ecosystems, (iii) evaluate model structures, (iv) assess sampling strategies, (v) conduct ecological forecasting, and (vi) detect ecosystem acclimation to climate change. One of the key innovations of the web-based EcoPAD is the automated near- or real-time forecasting of ecosystem dynamics with uncertainty fully quantified. The user friendly webpage enables non-modelers to explore their data for simulation and data assimilation. As a case study, we applied EcoPAD to the Spruce and Peatland Responses Under Climatic and Environmental Change Experiment (SPRUCE), a whole ecosystem warming and CO2 enrichment treatment project in the northern peatland, assimilated multiple data streams into a process based ecosystem model, enhanced timely feedback between modelers and experimenters, ultimately improved ecosystem forecasting and made better use of current knowledge. Built in a framework with flexible API, EcoPAD is easily portable and will benefit scientific communities, policy makers as well as the general public.

Climate extremes in the Pacific: improving seasonal prediction of tropical cyclones and extreme ocean temperatures to improve resilience

NASA Astrophysics Data System (ADS)

Kuleshov, Y.; Jones, D.; Spillman, C. M.

2012-04-01

Climate change and climate extremes have a major impact on Australia and Pacific Island countries. Of particular concern are tropical cyclones and extreme ocean temperatures, the first being the most destructive events for terrestrial systems, while the latter has the potential to devastate ocean ecosystems through coral bleaching. As a practical response to climate change, under the Pacific-Australia Climate Change Science and Adaptation Planning program (PACCSAP), we are developing enhanced web-based information tools for providing seasonal forecasts for climatic extremes in the Western Pacific. Tropical cyclones are the most destructive weather systems that impact on coastal areas. Interannual variability in the intensity and distribution of tropical cyclones is large, and presently greater than any trends that are ascribable to climate change. In the warming environment, predicting tropical cyclone occurrence based on historical relationships, with predictors such as sea surface temperatures (SSTs) now frequently lying outside of the range of past variability meaning that it is not possible to find historical analogues for the seasonal conditions often faced by Pacific countries. Elevated SSTs are the primary trigger for mass coral bleaching events, which can lead to widespread damage and mortality on reef systems. Degraded coral reefs present many problems, including long-term loss of tourism and potential loss or degradation of fisheries. The monitoring and prediction of thermal stress events enables the support of a range of adaptive and management activities that could improve reef resilience to extreme conditions. Using the climate model POAMA (Predictive Ocean-Atmosphere Model for Australia), we aim to improve accuracy of seasonal forecasts of tropical cyclone activity and extreme SSTs for the regions of Western Pacific. Improved knowledge of extreme climatic events, with the assistance of tailored forecast tools, will help enhance the resilience and adaptive capacity of Australia and Pacific Island Countries under climate change. Acknowledgement The research discussed in this paper was conducted with the support of the PACCSAP supported by the AusAID and Department of Climate Change and Energy Efficiency and delivered by the Bureau of Meteorology and CSIRO.

Updating Known Distribution Models for Forecasting Climate Change Impact on Endangered Species

PubMed Central

Muñoz, Antonio-Román; Márquez, Ana Luz; Real, Raimundo

2013-01-01

To plan endangered species conservation and to design adequate management programmes, it is necessary to predict their distributional response to climate change, especially under the current situation of rapid change. However, these predictions are customarily done by relating de novo the distribution of the species with climatic conditions with no regard of previously available knowledge about the factors affecting the species distribution. We propose to take advantage of known species distribution models, but proceeding to update them with the variables yielded by climatic models before projecting them to the future. To exemplify our proposal, the availability of suitable habitat across Spain for the endangered Bonelli's Eagle (Aquila fasciata) was modelled by updating a pre-existing model based on current climate and topography to a combination of different general circulation models and Special Report on Emissions Scenarios. Our results suggested that the main threat for this endangered species would not be climate change, since all forecasting models show that its distribution will be maintained and increased in mainland Spain for all the XXI century. We remark on the importance of linking conservation biology with distribution modelling by updating existing models, frequently available for endangered species, considering all the known factors conditioning the species' distribution, instead of building new models that are based on climate change variables only. PMID:23840330

Updating known distribution models for forecasting climate change impact on endangered species.

PubMed

Muñoz, Antonio-Román; Márquez, Ana Luz; Real, Raimundo

2013-01-01

To plan endangered species conservation and to design adequate management programmes, it is necessary to predict their distributional response to climate change, especially under the current situation of rapid change. However, these predictions are customarily done by relating de novo the distribution of the species with climatic conditions with no regard of previously available knowledge about the factors affecting the species distribution. We propose to take advantage of known species distribution models, but proceeding to update them with the variables yielded by climatic models before projecting them to the future. To exemplify our proposal, the availability of suitable habitat across Spain for the endangered Bonelli's Eagle (Aquila fasciata) was modelled by updating a pre-existing model based on current climate and topography to a combination of different general circulation models and Special Report on Emissions Scenarios. Our results suggested that the main threat for this endangered species would not be climate change, since all forecasting models show that its distribution will be maintained and increased in mainland Spain for all the XXI century. We remark on the importance of linking conservation biology with distribution modelling by updating existing models, frequently available for endangered species, considering all the known factors conditioning the species' distribution, instead of building new models that are based on climate change variables only.

Development of a Climate Resilience Screening Index (CRSI) ...

EPA Pesticide Factsheets

A Climate Resilience Screening Index is being developed that is applicable at multiple scales for the United States. Those scales include national, state, county and community. The index will be applied at the first three scales and at selected communities. The index was developed in order to explicitly include domains, indicators and metrics addressing environmental, economic and societal aspects of climate resilience. In addition, the index uses indicators and metrics that assess ecosystem, economic, governance and social services at these scales. Finally, we are developing forecasting approaches that can relate intended changes in services and governance to likely levels of changes in the resiliency of communities to climate change impacts. The present challenge is the incorporation of the index, its relationships to governance and the developing forecasting tools into Federal decision-making across US government and into state/county/community decision-making across the US. Governmental acceptance of changes to policies often can be just as challenging as the initial technical acceptance of the index and its relation to climate change. Climate Resilience Index is a requested product by ORD AA and EPA Administrator through SHC Program. Index needed to assess states', counties', and communities' abilities of recovery from climate events. Audience: Internal EPA (Administrator, IO, OLEM, OW and OAR) and external (states, counties and communities). Product

Framework for Probabilistic Projections of Energy-Relevant Streamflow Indicators under Climate Change Scenarios for the U.S.

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

Wagener, Thorsten; Mann, Michael; Crane, Robert

2014-04-29

This project focuses on uncertainty in streamflow forecasting under climate change conditions. The objective is to develop easy to use methodologies that can be applied across a range of river basins to estimate changes in water availability for realistic projections of climate change. There are three major components to the project: Empirical downscaling of regional climate change projections from a range of Global Climate Models; Developing a methodology to use present day information on the climate controls on the parameterizations in streamflow models to adjust the parameterizations under future climate conditions (a trading-space-for-time approach); and Demonstrating a bottom-up approach tomore » establishing streamflow vulnerabilities to climate change. The results reinforce the need for downscaling of climate data for regional applications, and further demonstrates the challenges of using raw GCM data to make local projections. In addition, it reinforces the need to make projections across a range of global climate models. The project demonstrates the potential for improving streamflow forecasts by using model parameters that are adjusted for future climate conditions, but suggests that even with improved streamflow models and reduced climate uncertainty through the use of downscaled data, there is still large uncertainty is the streamflow projections. The most useful output from the project is the bottom-up vulnerability driven approach to examining possible climate and land use change impacts on streamflow. Here, we demonstrate an inexpensive and easy to apply methodology that uses Classification and Regression Trees (CART) to define the climate and environmental parameters space that can produce vulnerabilities in the system, and then feeds in the downscaled projections to determine the probability top transitioning to a vulnerable sate. Vulnerabilities, in this case, are defined by the end user.« less

Climate Prediction Center - Outlooks: CFS Forecast of Seasonal Climate

Science.gov Websites

National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site government Web resources and services. CFS Seasonal Climate Forecasts CFS Forecast of Seasonal Climate discontinued after October 2012. This page displays seasonal climate anomalies from the NCEP coupled forecast

Forecasting the future risk of Barmah Forest virus disease under climate change scenarios in Queensland, Australia.

PubMed

Naish, Suchithra; Mengersen, Kerrie; Hu, Wenbiao; Tong, Shilu

2013-01-01

Mosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV) disease under climate change scenarios in Queensland, Australia. We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000-2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections. We conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland.

Predicting impacts of climate change on Fasciola hepatica risk.

PubMed

Fox, Naomi J; White, Piran C L; McClean, Colin J; Marion, Glenn; Evans, Andy; Hutchings, Michael R

2011-01-10

Fasciola hepatica (liver fluke) is a physically and economically devastating parasitic trematode whose rise in recent years has been attributed to climate change. Climate has an impact on the free-living stages of the parasite and its intermediate host Lymnaea truncatula, with the interactions between rainfall and temperature having the greatest influence on transmission efficacy. There have been a number of short term climate driven forecasts developed to predict the following season's infection risk, with the Ollerenshaw index being the most widely used. Through the synthesis of a modified Ollerenshaw index with the UKCP09 fine scale climate projection data we have developed long term seasonal risk forecasts up to 2070 at a 25 km square resolution. Additionally UKCIP gridded datasets at 5 km square resolution from 1970-2006 were used to highlight the climate-driven increase to date. The maps show unprecedented levels of future fasciolosis risk in parts of the UK, with risk of serious epidemics in Wales by 2050. The seasonal risk maps demonstrate the possible change in the timing of disease outbreaks due to increased risk from overwintering larvae. Despite an overall long term increase in all regions of the UK, spatio-temporal variation in risk levels is expected. Infection risk will reduce in some areas and fluctuate greatly in others with a predicted decrease in summer infection for parts of the UK due to restricted water availability. This forecast is the first approximation of the potential impacts of climate change on fasciolosis risk in the UK. It can be used as a basis for indicating where active disease surveillance should be targeted and where the development of improved mitigation or adaptation measures is likely to bring the greatest benefits.

Climate-growth relationships for yellow-poplar across structural and site quality gradients in the southern Appalachian Mountains

Treesearch

Tara L. Keyser; Peter M. Brown

2014-01-01

Forecasted changes in climate across the southeastern US include an increase in temperature along with more variable precipitation patterns, including an increase in the severity and frequency of drought events. As such, the management of forests for increased resistance or resilience to the direct and indirect effects of climate change, including decreased tree- and...

Extinction debt from climate change for frogs in the wet tropics

PubMed Central

Brook, Barry W.; Hoskin, Conrad J.; Pressey, Robert L.; VanDerWal, Jeremy; Williams, Stephen E.

2016-01-01

The effect of twenty-first-century climate change on biodiversity is commonly forecast based on modelled shifts in species ranges, linked to habitat suitability. These projections have been coupled with species–area relationships (SAR) to infer extinction rates indirectly as a result of the loss of climatically suitable areas and associated habitat. This approach does not model population dynamics explicitly, and so accepts that extinctions might occur after substantial (but unknown) delays—an extinction debt. Here we explicitly couple bioclimatic envelope models of climate and habitat suitability with generic life-history models for 24 species of frogs found in the Australian Wet Tropics (AWT). We show that (i) as many as four species of frogs face imminent extinction by 2080, due primarily to climate change; (ii) three frogs face delayed extinctions; and (iii) this extinction debt will take at least a century to be realized in full. Furthermore, we find congruence between forecast rates of extinction using SARs, and demographic models with an extinction lag of 120 years. We conclude that SAR approaches can provide useful advice to conservation on climate change impacts, provided there is a good understanding of the time lags over which delayed extinctions are likely to occur. PMID:27729484

Climate science and famine early warning

USGS Publications Warehouse

Verdin, James P.; Funk, Chris; Senay, Gabriel B.; Choularton, R.

2005-01-01

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Climate science and famine early warning.

PubMed

Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard

2005-11-29

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Climate science and famine early warning

PubMed Central

Verdin, James; Funk, Chris; Senay, Gabriel; Choularton, Richard

2005-01-01

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised. PMID:16433101

Forecasting the combined effects of urbanization and climate change on stream ecosystems: from impacts to management options

USGS Publications Warehouse

Nelson, Kären C.; Palmer, Margaret A.; Pizzuto, James E.; Moglen, Glenn E.; Angermeier, Paul L.; Hilderbrand, Robert H.; Dettinger, Mike; Hayhoe, Katharine

2009-01-01

Synthesis and applications. The interaction of climate change and urban growth may entail significant reconfiguring of headwater streams, including a loss of ecosystem structure and services, which will be more costly than climate change alone. On local scales, stakeholders cannot control climate drivers but they can mitigate stream impacts via careful land use. Therefore, to conserve stream ecosystems, we recommend that proactive measures be taken to insure against species loss or severe population declines. Delays will inevitably exacerbate the impacts of both climate change and urbanization on headwater systems.

Application of global weather and climate model output to the design and operation of wind-energy systems

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

Curry, Judith

This project addressed the challenge of providing weather and climate information to support the operation, management and planning for wind-energy systems. The need for forecast information is extending to longer projection windows with increasing penetration of wind power into the grid and also with diminishing reserve margins to meet peak loads during significant weather events. Maintenance planning and natural gas trading is being influenced increasingly by anticipation of wind generation on timescales of weeks to months. Future scenarios on decadal time scales are needed to support assessment of wind farm siting, government planning, long-term wind purchase agreements and the regulatorymore » environment. The challenge of making wind forecasts on these longer time scales is associated with a wide range of uncertainties in general circulation and regional climate models that make them unsuitable for direct use in the design and planning of wind-energy systems. To address this challenge, CFAN has developed a hybrid statistical/dynamical forecasting scheme for delivering probabilistic forecasts on time scales from one day to seven months using what is arguably the best forecasting system in the world (European Centre for Medium Range Weather Forecasting, ECMWF). The project also provided a framework to assess future wind power through developing scenarios of interannual to decadal climate variability and change. The Phase II research has successfully developed an operational wind power forecasting system for the U.S., which is being extended to Europe and possibly Asia.« less

Pacific-Australia Climate Change Science and Adaptation Planning program: supporting climate science and enhancing climate services in Pacific Island Countries

NASA Astrophysics Data System (ADS)

Kuleshov, Yuriy; Jones, David; Hendon, Harry; Charles, Andrew; Shelton, Kay; de Wit, Roald; Cottrill, Andrew; Nakaegawa, Toshiyuki; Atalifo, Terry; Prakash, Bipendra; Seuseu, Sunny; Kaniaha, Salesa

2013-04-01

Over the past few years, significant progress in developing climate science for the Pacific has been achieved through a number of research projects undertaken under the Australian government International Climate Change Adaptation Initiative (ICCAI). Climate change has major impact on Pacific Island Countries and advancement in understanding past, present and futures climate in the region is vital for island nation to develop adaptation strategies to their rapidly changing environment. This new science is now supporting new services for a wide range of stakeholders in the Pacific through the National Meteorological Agencies of the region. Seasonal climate prediction is particularly important for planning in agriculture, tourism and other weather-sensitive industries, with operational services provided by all National Meteorological Services in the region. The interaction between climate variability and climate change, for example during droughts or very warm seasons, means that much of the early impacts of climate change are being felt through seasonal variability. A means to reduce these impacts is to improve forecasts to support decision making. Historically, seasonal climate prediction has been developed based on statistical past relationship. Statistical methods relate meteorological variables (e.g. temperature and rainfall) to indices which describe large-scale environment (e.g. ENSO indices) using historical data. However, with observed climate change, statistical approaches based on historical data are getting less accurate and less reliable. Recognising the value of seasonal forecasts, we have used outputs of a dynamical model POAMA (Predictive Ocean Atmosphere Model for Australia), to develop web-based information tools (http://poama.bom.gov.au/experimental/pasap/index.shtml) which are now used by climate services in 15 partner countries in the Pacific for preparing seasonal climate outlooks. Initial comparison conducted during 2012 has shown that the predictive skill of POAMA is consistently higher than skill of statistical-based method. Presently, under the Pacific-Australia Climate Change Science and Adaptation Planning (PACCSAP) program, we are developing dynamical model-based seasonal climate prediction for climate extremes. Of particular concern are tropical cyclones which are the most destructive weather systems that impact on coastal areas of Australia and Pacific Island Countries. To analyse historical cyclone data, we developed a consolidate archive for the Southern Hemisphere and North-Western Pacific (http://www.bom.gov.au/cyclone/history/tracks/). Using dynamical climate models (POAMA and Japan Meteorological Agency's model), we work on improving accuracy of seasonal forecasts of tropical cyclone activity for the regions of Western Pacific. Improved seasonal climate prediction based on dynamical models will further enhance climate services in Australia and Pacific Island Countries.

Improving regional climate and hydrological forecasting following the record setting flooding across the Lake Ontario - St. Lawrence River system

NASA Astrophysics Data System (ADS)

Gronewold, A.; Seglenieks, F.; Bruxer, J.; Fortin, V.; Noel, J.

2017-12-01

In the spring of 2017, water levels across Lake Ontario and the upper St. Lawrence River exceeded record high levels, leading to widespread flooding, damage to property, and controversy over regional dam operating protocols. Only a few years earlier, water levels on Lakes Superior, Michigan, and Huron (upstream of Lake Ontario) had dropped to record low levels leading to speculation that either anthropogenic controls or climate change were leading to chronic water loss from the Great Lakes. The contrast between low water level conditions across Earth's largest lake system from the late 1990s through 2013, and the rapid rise prior to the flooding in early 2017, underscores the challenges of quantifying and forecasting hydrologic impacts of rising regional air and water temperatures (and associated changes in lake evaporation) and persistent increases in long-term precipitation. Here, we assess the hydrologic conditions leading to the recent record flooding across the Lake Ontario - St. Lawrence River system, with a particular emphasis on understanding the extent to which those conditions were consistent with observed and anticipated changes in historical and future climate, and the extent to which those conditions could have been anticipated through improvements in seasonal climate outlooks and hydrological forecasts.

BEACH GRASS INVASIONS AND COASTAL FLOOD PROTECTION: FORECASTING THE EFFECTS OF CLIMATE CHANGE ON COASTAL VULNERABILITY

EPA Science Inventory

Our work will yield an increased general understanding of interactions among the alteration of coastal ecosystem, species invasions, climate change, and human risk in coastal environments. In addition, we will conduct a quantitative vulnerability assessment of a specific coast...

Spatial forecasting of switchgrass productivity under current and future climate change scenarios

USDA-ARS?s Scientific Manuscript database

Evaluating the potential of alternative energy crops across large geographic regions and over time is necessary to determine if feedstock production is feasible and sustainable in the face of growing production demands and climatic change. Panicum virgatum L., a perennial herbaceous grass, is a prom...

FORECASTING REGIONAL TO GLOBAL PLANT MIGRATION IN RESPONSE TO CLIMATE CHANGE

EPA Science Inventory

The rate of future climate change is likely to exceed the migration rates of most plant species. The replacement of dominant species by locally rare species may require decades, and extinctions may occur when plant species cannot migrate fast enough to escape the consequences of...

Revision and application of the LINKAGES model to simulate forest growth in central hardwood landscapes in response to climate change

Treesearch

William D. Dijak; Brice B. Hanberry; Jacob S. Fraser; Hong S. He; Wen J. Wang; Frank R. Thompson

2017-01-01

Context. Global climate change impacts forest growth and methods of modeling those impacts at the landscape scale are needed to forecast future forest species composition change and abundance. Changes in forest landscapes will affect ecosystem processes and services such as succession and disturbance, wildlife habitat, and production of forest...

Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

NASA Technical Reports Server (NTRS)

Cohen, Charlie; Robertson, Franklin; Molod, Andrea

2014-01-01

The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of SST gradients, associated PBL baroclinicity enabled by turbulent mixing, the ensuing PBL moisture convergence, and how changes in these processes relate to convective precipitation bias growth over this short period.

Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

NASA Astrophysics Data System (ADS)

Robertson, F. R.; Cohen, C.

2014-12-01

The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of SST gradients, associated PBL baroclinicity enabled by turbulent mixing, the ensuing PBL moisture convergence, and how changes in these processes relate to convective precipitation bias growth over this short period.

Section on Observed Impacts on El Nino

NASA Technical Reports Server (NTRS)

Rosenzweig, Cynthia

2000-01-01

Agricultural applications of El Nino forecasts are already underway in some countries and need to be evaluated or re-evaluated. For example, in Peru, El Nino forecasts have been incorporated into national planning for the agricultural sector, and areas planted with rice and cotton (cotton being the more drought-tolerant crop) are adjusted accordingly. How well are this and other such programs working? Such evaluations will contribute to the governmental and intergovernmental institutions, including the Inter-American Institute for Global Change Research and the US National Ocean and Atmospheric Agency that are fostering programs to aid the effective use of forecasts. As El Nino climate forecasting grows out of the research mode into operational mode, the research focus shifts to include the design of appropriate modes of utilization. Awareness of and sensitivity to the costs of prediction errors also grow. For example, one major forecasting model failed to predict the very large El Nino event of 1997, when Pacific sea-surface temperatures were the highest on record. Although simple correlations between El Nino events and crop yields may be suggestive, more sophisticated work is needed to understand the subtleties of the interplay among the global climate system, regional climate patterns, and local agricultural systems. Honesty about the limitations of an forecast is essential, especially when human livelihoods are at stake. An end-to-end analysis links tools and expertise from the full sequence of ENSO cause-and-effect processes. Representatives from many disciplines are needed to achieve insights, e.g, oceanographers and atmospheric scientists who predict El Nino events, climatologists who drive global climate models with sea-surface temperature predictions, agronomists who translate regional climate connections in to crop yield forecasts, and economists who analyze market adjustments to the vagaries of climate and determine the value of climate forecasts. Methods include historical studies to understand past patterns and to test hindcasts of the prediction tools, crop modeling, spatial analysis and remote sensing. This research involves expanding, deepening, and applying the understanding of physical climate to the fields of agronomy and social science; and the reciprocal understanding of crop growth and farm economics to climatology. Delivery of a regional climate forecast with no information about how the climate forecast was derived limits its effectiveness. Explanation of a region's major climate driving forces helps to place a seasonal forecast in context. Then, a useful approach is to show historical responses to previous El Nino events, and projections, with uncertainty intervals, of crop response from dynamic process crop growth models. Regional ID forecasts should be updated with real-time weather conditions. Since every El Nino event is different, it is important to track, report and advise on each new event as it unfolds. The stability of human enterprises depends on understanding both the potentialities and the limits of predictability. Farmers rely on past experience to anticipate and respond to fluctuations in the biophysical systems on which their livelihoods depend. Now scientists are improving their ability to predict some major elements of climate variability. The improvements in the reliability of El Nino forecasts are encouraging, but seasonal forecasts for agriculture are not, and will probably never be completely infallible, due to the chaotic nature of the climate system. Uncertainties proliferate as we extend beyond Pacific sea-surface temperatures to climate teleconnections and agricultural outcomes. The goal of this research is to shed as a clear light as possible on these inherent uncertainties and thus to contribute to the development of appropriate responses to El Nino and other seasonal forecasts for a range of stakeholders, which, ultimately, includes food consumers everywhere.

Estimates of the long-term U.S. economic impacts of global climate change-induced drought.

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

Ehlen, Mark Andrew; Loose, Verne W.; Warren, Drake E.

2010-01-01

While climate-change models have done a reasonable job of forecasting changes in global climate conditions over the past decades, recent data indicate that actual climate change may be much more severe. To better understand some of the potential economic impacts of these severe climate changes, Sandia economists estimated the impacts to the U.S. economy of climate change-induced impacts to U.S. precipitation over the 2010 to 2050 time period. The economists developed an impact methodology that converts changes in precipitation and water availability to changes in economic activity, and conducted simulations of economic impacts using a large-scale macroeconomic model of themore » U.S. economy.« less

Should we use seasonnal meteorological ensemble forecasts for hydrological forecasting? A case study for nordic watersheds in Canada.

NASA Astrophysics Data System (ADS)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert; Guay, Catherine

2017-04-01

Hydro-electricity is a major source of energy for many countries throughout the world, including Canada. Long lead-time streamflow forecasts are all the more valuable as they help decision making and dam management. Different techniques exist for long-term hydrological forecasting. Perhaps the most well-known is 'Extended Streamflow Prediction' (ESP), which considers past meteorological scenarios as possible, often equiprobable, future scenarios. In the ESP framework, those past-observed meteorological scenarios (climatology) are used in turn as the inputs of a chosen hydrological model to produce ensemble forecasts (one member corresponding to each year in the available database). Many hydropower companies, including Hydro-Québec (province of Quebec, Canada) use variants of the above described ESP system operationally for long-term operation planning. The ESP system accounts for the hydrological initial conditions and for the natural variability of the meteorological variables. However, it cannot consider the current initial state of the atmosphere. Climate models can help remedy this drawback. In the context of a changing climate, dynamical forecasts issued from climate models seem to be an interesting avenue to improve upon the ESP method and could help hydropower companies to adapt their management practices to an evolving climate. Long-range forecasts from climate models can also be helpful for water management at locations where records of past meteorological conditions are short or nonexistent. In this study, we compare 7-month hydrological forecasts obtained from climate model outputs to an ESP system. The ESP system mimics the one used operationally at Hydro-Québec. The dynamical climate forecasts are produced by the European Center for Medium range Weather Forecasts (ECMWF) System4. Forecasts quality is assessed using numerical scores such as the Continuous Ranked Probability Score (CRPS) and the Ignorance score and also graphical tools such as the reliability diagram. This study covers 10 nordic watersheds. We show that forecast performance according to the CRPS varies with lead-time but also with the period of the year. The raw forecasts from the ECMWF System4 display important biases for both temperature and precipitation, which need to be corrected. The linear scaling method is used for this purpose and is found effective. Bias correction improves forecasts performance, especially during the summer when the precipitations are over-estimated. According to the CRPS, bias corrected forecasts from System4 show performances comparable to those of the ESP system. However, the Ignorance score, which penalizes the lack of calibration (under-dispersive forecasts in this case) more severely than the CRPS, provides a different outlook for the comparison of the two systems. In fact, according to the Ignorance score, the ESP system outperforms forecasts based on System4 in most cases. This illustrates that the joint use of several metrics is crucial to assess the quality of a forecasts system thoroughly. Globally, ESP provide reliable forecasts which can be over-dispersed whereas bias corrected ECMWF System4 forecasts are sharper but at the risk of missing events.

Accounting for groundwater in stream fish thermal habitat responses to climate change

USGS Publications Warehouse

Snyder, Craig D.; Hitt, Nathaniel P.; Young, John A.

2015-01-01

Forecasting climate change effects on aquatic fauna and their habitat requires an understanding of how water temperature responds to changing air temperature (i.e., thermal sensitivity). Previous efforts to forecast climate effects on brook trout habitat have generally assumed uniform air-water temperature relationships over large areas that cannot account for groundwater inputs and other processes that operate at finer spatial scales. We developed regression models that accounted for groundwater influences on thermal sensitivity from measured air-water temperature relationships within forested watersheds in eastern North America (Shenandoah National Park, USA, 78 sites in 9 watersheds). We used these reach-scale models to forecast climate change effects on stream temperature and brook trout thermal habitat, and compared our results to previous forecasts based upon large-scale models. Observed stream temperatures were generally less sensitive to air temperature than previously assumed, and we attribute this to the moderating effect of shallow groundwater inputs. Predicted groundwater temperatures from air-water regression models corresponded well to observed groundwater temperatures elsewhere in the study area. Predictions of brook trout future habitat loss derived from our fine-grained models were far less pessimistic than those from prior models developed at coarser spatial resolutions. However, our models also revealed spatial variation in thermal sensitivity within and among catchments resulting in a patchy distribution of thermally suitable habitat. Habitat fragmentation due to thermal barriers therefore may have an increasingly important role for trout population viability in headwater streams. Our results demonstrate that simple adjustments to air-water temperature regression models can provide a powerful and cost-effective approach for predicting future stream temperatures while accounting for effects of groundwater.

Water quality in the Schuylkill River, Pennsylvania: the potential for long-lead forecasts

NASA Astrophysics Data System (ADS)

Block, P. J.; Peralez, J.

2012-12-01

Prior analysis of pathogen levels in the Schuylkill River has led to a categorical daily forecast of water quality (denoted as red, yellow, or green flag days.) The forecast, available to the public online through the Philadelphia Water Department, is predominantly based on the local precipitation forecast. In this study, we explore the feasibility of extending the forecast to the seasonal scale by associating large-scale climate drivers with local precipitation and water quality parameter levels. This advance information is relevant for recreational activities, ecosystem health, and water treatment (energy, chemicals), as the Schuylkill provides 40% of Philadelphia's water supply. Preliminary results indicate skillful prediction of average summertime water quality parameters and characteristics, including chloride, coliform, turbidity, alkalinity, and others, using season-ahead oceanic and atmospheric variables, predominantly from the North Atlantic. Water quality parameter trends, including historic land use changes along the river, association with climatic variables, and prediction models will be presented.

Weather-centric rangeland revegetation planning

USGS Publications Warehouse

Hardegree, Stuart P.; Abatzoglou, John T.; Brunson, Mark W.; Germino, Matthew; Hegewisch, Katherine C.; Moffet, Corey A.; Pilliod, David S.; Roundy, Bruce A.; Boehm, Alex R.; Meredith, Gwendwr R.

2018-01-01

Invasive annual weeds negatively impact ecosystem services and pose a major conservation threat on semiarid rangelands throughout the western United States. Rehabilitation of these rangelands is challenging due to interannual climate and subseasonal weather variability that impacts seed germination, seedling survival and establishment, annual weed dynamics, wildfire frequency, and soil stability. Rehabilitation and restoration outcomes could be improved by adopting a weather-centric approach that uses the full spectrum of available site-specific weather information from historical observations, seasonal climate forecasts, and climate-change projections. Climate data can be used retrospectively to interpret success or failure of past seedings by describing seasonal and longer-term patterns of environmental variability subsequent to planting. A more detailed evaluation of weather impacts on site conditions may yield more flexible adaptive-management strategies for rangeland restoration and rehabilitation, as well as provide estimates of transition probabilities between desirable and undesirable vegetation states. Skillful seasonal climate forecasts could greatly improve the cost efficiency of management treatments by limiting revegetation activities to time periods where forecasts suggest higher probabilities of successful seedling establishment. Climate-change projections are key to the application of current environmental models for development of mitigation and adaptation strategies and for management practices that require a multidecadal planning horizon. Adoption of new weather technology will require collaboration between land managers and revegetation specialists and modifications to the way we currently plan and conduct rangeland rehabilitation and restoration in the Intermountain West.

Winter precipitation forecast in the European and Mediterranean regions using cluster analysis

NASA Astrophysics Data System (ADS)

Molnos, S.

2017-12-01

The European and Mediterranean climates are sensitive to large-scale circulation of the atmosphere andocean making it difficult to forecast precipitation or temperature on seasonal time-scales. In addition, theMediterranean region has been identified as a hotspot for climate change and already today a drying in theMediterranean region is observed.Thus, it is critically important to predict seasonal droughts as early as possible such that water managersand stakeholders can mitigate impacts.We developed a novel cluster-based forecast method to empirically predict winter's precipitationanomalies in European and Mediterranean regions using precursors in autumn. This approach does notonly utilizes the amplitude but also the pattern of the precursors in generating the forecast.Using a toy model we show that it achieves a better forecast skill than more traditional regression models. Furthermore, we compare our algorithm with dynamic forecast models demonstrating that our prediction method performs better in terms of time and pattern correlation in the Mediterranean and European regions.

Integrating observation and statistical forecasts over sub-Saharan Africa to support Famine Early Warning

USGS Publications Warehouse

Funk, Chris; Verdin, James P.; Husak, Gregory

2007-01-01

Famine early warning in Africa presents unique challenges and rewards. Hydrologic extremes must be tracked and anticipated over complex and changing climate regimes. The successful anticipation and interpretation of hydrologic shocks can initiate effective government response, saving lives and softening the impacts of droughts and floods. While both monitoring and forecast technologies continue to advance, discontinuities between monitoring and forecast systems inhibit effective decision making. Monitoring systems typically rely on high resolution satellite remote-sensed normalized difference vegetation index (NDVI) and rainfall imagery. Forecast systems provide information on a variety of scales and formats. Non-meteorologists are often unable or unwilling to connect the dots between these disparate sources of information. To mitigate these problem researchers at UCSB's Climate Hazard Group, NASA GIMMS and USGS/EROS are implementing a NASA-funded integrated decision support system that combines the monitoring of precipitation and NDVI with statistical one-to-three month forecasts. We present the monitoring/forecast system, assess its accuracy, and demonstrate its application in food insecure sub-Saharan Africa.

Long-term plant responses to climate are moderated by biophysical attributes in a North American desert

USGS Publications Warehouse

Munson, Seth M.; Webb, Robert H.; Housman, David C.; Veblen, Kari E.; Nussear, Kenneth E.; Beever, Erik A.; Hartney, Kristine B.; Miriti, Maria N.; Phillips, Susan L.; Fulton, Robert E.; Tallent, Nita G.

2015-01-01

Synthesis. Our results emphasize the importance of understanding climate-vegetation relationships in the context of biophysical attributes that influence water availability and provide an important forecast of climate-change effects, including plant mortality and land degradation in dryland regions throughout the world.

Scientific motivation for ADM/Aeolus mission

NASA Astrophysics Data System (ADS)

Källén, Erland

2018-04-01

The ADM/Aeolus wind lidar mission will provide a global coverage of atmospheric wind profiles. Atmospheric wind observations are required for initiating weather forecast models and for predicting and monitoring long term climate change. Improved knowledge of the global wind field is widely recognised as fundamental to advancing the understanding and prediction of weather and climate. In particular over tropical areas there is a need for better wind data leading to improved medium range (3-10 days) weather forecasts over the whole globe.

Forecasting the Future Risk of Barmah Forest Virus Disease under Climate Change Scenarios in Queensland, Australia

PubMed Central

Naish, Suchithra; Mengersen, Kerrie; Hu, Wenbiao; Tong, Shilu

2013-01-01

Background Mosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV) disease under climate change scenarios in Queensland, Australia. Methods/Principal Findings We obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000–2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections. Conclusions/Significance We conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland. PMID:23690959

Forecasting civil conflict along the shared socioeconomic pathways

NASA Astrophysics Data System (ADS)

Hegre, Håvard; Buhaug, Halvard; Calvin, Katherine V.; Nordkvelle, Jonas; Waldhoff, Stephanie T.; Gilmore, Elisabeth

2016-05-01

Climate change and armed civil conflict are both linked to socioeconomic development, although conditions that facilitate peace may not necessarily facilitate mitigation and adaptation to climate change. While economic growth lowers the risk of conflict, it is generally associated with increased greenhouse gas emissions and costs of climate mitigation policies. This study investigates the links between growth, climate change, and conflict by simulating future civil conflict using new scenario data for five alternative socioeconomic pathways with different mitigation and adaptation assumptions, known as the shared socioeconomic pathways (SSPs). We develop a statistical model of the historical effect of key socioeconomic variables on country-specific conflict incidence, 1960-2013. We then forecast the annual incidence of conflict, 2014-2100, along the five SSPs. We find that SSPs with high investments in broad societal development are associated with the largest reduction in conflict risk. This is most pronounced for the least developed countries—poverty alleviation and human capital investments in poor countries are much more effective instruments to attain global peace and stability than further improvements to wealthier economies. Moreover, the SSP that describes a sustainability pathway, which poses the lowest climate change challenges, is as conducive to global peace as the conventional development pathway.

A method for estimating vulnerability of Douglas-fir growth to climate change in the Northwestern U.S.

Treesearch

J.S. Littell; D.L. Peterson

2005-01-01

Borrowing from landscape ecology, atmospheric science, and integrated assessment, we aim to understand the complex interactions that determine productivity in montane forests and utilize such relationships to forecast montane forest vulnerability under global climate change. Specifically, we identify relationships for precipitation and temperature that govern the...

An aerobic scope-based habitat suitability index for predicting the effects of multi-dimensional climate change stressors on marine teleosts

NASA Astrophysics Data System (ADS)

Del Raye, Gen; Weng, Kevin C.

2015-03-01

Climate change will expose many marine ecosystems to temperature, oxygen and CO2 conditions that have not been experienced for millennia. Predicting the impact of these changes on marine fishes is difficult due to the complexity of these disparate stressors and the inherent non-linearity of physiological systems. Aerobic scope (the difference between maximum and minimum aerobic metabolic rates) is a coherent, unifying physiological framework that can be used to examine all of the major environmental changes expected to occur in the oceans during this century. Using this framework, we develop a physiology-based habitat suitability model to forecast the response of marine fishes to simultaneous ocean acidification, warming and deoxygenation, including interactions between all three stressors. We present an example of the model parameterized for Thunnus albacares (yellowfin tuna), an important fisheries species that is likely to be affected by climate change. We anticipate that if embedded into multispecies ecosystem models, our model could help to more precisely forecast climate change impacts on the distribution and abundance of other high value species. Finally, we show how our model may indicate the potential for, and limits of, adaptation to chronic stressors.

Visualisation and communication of probabilistic climate forecasts to renewable-energy policy makers

NASA Astrophysics Data System (ADS)

Steffen, Sophie; Lowe, Rachel; Davis, Melanie; Doblas-Reyes, Francisco J.; Rodó, Xavier

2014-05-01

Despite the strong dependence on weather and climate variability of the renewable-energy industry, and the existence of several initiatives towards demonstrating the added benefits of integrating probabilistic forecasts into energy decision-making processes, weather and climate forecasts are still under-utilised within the sector. Improved communication is fundamental to stimulate the use of climate forecast information within decision-making processes, in order to adapt to a highly climate dependent renewable-energy industry. This work focuses on improving the visualisation of climate forecast information, paying special attention to seasonal time scales. This activity is central to enhance climate services for renewable energy and to optimise the usefulness and usability of inherently complex climate information. In the realm of the Global Framework for Climate Services (GFCS) initiative, and subsequent European projects: Seasonal-to-Decadal Climate Prediction for the Improvement of European Climate Service (SPECS) and the European Provision of Regional Impacts Assessment in Seasonal and Decadal Timescales (EUPORIAS), this paper investigates the visualisation and communication of seasonal forecasts with regards to their usefulness and usability, to enable the development of a European climate service. The target end user is the group of renewable-energy policy makers, who are central to enhance climate services for the energy industry. The overall objective is to promote the wide-range dissemination and exchange of actionable climate information based on seasonal forecasts from Global Producing Centres (GPCs). It examines the existing main barriers and deficits. Examples of probabilistic climate forecasts from different GPC's are used to make a catalogue of current approaches, to assess their advantages and limitations and, finally, to recommend better alternatives. Interviews have been conducted with renewable-energy stakeholders to receive feedback for the improvement of existing visualisation techniques of forecasts. The overall aim is to establish a communication protocol for the visualisation of probabilistic climate forecasts, which does not currently exist. GPCs show their own probabilistic forecasts with limited consistency in their communication across different centres, which complicates the understanding for the end user. The recommended communication protocol for both the visualisation and description of climate forecasts can help to introduce a standard format and message to end users from several climate-sensitive sectors, such as energy, tourism, agriculture and health.

Forecasting climate change impacts on the distribution of wetland habitat in the Midwestern United states.

PubMed

Garris, Heath W; Mitchell, Randall J; Fraser, Lauchlan H; Barrett, Linda R

2015-02-01

Shifting precipitation patterns brought on by climate change threaten to alter the future distribution of wetlands. We developed a set of models to understand the role climate plays in determining wetland formation on a landscape scale and to forecast changes in wetland distribution for the Midwestern United States. These models combined 35 climate variables with 21 geographic and anthropogenic factors thought to encapsulate other major drivers of wetland distribution for the Midwest. All models successfully recreated a majority of the variation in current wetland area within the Midwest, and showed that wetland area was significantly associated with climate, even when controlling for landscape context. Inferential (linear) models identified a consistent negative association between wetland area and isothermality. This is likely the result of regular inundation in areas where precipitation accumulates as snow, then melts faster than drainage capacity. Moisture index seasonality was identified as a key factor distinguishing between emergent and forested wetland types, where forested wetland area at the landscape scale is associated with a greater seasonal variation in water table depth. Forecasting models (neural networks) predicted an increase in potential wetland area in the coming century, with areas conducive to forested wetland formation expanding more rapidly than areas conducive to emergent wetlands. Local cluster analyses identified Iowa and Northeastern Missouri as areas of anticipated wetland expansion, indicating both a risk to crop production within the Midwest Corn Belt and an opportunity for wetland conservation, while Northern Minnesota and Michigan are potentially at risk of wetland losses under a future climate. © 2014 John Wiley & Sons Ltd.

Confronting the demand and supply of snow seasonal forecasts for ski resorts : the case of French Alps

NASA Astrophysics Data System (ADS)

Dubois, Ghislain

2017-04-01

Alpine ski resorts are highly dependent on snow, which availability is characterized by a both a high inter-annual variability and a gradual diminution due to climate change. Due to this dependency to climatic resources, the ski industry is increasingly affected by climate change: higher temperatures limit snow falls, increase melting and limit the possibilities of technical snow making. Therefore, since the seventies, managers drastically improved their practices, both to adapt to climate change and to this inter-annual variability of snow conditions. Through slope preparation and maintenance, snow stock management, artificial snow making, a typical resort can approximately keep the same season duration with 30% less snow. The ski industry became an activity of high technicity The EUPORIAS FP7 (www.euporias.eu) project developed between 2012 and 2016 a deep understanding of the supply and demand conditions for the provision of climate services disseminating seasonal forecasts. In particular, we developed a case study, which allowed conducting several activities for a better understanding of the demand and of the business model of future services applied to the ski industry. The investigations conducted in France inventoried the existing tools and databases, assessed the decision making process and data needs of ski operators, and provided evidences that some discernable skill of seasonal forecasts exist. This case study formed the basis of the recently funded PROSNOW H2020 project. We will present the main results of EUPORIAS project for the ski industry.

Seasonal and decadal information towards climate services: EUPORIAS

NASA Astrophysics Data System (ADS)

Buontempo, Carlo; Hewitt, Chris

2013-04-01

Societies have always faced challenges and opportunities arising from variations in climate, and have often flourished or collapsed depending on their ability to adapt to such changes. Recent advances in our understanding and ability to forecast climate variability and climate change have meant that skilful predictions are beginning to be routinely made on seasonal to decadal (s2d) timescales. Such forecasts have the potential to be of great value to a wide range of decision-making, where outcomes are strongly influenced by variations in the climate. The European Commission have recently commissioned a major four year long project (EUPORIAS) to develop prototype end-to-end climate impact prediction services operating on a seasonal to decadal timescale, and assess their value in informing decision-making. EUPORIAS commenced on 1 November 2012, coordinated by the UK Met Office leading a consortium of 24 organisations representing world-class European climate research and climate service centres, expertise in impacts assessments and seasonal predictions, two United Nations agencies, specialists in new media, and commercial companies in climate-vulnerable sectors such as energy, water and tourism. The paper describes the setup of the project, its main outcome and some of the very preliminary results.

Precipitation forecast verification over Brazilian watersheds on present and future climate

NASA Astrophysics Data System (ADS)

Xavier, L.; Bruyere, C. L.; Rotunno, O.

2016-12-01

Evaluating the quality of precipitation forecast is an essential step for hydrological studies, among other applications, which is particularly relevant when taking into account climate change and the consequent likely modification of precipitation patterns. In this study we analyzed daily precipitation forecasts given by the global model CESM and the regional model WRF on present and future climate. For present runs, CESM data have been considered from 1980 to 2005, and WRF data from 1990 to 2000. CESM future runs were available for 3 RCP scenarios (4.5, 6.0 and 8.5), over 2005-2100 period; for WRF, future runs spanned 4 different 11-year periods (2020-2030, 2030-2040, 2050-2060 and 2080-2090). WRF simulations had been driven by bias-corrected forcings, and had been done on present climate for a 24 members ensemble created by varying the adopted parameterization schemes. On WRF future climate simulations, data from 3 members out of the original ensemble were available. Precipitation data have been spatially averaged over some large Brazilian watersheds (Amazon and subbasins, Tocantins, Sao Francisco, 4 of Parana`s subbasins) and have been evaluated for present climate against a gauge gridded dataset and ERA Interim data both spanning the 1980-2013 period. The evaluation was focused on the analysis of precipitation forecasts probabilities distribution. Taking into account daily and monthly mean precipitation aggregated on 3-month periods (DJF,MAM,JJA,SON), we adopted some skill measures, amongst them, the Perkins Skill Score (PSS). From the results we verified that on present climate WRF ensemble mean led to clearly better results when compared with CESM data for Amazon, Tocantins and Sao Francisco, but model was not as skillful to the other basins, which could be also been observed for future climate. PSS results from future runs showed that few changes would be observed over the different periods for the considered basins.

The End-to-end Demonstrator for improved decision making in the water sector in Europe (EDgE)

NASA Astrophysics Data System (ADS)

Wood, Eric; Wanders, Niko; Pan, Ming; Sheffield, Justin; Samaniego, Luis; Thober, Stephan; Kumar, Rohinni; Prudhomme, Christel; Houghton-Carr, Helen

2017-04-01

High-resolution simulations of water resources from hydrological models are vital to supporting important climate services. Apart from a high level of detail, both spatially and temporally, it is important to provide simulations that consistently cover a range of timescales, from historical reanalysis to seasonal forecast and future projections. In the new EDgE project commissioned by the ECMWF (C3S) we try to fulfill these requirements. EDgE is a proof-of-concept project which combines climate data and state-of-the-art hydrological modelling to demonstrate a water-oriented information system implemented through a web application. EDgE is working with key European stakeholders representative of private and public sectors to jointly develop and tailor approaches and techniques. With these tools, stakeholders are assisted in using improved climate information in decision-making, and supported in the development of climate change adaptation and mitigation policies. Here, we present the first results of the EDgE modelling chain, which is divided into three main processes: 1) pre-processing and downscaling; 2) hydrological modelling; 3) post-processing. Consistent downscaling and bias corrections for historical simulations, seasonal forecasts and climate projections ensure that the results across scales are robust. The daily temporal resolution and 5km spatial resolution ensure locally relevant simulations. With the use of four hydrological models (PCR-GLOBWB, VIC, mHM, Noah-MP), uncertainty between models is properly addressed, while consistency is guaranteed by using identical input data for static land surface parameterizations. The forecast results are communicated to stakeholders via Sectoral Climate Impact Indicators (SCIIs) that have been created in collaboration with the end-user community of the EDgE project. The final product of this project is composed of 15 years of seasonal forecast and 10 climate change projections, all combined with four hydrological models. These unique high-resolution climate information simulations in the EDgE project provide an unprecedented information system for decision-making over Europe.

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

Treesearch

S. Sun; Ge Sun; Erika Cohen Mack; Steve McNulty; Peter Caldwell; K. Duan; Y. Zhang

2015-01-01

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)...

Extinction debt from climate change for frogs in the wet tropics.

PubMed

Fordham, Damien A; Brook, Barry W; Hoskin, Conrad J; Pressey, Robert L; VanDerWal, Jeremy; Williams, Stephen E

2016-10-01

The effect of twenty-first-century climate change on biodiversity is commonly forecast based on modelled shifts in species ranges, linked to habitat suitability. These projections have been coupled with species-area relationships (SAR) to infer extinction rates indirectly as a result of the loss of climatically suitable areas and associated habitat. This approach does not model population dynamics explicitly, and so accepts that extinctions might occur after substantial (but unknown) delays-an extinction debt. Here we explicitly couple bioclimatic envelope models of climate and habitat suitability with generic life-history models for 24 species of frogs found in the Australian Wet Tropics (AWT). We show that (i) as many as four species of frogs face imminent extinction by 2080, due primarily to climate change; (ii) three frogs face delayed extinctions; and (iii) this extinction debt will take at least a century to be realized in full. Furthermore, we find congruence between forecast rates of extinction using SARs, and demographic models with an extinction lag of 120 years. We conclude that SAR approaches can provide useful advice to conservation on climate change impacts, provided there is a good understanding of the time lags over which delayed extinctions are likely to occur. © 2016 The Author(s).

Seasonal Climate Forecasts and Adoption by Agriculture

NASA Astrophysics Data System (ADS)

Garbrecht, Jurgen; Meinke, Holger; Sivakumar, Mannava V. K.; Motha, Raymond P.; Salinger, Michael J.

2005-06-01

Recent advances in atmospheric and ocean sciences and a better understanding of the global climate have led to skillful climate forecasts at seasonal to interannual timescales, even in midlatitudes. These scientific advances and forecasting capabilities have opened the door to practical applications that benefit society. The benefits include the reduction of weather/climate related risks and vulnerability, increased economic opportunities, enhanced food security, mitigation of adverse climate impacts, protection of environmental quality, and so forth. Agriculture in particular can benefit substantially from accurate long-lead seasonal climate forecasts. Indeed, agricultural production very much depends on weather, climate, and water availability, and unexpected departures from anticipated climate conditions can thwart the best laid management plans. Timely climate forecasts offer means to reduce losses in drought years, increase profitability in good years, deal more effectively with climate variability, and choose from targeted risk-management strategies. In addition to benefiting farmers, forecasts can also help marketing systems and downstream users prepare for anticipated production outcomes and associated consequences.

Predicting climate effects on Pacific sardine

PubMed Central

Deyle, Ethan R.; Fogarty, Michael; Hsieh, Chih-hao; Kaufman, Les; MacCall, Alec D.; Munch, Stephan B.; Perretti, Charles T.; Ye, Hao; Sugihara, George

2013-01-01

For many marine species and habitats, climate change and overfishing present a double threat. To manage marine resources effectively, it is necessary to adapt management to changes in the physical environment. Simple relationships between environmental conditions and fish abundance have long been used in both fisheries and fishery management. In many cases, however, physical, biological, and human variables feed back on each other. For these systems, associations between variables can change as the system evolves in time. This can obscure relationships between population dynamics and environmental variability, undermining our ability to forecast changes in populations tied to physical processes. Here we present a methodology for identifying physical forcing variables based on nonlinear forecasting and show how the method provides a predictive understanding of the influence of physical forcing on Pacific sardine. PMID:23536299

Assessing the vulnerability of economic sectors to climate variability to improve the usability of seasonal to decadal climate forecasts in Europe - a preliminary concept

NASA Astrophysics Data System (ADS)

Funk, Daniel

2015-04-01

Climate variability poses major challenges for decision-makers in climate-sensitive sectors. Seasonal to decadal (S2D) forecasts provide potential value for management decisions especially in the context of climate change where information from present or past climatology loses significance. However, usable and decision-relevant tailored climate forecasts are still sparse for Europe and successful examples of application require elaborate and individual producer-user interaction. The assessment of sector-specific vulnerabilities to critical climate conditions at specific temporal scale will be a great step forward to increase the usability and efficiency of climate forecasts. A concept for a sector-specific vulnerability assessment (VA) to climate variability is presented. The focus of this VA is on the provision of usable vulnerability information which can be directly incorporated in decision-making processes. This is done by developing sector-specific climate-impact-decision-pathways and the identification of their specific time frames using data from both bottom-up and top-down approaches. The structure of common VA's for climate change related issues is adopted which envisages the determination of exposure, sensitivity and coping capacity. However, the application of the common vulnerability components within the context of climate service application poses some fundamental considerations: Exposure - the effect of climate events on the system of concern may be modified and delayed due to interconnected systems (e.g. catchment). The critical time-frame of a climate event or event sequence is dependent on system-internal thresholds and initial conditions. But also on decision-making processes which require specific lead times of climate information to initiate respective coping measures. Sensitivity - in organizational systems climate may pose only one of many factors relevant for decision making. The scope of "sensitivity" in this concept comprises both the potential physical response of the system of concern as well as the criticality of climate-related decision-making processes. Coping capacity - in an operational context coping capacity can only reduce vulnerability if it can be applied purposeful. With respect to climate vulnerabilities this refers to the availability of suitable, usable and skillful climate information. The focus for this concept is on existing S2D climate service products and their match with user needs. The outputs of the VA are climate-impact-decision-pathways which characterize critical climate conditions, estimate the role of climate in decision-making processes and evaluate the availability and potential usability of S2D climate forecast products. A classification scheme is developed for each component of the impact-pathway to assess its specific significance. The systemic character of these schemes enables a broad application of this VA across sectors where quantitative data is limited. This concept is developed and will be tested within the context of the EU-FP7 project "European Provision Of Regional Impacts Assessments on Seasonal and Decadal Timescales" EUPORIAS.

Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

NASA Astrophysics Data System (ADS)

Yang, Tiantian; Asanjan, Ata Akbari; Welles, Edwin; Gao, Xiaogang; Sorooshian, Soroosh; Liu, Xiaomang

2017-04-01

Reservoirs are fundamental human-built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast-informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month-ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Results show (1) three methods are capable of providing monthly reservoir inflows with satisfactory statistics; (2) the results obtained by Random Forest have the best statistical performances compared with the other two methods; (3) another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; (4) climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and (5) different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.

River catchment rainfall series analysis using additive Holt-Winters method

NASA Astrophysics Data System (ADS)

Puah, Yan Jun; Huang, Yuk Feng; Chua, Kuan Chin; Lee, Teang Shui

2016-03-01

Climate change is receiving more attention from researchers as the frequency of occurrence of severe natural disasters is getting higher. Tropical countries like Malaysia have no distinct four seasons; rainfall has become the popular parameter to assess climate change. Conventional ways that determine rainfall trends can only provide a general result in single direction for the whole study period. In this study, rainfall series were modelled using additive Holt-Winters method to examine the rainfall pattern in Langat River Basin, Malaysia. Nine homogeneous series of more than 25 years data and less than 10% missing data were selected. Goodness of fit of the forecasted models was measured. It was found that seasonal rainfall model forecasts are generally better than the monthly rainfall model forecasts. Three stations in the western region exhibited increasing trend. Rainfall in southern region showed fluctuation. Increasing trends were discovered at stations in the south-eastern region except the seasonal analysis at station 45253. Decreasing trend was found at station 2818110 in the east, while increasing trend was shown at station 44320 that represents the north-eastern region. The accuracies of both rainfall model forecasts were tested using the recorded data of years 2010-2012. Most of the forecasts are acceptable.

Improving Decision-Making Activities for Meningitis and Malaria

NASA Technical Reports Server (NTRS)

Ceccato, Pietro; Trzaska, Sylwia; Garcia-Pando, Carlos Perez; Kalashnikova, Olga; del Corral, John; Cousin, Remi; Blumenthal, M. Benno; Bell, Michael; Connor, Stephen J.; Thomson, Madeleine C.

2013-01-01

Public health professionals are increasingly concerned about the potential impact that climate variability and change can have on infectious disease. The International Research Institute for Climate and Society (IRI) is developing new products to increase the public health community's capacity to understand, use and demand the appropriate climate data and climate information to mitigate the public health impacts of climate on infectious disease, in particular meningitis and malaria. In this paper, we present the new and improved products that have been developed for: (i) estimating dust aerosol for forecasting risks of meningitis and (ii) for monitoring temperature and rainfall and integrating them into a vectorial capacity model for forecasting risks of malaria epidemics. We also present how the products have been integrated into a knowledge system (IRI Data Library Map Room, SERVIR) to support the use of climate and environmental information in climate-sensitive health decision-making.

The prediction of the impact of climatic factors on short-term electric power load based on the big data of smart city

NASA Astrophysics Data System (ADS)

Qiu, Yunfei; Li, Xizhong; Zheng, Wei; Hu, Qinghe; Wei, Zhanmeng; Yue, Yaqin

2017-08-01

The climate changes have great impact on the residents’ electricity consumption, so the study on the impact of climatic factors on electric power load is of significance. In this paper, the effects of the data of temperature, rainfall and wind of smart city on short-term power load is studied to predict power load. The authors studied the relation between power load and daily temperature, rainfall and wind in the 31 days of January of one year. In the research, the authors used the Matlab neural network toolbox to establish the combinational forecasting model. The authors trained the original input data continuously to get the internal rules inside the data and used the rules to predict the daily power load in the next January. The prediction method relies on the accuracy of weather forecasting. If the weather forecasting is different from the actual weather, we need to correct the climatic factors to ensure accurate prediction.

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Singh, Vijay P.; Xia, Youlong

2018-03-01

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large-scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high-quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

Contributions to Global Augmented Compound Urban Heat Extreme (ACUTE) from Climate Change and the Urban Heat Island Effect

NASA Astrophysics Data System (ADS)

Huang, K.

2017-12-01

Over the next decades, climate change is projected to increase the intensity and frequency of extreme heat events (EHEs). The severity and periodicity of these hazards are likely to be further compounded by stronger urban heat island (UHI) effects as the world continues to urbanize. However, there is little known about how greenhouse gases (GHG) induced changes in EHE will interact with UHI, and what this will mean for the exposure of urban populations to high temperature. This work aims to fill this knowledge gap by combining a mesoscale meteorological model (Weather Research Forecasting, WRF) with a global urban expansion forecast, to generate spatially explicit projections of compound urban temperature extremes through 2050. These global projections include all the urban areas in developing world. The respective contributions from GHG-induced climate change, the UHI effect, and their interaction vary across different types of urban areas. The resulting compound heat extremes will be more intense and frequent in emerging Asian and African mega urban regions, located in tropical/subtropical climates, due to their unprecedented sizes and the significantly reduced evaporation. Previous studies neglecting the interaction between global climate change and regional UHI effect have underestimated exposure to heat extremes in urban areas.

Climate change, extreme weather events, and us health impacts: what can we say?

PubMed

Mills, David M

2009-01-01

Address how climate change impacts on a group of extreme weather events could affect US public health. A literature review summarizes arguments for, and evidence of, a climate change signal in select extreme weather event categories, projections for future events, and potential trends in adaptive capacity and vulnerability in the United States. Western US wildfires already exhibit a climate change signal. The variability within hurricane and extreme precipitation/flood data complicates identifying a similar climate change signal. Health impacts of extreme events are not equally distributed and are very sensitive to a subset of exceptional extreme events. Cumulative uncertainty in forecasting climate change driven characteristics of extreme events and adaptation prevents confidently projecting the future health impacts from hurricanes, wildfires, and extreme precipitation/floods in the United States attributable to climate change.

Cod Collapse and the Climate in the North Atlantic

NASA Astrophysics Data System (ADS)

Meng, K. C.; Oremus, K. L.; Gaines, S.

2014-12-01

Effective fisheries management requires forecasting population changes. We find a negative relationship between the North Atlantic Oscillation (NAO) index and subsequently surveyed biomass and catch of Atlantic cod, Gadus morhua, off the New England coast. A 1-unit NAO increase is associated with a 17% decrease in surveyed biomass of age-1 cod the following year. This relationship persists as the cod mature, such that observed NAO can be used to forecast future adult biomass. We also document that an NAO event lowers catch for up to 15 years afterward. In contrast to forecasts by existing stock assessment models, our NAO-driven statistical model successfully hindcasts the recent collapse of New England cod fisheries following strong NAO events in 2007 and 2008 (see figure). This finding can serve as a template for forecasting other fisheries affected by climatic conditions.

National Centers for Environmental Prediction (NCEP)

Science.gov Websites

Tropical Marine Fire Weather Forecast Maps Unified Surface Analysis Climate Climate Prediction Climate forecasts of hazardous flight conditions at all levels within domestic and international air space. Climate Prediction Center monitors and forecasts short-term climate fluctuations and provides information on the

Amplified plant turnover in response to climate change forecast by Late Quaternary records

NASA Astrophysics Data System (ADS)

Nogués-Bravo, D.; Veloz, S.; Holt, B. G.; Singarayer, J.; Valdes, P.; Davis, B.; Brewer, S. C.; Williams, J. W.; Rahbek, C.

2016-12-01

Conservation decisions are informed by twenty-first-century climate impact projections that typically predict high extinction risk. Conversely, the palaeorecord shows strong sensitivity of species abundances and distributions to past climate changes, but few clear instances of extinctions attributable to rising temperatures. However, few studies have incorporated palaeoecological data into projections of future distributions. Here we project changes in abundance and conservation status under a climate warming scenario for 187 European and North American plant taxa using niche-based models calibrated against taxa-climate relationships for the past 21,000 years. We find that incorporating long-term data into niche-based models increases the magnitude of projected future changes for plant abundances and community turnover. The larger projected changes in abundances and community turnover translate into different, and often more threatened, projected IUCN conservation status for declining tree taxa, compared with traditional approaches. An average of 18.4% (North America) and 15.5% (Europe) of taxa switch IUCN categories when compared with single-time model results. When taxa categorized as `Least Concern' are excluded, the palaeo-calibrated models increase, on average, the conservation threat status of 33.2% and 56.8% of taxa. Notably, however, few models predict total disappearance of taxa, suggesting resilience for these taxa, if climate were the only extinction driver. Long-term studies linking palaeorecords and forecasting techniques have the potential to improve conservation assessments.

Development of predictive weather scenarios for early prediction of rice yield in South Korea

NASA Astrophysics Data System (ADS)

Shin, Y.; Cho, J.; Jung, I.

2017-12-01

International grain prices are becoming unstable due to frequent occurrence of abnormal weather phenomena caused by climate change. Early prediction of grain yield using weather forecast data is important for stabilization of international grain prices. The APEC Climate Center (APCC) is providing seasonal forecast data based on monthly climate prediction models for global seasonal forecasting services. The 3-month and 6-month seasonal forecast data using the multi-model ensemble (MME) technique are provided in their own website, ADSS (APCC Data Service System, http://adss.apcc21.org/). The spatial resolution of seasonal forecast data for each individual model is 2.5°×2.5°(about 250km) and the time scale is created as monthly. In this study, we developed customized weather forecast scenarios that are combined seasonal forecast data and observational data apply to early rice yield prediction model. Statistical downscale method was applied to produce meteorological input data of crop model because field scale crop model (ORYZA2000) requires daily weather data. In order to determine whether the forecasting data is suitable for the crop model, we produced spatio-temporal downscaled weather scenarios and evaluated the predictability by comparison with observed weather data at 57 ASOS stations in South Korea. The customized weather forecast scenarios can be applied to various application fields not only early rice yield prediction. Acknowledgement This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No: PJ012855022017)" Rural Development Administration, Republic of Korea.

Development of risk-based air quality management strategies under impacts of climate change.

PubMed

Liao, Kuo-Jen; Amar, Praveen; Tagaris, Efthimios; Russell, Armistead G

2012-05-01

Climate change is forecast to adversely affect air quality through perturbations in meteorological conditions, photochemical reactions, and precursor emissions. To protect the environment and human health from air pollution, there is an increasing recognition of the necessity of developing effective air quality management strategies under the impacts of climate change. This paper presents a framework for developing risk-based air quality management strategies that can help policy makers improve their decision-making processes in response to current and future climate change about 30-50 years from now. Development of air quality management strategies under the impacts of climate change is fundamentally a risk assessment and risk management process involving four steps: (1) assessment of the impacts of climate change and associated uncertainties; (2) determination of air quality targets; (3) selections of potential air quality management options; and (4) identification of preferred air quality management strategies that minimize control costs, maximize benefits, or limit the adverse effects of climate change on air quality when considering the scarcity of resources. The main challenge relates to the level of uncertainties associated with climate change forecasts and advancements in future control measures, since they will significantly affect the risk assessment results and development of effective air quality management plans. The concept presented in this paper can help decision makers make appropriate responses to climate change, since it provides an integrated approach for climate risk assessment and management when developing air quality management strategies. Development of climate-responsive air quality management strategies is fundamentally a risk assessment and risk management process. The risk assessment process includes quantification of climate change impacts on air quality and associated uncertainties. Risk management for air quality under the impacts of climate change includes determination of air quality targets, selections of potential management options, and identification of effective air quality management strategies through decision-making models. The risk-based decision-making framework can also be applied to develop climate-responsive management strategies for the other environmental dimensions and assess costs and benefits of future environmental management policies.

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

DOE PAGES

Boer, George J.; Smith, Douglas M.; Cassou, Christophe; ...

2016-01-01

The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Predictionmore » (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Furthermore, groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.« less

Comparison of Strategies for Climate Change Adaptation of Water Supply and Flood Control Reservoirs

NASA Astrophysics Data System (ADS)

Ng, T. L.; Yang, P.; Bhushan, R.

2016-12-01

With climate change, streamflows are expected to become more fluctuating, with more frequent and intense floods and droughts. This complicates reservoir operation, which is highly sensitive to inflow variability. We make a comparative evaluation of three strategies for adapting reservoirs to climate-induced shifts in streamflow patterns. Specifically, we examine the effectiveness of (i) expanding the capacities of reservoirs by way of new off-stream reservoirs, (ii) introducing wastewater reclamation to augment supplies, and (iii) improving real-time streamflow forecasts for more optimal decision-making. The first two are hard strategies involving major infrastructure modifications, while the third a soft strategy entailing adjusting the system operation. A comprehensive side-by-side comparison of the three strategies is as yet lacking in the literature despite the many past studies investigating the strategies individually. To this end, we developed an adaptive forward-looking linear program that solves to yield the optimal decisions for the current time as a function of an ensemble forecast of future streamflows. Solving the model repeatedly on a rolling basis with regular updating of the streamflow forecast simulates the system behavior over the entire operating horizon. Results are generated for two hypothetical water supply and flood control reservoirs of differing inflows and demands. Preliminary findings suggest that of the three strategies, improving streamflow forecasts to be most effective in mitigating the effects of climate change. We also found that, in average terms, both additional reservoir capacity and wastewater reclamation have potential to reduce water shortage and downstream flooding. However, in the worst case, the potential of the former to reduce water shortage is limited, and similarly so the potential of the latter to reduce downstream flooding.

Creating Dynamically Downscaled Seasonal Climate Forecast and Climate Change Projection Information for the North American Monsoon Region Suitable for Decision Making Purposes

NASA Astrophysics Data System (ADS)

Castro, C. L.; Dominguez, F.; Chang, H.

2010-12-01

Current seasonal climate forecasts and climate change projections of the North American monsoon are based on the use of course-scale information from a general circulation model. The global models, however, have substantial difficulty in resolving the regional scale forcing mechanisms of precipitation. This is especially true during the period of the North American Monsoon in the warm season. Precipitation is driven primarily due to the diurnal cycle of convection, and this process cannot be resolve in coarse-resolution global models that have a relatively poor representation of terrain. Though statistical downscaling may offer a relatively expedient method to generate information more appropriate for the regional scale, and is already being used in the resource decision making processes in the Southwest U.S., its main drawback is that it cannot account for a non-stationary climate. Here we demonstrate the use of a regional climate model, specifically the Weather Research and Forecast (WRF) model, for dynamical downscaling of the North American Monsoon. To drive the WRF simulations, we use retrospective reforecasts from the Climate Forecast System (CFS) model, the operational model used at the U.S. National Center for Environmental Prediction, and three select “well performing” IPCC AR 4 models for the A2 emission scenario. Though relatively computationally expensive, the use of WRF as a regional climate model in this way adds substantial value in the representation of the North American Monsoon. In both cases, the regional climate model captures a fairly realistic and reasonable monsoon, where none exists in the driving global model, and captures the dominant modes of precipitation anomalies associated with ENSO and the Pacific Decadal Oscillation (PDO). Long-term precipitation variability and trends in these simulations is considered via the standardized precipitation index (SPI), a commonly used metric to characterize long-term drought. Dynamically downscaled climate projection data will be integrated into future water resource projections in the state of Arizona, through a cooperative effort involving numerous water resource stakeholders.

Contrasting responses to a climate regime change by sympatric, ice-dependent predators.

PubMed

Younger, Jane L; van den Hoff, John; Wienecke, Barbara; Hindell, Mark; Miller, Karen J

2016-03-15

Models that predict changes in the abundance and distribution of fauna under future climate change scenarios often assume that ecological niche and habitat availability are the major determinants of species' responses to climate change. However, individual species may have very different capacities to adapt to environmental change, as determined by intrinsic factors such as their dispersal ability, genetic diversity, generation time and rate of evolution. These intrinsic factors are usually excluded from forecasts of species' abundance and distribution changes. We aimed to determine the importance of these factors by comparing the impact of the most recent climate regime change, the late Pleistocene glacial-interglacial transition, on two sympatric, ice-dependent meso-predators, the emperor penguin (Aptenodytes forsteri) and Weddell seal (Leptonychotes weddellii). We reconstructed the population trend of emperor penguins and Weddell seals in East Antarctica over the past 75,000 years using mitochondrial DNA sequences and an extended Bayesian skyline plot method. We also assessed patterns of contemporary population structure and genetic diversity. Despite their overlapping distributions and shared dependence on sea ice, our genetic data revealed very different responses to climate warming between these species. The emperor penguin population grew rapidly following the glacial-interglacial transition, but the size of the Weddell seal population did not change. The expansion of emperor penguin numbers during the warm Holocene may have been facilitated by their higher dispersal ability and gene flow among colonies, and fine-scale differences in preferred foraging locations. The vastly different climate change responses of two sympatric ice-dependent predators suggests that differing adaptive capacities and/or fine-scale niche differences can play a major role in species' climate change responses, and that adaptive capacity should be considered alongside niche and distribution in future species forecasts.

Chapter 3: Climate Change

Treesearch

Steve McNulty; Jennifer Moore Myers; Peter Caldwell; Ge Sun

2011-01-01

Since 1960, all but two southern capital cities (Montgomery, AL and Oklahoma City, OK) have experienced a statistically significant increase in average annual temperature (approximately 0.016 °C), but none has experienced significant trends in precipitation. The South is forecasted to experience warmer temperatures for the duration of the 21st century; forecasts are...

NOMADS-NOAA Operational Model Archive and Distribution System

Science.gov Websites

Forecast Maps Climate Climate Prediction Climate Archives Weather Safety Storm Ready NOAA Central Library (16km) 6 hours grib filter http OpenDAP-alt URMA hourly - http - Climate Models Climate Forecast System Flux Products 6 hours grib filter http - Climate Forecast System 3D Pressure Products 6 hours grib

The visualisation and communication of probabilistic climate forecasts to renewable energy policy makers

NASA Astrophysics Data System (ADS)

Doblas-Reyes, F.; Steffen, S.; Lowe, R.; Davis, M.; Rodó, X.

2013-12-01

Despite the strong dependence of weather and climate variability on the renewable energy industry, and several initiatives towards demonstrating the added benefits of integrating probabilistic forecasts into energy decision making process, they are still under-utilised within the sector. Improved communication is fundamental to stimulate the use of climate forecast information within decision-making processes, in order to adapt to a highly climate dependent renewable energy industry. This paper focuses on improving the visualisation of climate forecast information, paying special attention to seasonal to decadal (s2d) timescales. This is central to enhance climate services for renewable energy, and optimise the usefulness and usability of inherently complex climate information. In the realm of the Global Framework for Climate Services (GFCS) initiative, and subsequent European projects: Seasonal-to-Decadal Climate Prediction for the Improvement of European Climate Service (SPECS) and the European Provision of Regional Impacts Assessment in Seasonal and Decadal Timescales (EUPORIAS), this paper investigates the visualisation and communication of s2d forecasts with regards to their usefulness and usability, to enable the development of a European climate service. The target end user will be renewable energy policy makers, who are central to enhance climate services for the energy industry. The overall objective is to promote the wide-range dissemination and exchange of actionable climate information based on s2d forecasts from Global Producing Centres (GPC's). Therefore, it is crucial to examine the existing main barriers and deficits. Examples of probabilistic climate forecasts from different GPC's were used to prepare a catalogue of current approaches, to assess their advantages and limitations and finally to recommend better alternatives. In parallel, interviews were conducted with renewable energy stakeholders to receive feedback for the improvement of existing visualisation techniques of forecasts. The overall aim is to establish a communication protocol for the visualisation of probabilistic climate forecasts, which does not currently exist. Global Producing Centres show their own probabilistic forecasts with limited consistency in their communication across different centres, which complicates the understanding for the end user. A communication protocol for both the visualisation and description of climate forecasts can help to introduce a standard format and message to end users from several climate-sensitive sectors, such as energy, tourism, agriculture and health. It is hoped that this work will facilitate the improvement of decision-making processes relying on forecast information and enable their wide-range dissemination based on a standardised approach.

Forecasting European Wildfires Today and in the Future

NASA Astrophysics Data System (ADS)

Navarro Abellan, Maria; Porras Alegre, Ignasi; María Sole, Josep; Gálvez, Pedro; Bielski, Conrad; Nurmi, Pertti

2017-04-01

Society as a whole is increasingly exposed and vulnerable to natural disasters due to extreme weather events exacerbated by climate change. The increased frequency of wildfires is not only a result of a changing climate, but wildfires themselves also produce a significant amount of greenhouse gases that, in-turn, further contribute to global warming. I-REACT (Improving Resilience to Emergencies through Advanced Cyber Technologies) is an innovation project funded by the European Commission , which aims to use social media, smartphones and wearables to improve natural disaster management by integrating existing services, both local and European, into a platform that supports the entire emergency management cycle. In order to assess the impact of climate change on wildfire hazards, METEOSIM designed two different System Processes (SP) that will be integrated into the I-REACT service that can provide information on a variety of time scales. SP1 - Climate Change Impact The climate change impact on climate variables related to fires is calculated by building an ensemble based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CORDEX data. A validation and an Empirical-Statistical Downscaling (ESD) calibration are done to assess the changes in the past of the climatic variables related to wildfires (temperature, precipitation, wind, relative humidity and Fire Weather Index). Calculations in the trend and the frequency of extreme events of those variables are done for three time scales: near-term (2011-2040), mid-term (2041-2070) and long term (2071-2100). SP2 - Operational daily forecast of the Canadian Forest Fire Weather Index (FWI) Using ensemble data from the ECMWF and from the GLAMEPS (multi-model ensemble) models, both supplied by the Finnish Meteorological Institute (FMI), the Fire Weather Index (FWI) and its index components are produced for each ensemble member within a wide forecast time range, from a few hours up to 10 days resulting in a probabilistic output of the FWI for different regions in Europe. This work will improve the currently available information to various wildfire information users such as fire departments, the civil protection, local authorities, etc., where accurate and reliable information in extreme weather situations are vital for improving planning and risk management.

Application of a Hybrid Forest Growth Model to Evaluate Climate Change Impacts on Productivity, Nutrient Cycling and Mortality in a Montane Forest Ecosystem.

PubMed

Seely, Brad; Welham, Clive; Scoullar, Kim

2015-01-01

Climate change introduces considerable uncertainty in forest management planning and outcomes, potentially undermining efforts at achieving sustainable practices. Here, we describe the development and application of the FORECAST Climate model. Constructed using a hybrid simulation approach, the model includes an explicit representation of the effect of temperature and moisture availability on tree growth and survival, litter decomposition, and nutrient cycling. The model also includes a representation of the impact of increasing atmospheric CO2 on water use efficiency, but no direct CO2 fertilization effect. FORECAST Climate was evaluated for its ability to reproduce the effects of historical climate on Douglas-fir and lodgepole pine growth in a montane forest in southern British Columbia, Canada, as measured using tree ring analysis. The model was subsequently used to project the long-term impacts of alternative future climate change scenarios on forest productivity in young and established stands. There was a close association between predicted sapwood production and measured tree ring chronologies, providing confidence that model is able to predict the relative impact of annual climate variability on tree productivity. Simulations of future climate change suggest a modest increase in productivity in young stands of both species related to an increase in growing season length. In contrast, results showed a negative impact on stemwood biomass production (particularly in the case of lodgepole pine) for established stands due to increased moisture stress mortality.

Application of a Hybrid Forest Growth Model to Evaluate Climate Change Impacts on Productivity, Nutrient Cycling and Mortality in a Montane Forest Ecosystem

PubMed Central

Seely, Brad; Welham, Clive; Scoullar, Kim

2015-01-01

Climate change introduces considerable uncertainty in forest management planning and outcomes, potentially undermining efforts at achieving sustainable practices. Here, we describe the development and application of the FORECAST Climate model. Constructed using a hybrid simulation approach, the model includes an explicit representation of the effect of temperature and moisture availability on tree growth and survival, litter decomposition, and nutrient cycling. The model also includes a representation of the impact of increasing atmospheric CO2 on water use efficiency, but no direct CO2 fertilization effect. FORECAST Climate was evaluated for its ability to reproduce the effects of historical climate on Douglas-fir and lodgepole pine growth in a montane forest in southern British Columbia, Canada, as measured using tree ring analysis. The model was subsequently used to project the long-term impacts of alternative future climate change scenarios on forest productivity in young and established stands. There was a close association between predicted sapwood production and measured tree ring chronologies, providing confidence that model is able to predict the relative impact of annual climate variability on tree productivity. Simulations of future climate change suggest a modest increase in productivity in young stands of both species related to an increase in growing season length. In contrast, results showed a negative impact on stemwood biomass production (particularly in the case of lodgepole pine) for established stands due to increased moisture stress mortality. PMID:26267446

Simulating post-wildfire forest trajectories under alternative climate and management scenarios.

PubMed

Tarancón, Alicia Azpeleta; Fulé, Peter Z; Shive, Kristen L; Sieg, Carolyn H; Meador, Andrew Sánchez; Strom, Barbara

Post-fire predictions of forest recovery under future climate change and management actions are necessary for forest managers to make decisions about treatments. We applied the Climate-Forest Vegetation Simulator (Climate-FVS), a new version of a widely used forest management model, to compare alternative climate and management scenarios in a severely burned multispecies forest of Arizona, USA. The incorporation of seven combinations of General Circulation Models (GCM) and emissions scenarios altered long-term (100 years) predictions of future forest condition compared to a No Climate Change (NCC) scenario, which forecast a gradual increase to high levels of forest density and carbon stock. In contrast, emissions scenarios that included continued high greenhouse gas releases led to near-complete deforestation by 2111. GCM-emissions scenario combinations that were less severe reduced forest structure and carbon stock relative to NCC. Fuel reduction treatments that had been applied prior to the severe wildfire did have persistent effects, especially under NCC, but were overwhelmed by increasingly severe climate change. We tested six management strategies aimed at sustaining future forests: prescribed burning at 5, 10, or 20-year intervals, thinning 40% or 60% of stand basal area, and no treatment. Severe climate change led to deforestation under all management regimes, but important differences emerged under the moderate scenarios: treatments that included regular prescribed burning fostered low density, wildfire-resistant forests composed of the naturally dominant species, ponderosa pine. Non-fire treatments under moderate climate change were forecast to become dense and susceptible to severe wildfire, with a shift to dominance by sprouting species. Current U.S. forest management requires modeling of future scenarios but does not mandate consideration of climate change effects. However, this study showed substantial differences in model outputs depending on climate and management actions. Managers should incorporate climate change into the process of analyzing the environmental effects of alternative actions.

A New Integrated Threshold Selection Methodology for Spatial Forecast Verification of Extreme Events

NASA Astrophysics Data System (ADS)

Kholodovsky, V.

2017-12-01

Extreme weather and climate events such as heavy precipitation, heat waves and strong winds can cause extensive damage to the society in terms of human lives and financial losses. As climate changes, it is important to understand how extreme weather events may change as a result. Climate and statistical models are often independently used to model those phenomena. To better assess performance of the climate models, a variety of spatial forecast verification methods have been developed. However, spatial verification metrics that are widely used in comparing mean states, in most cases, do not have an adequate theoretical justification to benchmark extreme weather events. We proposed a new integrated threshold selection methodology for spatial forecast verification of extreme events that couples existing pattern recognition indices with high threshold choices. This integrated approach has three main steps: 1) dimension reduction; 2) geometric domain mapping; and 3) thresholds clustering. We apply this approach to an observed precipitation dataset over CONUS. The results are evaluated by displaying threshold distribution seasonally, monthly and annually. The method offers user the flexibility of selecting a high threshold that is linked to desired geometrical properties. The proposed high threshold methodology could either complement existing spatial verification methods, where threshold selection is arbitrary, or be directly applicable in extreme value theory.

Near term climate projections for invasive species distributions

USGS Publications Warehouse

Jarnevich, C.S.; Stohlgren, T.J.

2009-01-01

Climate change and invasive species pose important conservation issues separately, and should be examined together. We used existing long term climate datasets for the US to project potential climate change into the future at a finer spatial and temporal resolution than the climate change scenarios generally available. These fine scale projections, along with new species distribution modeling techniques to forecast the potential extent of invasive species, can provide useful information to aide conservation and invasive species management efforts. We created habitat suitability maps for Pueraria montana (kudzu) under current climatic conditions and potential average conditions up to 30 years in the future. We examined how the potential distribution of this species will be affected by changing climate, and the management implications associated with these changes. Our models indicated that P. montana may increase its distribution particularly in the Northeast with climate change and may decrease in other areas. ?? 2008 Springer Science+Business Media B.V.

September Arctic Sea Ice minimum prediction - a new skillful statistical approach

NASA Astrophysics Data System (ADS)

Ionita-Scholz, Monica; Grosfeld, Klaus; Scholz, Patrick; Treffeisen, Renate; Lohmann, Gerrit

2017-04-01

Sea ice in both Polar Regions is an important indicator for the expression of global climate change and its polar amplification. Consequently, a broad interest exists on sea ice, its coverage, variability and long term change. Knowledge on sea ice requires high quality data on ice extent, thickness and its dynamics. However, its predictability is complex and it depends on various climate and oceanic parameters and conditions. In order to provide insights into the potential development of a monthly/seasonal signal of sea ice evolution, we developed a robust statistical model based on ocean heat content, sea surface temperature and different atmospheric variables to calculate an estimate of the September Sea ice extent (SSIE) on monthly time scale. Although previous statistical attempts at monthly/seasonal forecasts of SSIE show a relatively reduced skill, we show here that more than 92% (r = 0.96) of the September sea ice extent can be predicted at the end of May by using previous months' climate and oceanic conditions. The skill of the model increases with a decrease in the time lag used for the forecast. At the end of August, our predictions are even able to explain 99% of the SSIE. Our statistical model captures both the general trend as well as the interannual variability of the SSIE. Moreover, it is able to properly forecast the years with extreme high/low SSIE (e.g. 1996/ 2007, 2012, 2013). Besides its forecast skill for SSIE, the model could provide a valuable tool for identifying relevant regions and climate parameters that are important for the sea ice development in the Arctic and for detecting sensitive and critical regions in global coupled climate models with focus on sea ice formation.

High resolution climate projection of storm surge at the Venetian coast

NASA Astrophysics Data System (ADS)

Mel, R.; Sterl, A.; Lionello, P.

2013-04-01

Climate change impact on storm surge regime is of great importance for the safety and maintenance of Venice. In this study a future storm surge scenario is evaluated using new high resolution sea level pressure and wind data recently produced by EC-Earth, an Earth System Model based on the operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF). The study considers an ensemble of six 5 yr long simulations of the rcp45 scenario at 0.25° resolution and compares the 2094-2098 to the 2004-2008 period. EC-Earth sea level pressure and surface wind fields are used as input for a shallow water hydrodynamic model (HYPSE) which computes sea level and barotropic currents in the Adriatic Sea. Results show that a high resolution climate model is needed for producing realistic values of storm surge statistics and confirm previous studies in that they show little sensitivity of storm surge levels to climate change. However, some climate change signals are detected, such as increased persistence of high pressure conditions, an increased frequency of windless hour, and a decreased number of moderate windstorms.

Forecasting civil conflict along the shared socioeconomic pathways

DOE PAGES

Hegre, Håvard; Buhaug, Halvard; Calvin, Katherine V.; ...

2016-04-25

Climate change and armed civil conflict are both linked to socioeconomic development, although conditions that facilitate peace may not necessarily facilitate mitigation and adaptation to climate change. While economic growth lowers the risk of conflict, it is generally associated with increased greenhouse gas emissions and costs of climate mitigation policies. Here, this study investigates the links between growth, climate change, and conflict by simulating future civil conflict using new scenario data for five alternative socioeconomic pathways with different mitigation and adaptation assumptions, known as the shared socioeconomic pathways (SSPs). We develop a statistical model of the historical effect of keymore » socioeconomic variables on country-specific conflict incidence, 1960–2013. We then forecast the annual incidence of conflict, 2014–2100, along the five SSPs. We find that SSPs with high investments in broad societal development are associated with the largest reduction in conflict risk. This is most pronounced for the least developed countries—poverty alleviation and human capital investments in poor countries are much more effective instruments to attain global peace and stability than further improvements to wealthier economies. Moreover, the SSP that describes a sustainability pathway, which poses the lowest climate change challenges, is as conducive to global peace as the conventional development pathway.« less

Forecasting civil conflict along the shared socioeconomic pathways

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

Hegre, Håvard; Buhaug, Halvard; Calvin, Katherine V.

Climate change and armed civil conflict are both linked to socioeconomic development, although conditions that facilitate peace may not necessarily facilitate mitigation and adaptation to climate change. While economic growth lowers the risk of conflict, it is generally associated with increased greenhouse gas emissions and costs of climate mitigation policies. Here, this study investigates the links between growth, climate change, and conflict by simulating future civil conflict using new scenario data for five alternative socioeconomic pathways with different mitigation and adaptation assumptions, known as the shared socioeconomic pathways (SSPs). We develop a statistical model of the historical effect of keymore » socioeconomic variables on country-specific conflict incidence, 1960–2013. We then forecast the annual incidence of conflict, 2014–2100, along the five SSPs. We find that SSPs with high investments in broad societal development are associated with the largest reduction in conflict risk. This is most pronounced for the least developed countries—poverty alleviation and human capital investments in poor countries are much more effective instruments to attain global peace and stability than further improvements to wealthier economies. Moreover, the SSP that describes a sustainability pathway, which poses the lowest climate change challenges, is as conducive to global peace as the conventional development pathway.« less

Understanding and predicting climate variations in the Middle East for sustainable water resource management and development

NASA Astrophysics Data System (ADS)

Samuels, Rana

Water issues are a source of tension between Israelis and Palestinians. In the and region of the Middle East, water supply is not just scarce but also uncertain: It is not uncommon for annual rainfall to be as little as 60% or as much as 125% of the multiannual average. This combination of scarcity and uncertainty exacerbates the already strained economy and the already tensed political situation. The uncertainty could be alleviated if it were possible to better forecast water availability. Such forecasting is key not only for water planning and management, but also for economic policy and for political decision making. Water forecasts at multiple time scales are necessary for crop choice, aquifer operation and investments in desalination infrastructure. The unequivocal warming of the climate system adds another level of uncertainty as global and regional water cycles change. This makes the prediction of water availability an even greater challenge. Understanding the impact of climate change on precipitation can provide the information necessary for appropriate risk assessment and water planning. Unfortunately, current global circulation models (GCMs) are only able to predict long term climatic evolution at large scales but not local rainfall. The statistics of local precipitation are traditionally predicted using historical rainfall data. Obviously these data cannot anticipate changes that result from climate change. It is therefore clear that integration of the global information about climate evolution and local historical data is needed to provide the much needed predictions of regional water availability. Currently, there is no theoretical or computational framework that enables such integration for this region. In this dissertation both a conceptual framework and a computational platform for such integration are introduced. In particular, suite of models that link forecasts of climatic evolution under different CO2 emissions scenarios to observed rainfall data from local stations are developed. These are used to develop scenarios for local rainfall statistics such as average annual amounts, dry spells, wet spells and drought persistence. This suite of models can provide information that is not attainable from existing tools in terms of its spatial and temporal resolution. Specifically, the goal is to project the impact of established global climate change scenarios in this region and, how much of the change might be mitigated by proposed CO2 reduction strategies. A major problem in this enterprise is to find the best way to integrate global climatic information with local rainfall data. From the climatologic perspective the problem is to find the right teleconnections. That is, non local or global measurable phenomena that influence local rainfall in a way that could be characterized and quantified statistically. From the computational perspective the challenge is to model these subtle, nonlinear relationships and to downscale the global effects into local predictions. Climate simulations to the year 2100 under selected climate change scenarios are used. Overall, the suite of models developed and presented can be applied to answer most questions from the different water users and planners. Farmers and the irrigation community can ask "What is the probability of rain over the next week?" Policy makers can ask "How much desalination capacity will I need to meet demand 90% of the time in the climate change scenario over the next 20 years?" Aquifer managers can ask "What is the expected recharge rate of the aquifers over the next decade?" The use of climate driven answers to these questions will help the region better prepare and adapt to future shifts in water resources and availability.

Probabilistic accounting of uncertainty in forecasts of species distributions under climate change

USGS Publications Warehouse

Wenger, Seth J.; Som, Nicholas A.; Dauwalter, Daniel C.; Isaak, Daniel J.; Neville, Helen M.; Luce, Charles H.; Dunham, Jason B.; Young, Michael K.; Fausch, Kurt D.; Rieman, Bruce E.

2013-01-01

Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing models (model uncertainty), and uncertainty in future climate conditions (climate uncertainty) to produce site-specific frequency distributions of occurrence probabilities across a species’ range. We illustrated the method by forecasting suitable habitat for bull trout (Salvelinus confluentus) in the Interior Columbia River Basin, USA, under recent and projected 2040s and 2080s climate conditions. The 95% interval of total suitable habitat under recent conditions was estimated at 30.1–42.5 thousand km; this was predicted to decline to 0.5–7.9 thousand km by the 2080s. Projections for the 2080s showed that the great majority of stream segments would be unsuitable with high certainty, regardless of the climate data set or bull trout model employed. The largest contributor to uncertainty in total suitable habitat was climate uncertainty, followed by parameter uncertainty and model uncertainty. Our approach makes it possible to calculate a full distribution of possible outcomes for a species, and permits ready graphical display of uncertainty for individual locations and of total habitat.

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

Boer, George J.; Smith, Douglas M.; Cassou, Christophe

The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Predictionmore » (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Furthermore, groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.« less

Analysis of the Climate Change Technology Initiative

EIA Publications

1999-01-01

Analysis of the impact of specific policies on the reduction of carbon emissions and their impact on U.S. energy use and prices in the 2008-2012 time frame. Also, analyzes the impact of the President's Climate Change Technology Initiative, as defined for the 2000 budget, on reducing carbon emissions from the levels forecast in the Annual Energy Outlook 1999 reference case.

Potential effects of climate change on freshwater ecosystems of the New England/Mid-Atlantic Region

Treesearch

Marianne V. Moore; Michael L. Pace; John R. Mather; [and others; [Editor’s note: Patricia A. Flebbe is the SRS co-author for this publication.

1997-01-01

Numerous freshwater ecosystems, dense concentrations of humans along the eastern seaboard, extensive forests, and a history of intensive land use distinguish the New England/Mid-Atlantic Region. Human population densities are forecast to increase in portions of the region at the same time that climate is expected to be changing. Consequently, the effects of humans and...

Impact of seasonal forecast use on agricultural income in a system with varying crop costs and returns: an empirically-grounded simulation

NASA Astrophysics Data System (ADS)

Gunda, T.; Bazuin, J. T.; Nay, J.; Yeung, K. L.

2017-03-01

Access to seasonal climate forecasts can benefit farmers by allowing them to make more informed decisions about their farming practices. However, it is unclear whether farmers realize these benefits when crop choices available to farmers have different and variable costs and returns; multiple countries have programs that incentivize production of certain crops while other crops are subject to market fluctuations. We hypothesize that the benefits of forecasts on farmer livelihoods will be moderated by the combined impact of differing crop economics and changing climate. Drawing upon methods and insights from both physical and social sciences, we develop a model of farmer decision-making to evaluate this hypothesis. The model dynamics are explored using empirical data from Sri Lanka; primary sources include survey and interview information as well as game-based experiments conducted with farmers in the field. Our simulations show that a farmer using seasonal forecasts has more diversified crop selections, which drive increases in average agricultural income. Increases in income are particularly notable under a drier climate scenario, when a farmer using seasonal forecasts is more likely to plant onions, a crop with higher possible returns. Our results indicate that, when water resources are scarce (i.e. drier climate scenario), farmer incomes could become stratified, potentially compounding existing disparities in farmers’ financial and technical abilities to use forecasts to inform their crop selections. This analysis highlights that while programs that promote production of certain crops may ensure food security in the short-term, the long-term implications of these dynamics need careful evaluation.

Investigation of the climate change within Moscow metropolitan area

NASA Astrophysics Data System (ADS)

Varentsov, Mikhail; Trusilova, Kristina; Konstantinov, Pavel; Samsonov, Timofey

2014-05-01

As the urbanization continues worldwide more than half of the Earth's population live in the cities (U.N., 2010). Therefore the vulnerability of the urban environment - the living space for millions of people - to the climate change has to be investigated. It is well known that urban features strongly influence the atmospheric boundary layer and determine the microclimatic features of the local environment, such as urban heat island (UHI). Available temperature observations in cities are, however, influenced by the natural climate variations, human-induced climate warming (IPCC, 2007) and in the same time by the growth and structural modification of the urban areas. The relationship between these three factors and their roles in climate changes in the cities are very important for the climatic forecast and requires better understanding. In this study, we made analysis of the air temperature change and urban heat island evolution within Moscow urban area during decades 1970-2010, while this urban area had undergone intensive growth and building modification allowing the population of Moscow to increase from 7 to 12 million people. Analysis was based on the data from several meteorological stations in Moscow region and Moscow city, including meteorological observatory of Lomonosov Moscow State University. Differences in climate change between urban and rural stations, changes of the power and shape of urban heat island and their relationships with changes of building height and density were investigated. Collected data and obtained results are currently to be used for the validation of the regional climate model COSMO-CLM with the purpose to use this model for further more detailed climate research and forecasts for Moscow metropolitan area. References: 1. U.N. (2010), World Urbanization Prospects. The 2009 Revision.Rep., 1-47 pp, United Nations. Department of Economic and Social Affairs. Population Division., New York. 2. IPCC (2007), IPCC Fourth Assessment Report: Climate Change 2007 (AR4) Rep.,Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Near Real-time Ecological Forecasting of Peatland Responses to Warming and CO2 Treatment through EcoPAD-SPRUCE

NASA Astrophysics Data System (ADS)

Huang, Y.; Jiang, J.; Stacy, M.; Ricciuto, D. M.; Hanson, P. J.; Sundi, N.; Luo, Y.

2016-12-01

Ecological forecasting is critical in various aspects of our coupled human-nature systems, such as disaster risk reduction, natural resource management and climate change mitigation. Novel advancements are in urgent need to deepen our understandings of ecosystem dynamics, boost the predictive capacity of ecology, and provide timely and effective information for decision-makers in a rapidly changing world. Our Ecological Platform for Assimilation of Data (EcoPAD) facilitates the integration of current best knowledge from models, manipulative experimentations, observations and other modern techniques and provides both near real-time and long-term forecasting of ecosystem dynamics. As a case study, the web-based EcoPAD platform synchronizes real- or near real-time field measurements from the Spruce and Peatland Responses Under Climatic and Environmental Change Experiment (SPRUCE), a whole ecosystem warming and CO2 enrichment treatment experiment, assimilates multiple data streams into process based models, enhances timely feedback between modelers and experimenters, and ultimately improves ecosystem forecasting and makes best utilization of current knowledge. In addition to enable users to (i) estimate model parameters or state variables, (ii) quantify uncertainty of estimated parameters and projected states of ecosystems, (iii) evaluate model structures, (iv) assess sampling strategies, and (v) conduct ecological forecasting, EcoPAD-SPRUCE automated the workflow from real-time data acquisition, model simulation to result visualization. EcoPAD-SPRUCE promotes seamless feedback between modelers and experimenters, hand in hand to make better forecasting of future changes. The framework of EcoPAD-SPRUCE (with flexible API, Application Programming Interface) is easily portable and will benefit scientific communities, policy makers as well as the general public.

Economic Value of Weather and Climate Forecasts

NASA Astrophysics Data System (ADS)

Katz, Richard W.; Murphy, Allan H.

1997-06-01

Weather and climate extremes can significantly impact the economics of a region. This book examines how weather and climate forecasts can be used to mitigate the impact of the weather on the economy. Interdisciplinary in scope, it explores the meteorological, economic, psychological, and statistical aspects of weather prediction. Chapters by area specialists provide a comprehensive view of this timely topic. They encompass forecasts over a wide range of temporal scales, from weather over the next few hours to the climate months or seasons ahead, and address the impact of these forecasts on human behavior. Economic Value of Weather and Climate Forecasts seeks to determine the economic benefits of existing weather forecasting systems and the incremental benefits of improving these systems, and will be an interesting and essential text for economists, statisticians, and meteorologists.

Ecological constraints increase the climatic debt in forests

PubMed Central

Bertrand, Romain; Riofrío-Dillon, Gabriela; Lenoir, Jonathan; Drapier, Jacques; de Ruffray, Patrice; Gégout, Jean-Claude; Loreau, Michel

2016-01-01

Biodiversity changes are lagging behind current climate warming. The underlying determinants of this climatic debt are unknown and yet critical to understand the impacts of climate change on the present biota and improve forecasts of biodiversity changes. Here we assess determinants of climatic debt accumulated in French forest herbaceous plant communities between 1987 and 2008 (that is, a 1.05 °C mean difference between the observed and bioindicated temperatures). We show that warmer baseline conditions predispose plant communities to larger climatic debts, and that climate warming exacerbates this response. Forest plant communities, however, are absorbing part of the temperature increase mainly through the species' ability to tolerate changing climate. As climate warming is expected to accelerate during the twenty-first century, plant migration and tolerance to climatic stresses probably will be insufficient to absorb this impact posing threats to the sustainability of forest plant communities. PMID:27561410

Ecological constraints increase the climatic debt in forests

NASA Astrophysics Data System (ADS)

Bertrand, Romain; Riofrío-Dillon, Gabriela; Lenoir, Jonathan; Drapier, Jacques; de Ruffray, Patrice; Gégout, Jean-Claude; Loreau, Michel

2016-08-01

Biodiversity changes are lagging behind current climate warming. The underlying determinants of this climatic debt are unknown and yet critical to understand the impacts of climate change on the present biota and improve forecasts of biodiversity changes. Here we assess determinants of climatic debt accumulated in French forest herbaceous plant communities between 1987 and 2008 (that is, a 1.05 °C mean difference between the observed and bioindicated temperatures). We show that warmer baseline conditions predispose plant communities to larger climatic debts, and that climate warming exacerbates this response. Forest plant communities, however, are absorbing part of the temperature increase mainly through the species' ability to tolerate changing climate. As climate warming is expected to accelerate during the twenty-first century, plant migration and tolerance to climatic stresses probably will be insufficient to absorb this impact posing threats to the sustainability of forest plant communities.

Statistical models of temperature in the Sacramento-San Joaquin delta under climate-change scenarios and ecological implications

USGS Publications Warehouse

Wagner, R.W.; Stacey, M.; Brown, L.R.; Dettinger, M.

2011-01-01

Changes in water temperatures caused by climate change in California's Sacramento-San Joaquin Delta will affect the ecosystem through physiological rates of fishes and invertebrates. This study presents statistical models that can be used to forecast water temperature within the Delta as a response to atmospheric conditions. The daily average model performed well (R2 values greater than 0.93 during verification periods) for all stations within the Delta and San Francisco Bay provided there was at least 1 year of calibration data. To provide long-term projections of Delta water temperature, we forced the model with downscaled data from climate scenarios. Based on these projections, the ecological implications for the delta smelt, a key species, were assessed based on temperature thresholds. The model forecasts increases in the number of days above temperatures causing high mortality (especially along the Sacramento River) and a shift in thermal conditions for spawning to earlier in the year. ?? 2011 The Author(s).

Technology assessment network building: The International Association of Technology Assessment and Forecasting Institutions

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

Williams, G.; Andersen, J.

1996-01-01

With the globalization of trade and the increased understanding of transboundary problems such as global climate change, the need for understanding the consequences of technological change has never been higher. Institutional arrangements necessary to assess these changes and make decision makers aware of the consequences have not necessarily adapted to these world conditions. In response to this leading technology assessment and forecasting institutions formed an international association of technology assessment and forecasting institutions to assist in the diffusion of technology assessment in the decision-making process. This paper discusses the origins of the International Association of Technology Assessment and Forecasting Institutionsmore » (IATAFI) and the goals and the vision for the organization. The articles cited represent some of the topics discussed at the first IATAFI conference in Bergen, Norway in May 1994.« less

Diverse Responses of Global Vegetation to Climate Changes: Spatial Patterns and Time-lag Effects

NASA Astrophysics Data System (ADS)

Wu, D.; Zhao, X.; Zhou, T.; Huang, K.; Xu, W.

2014-12-01

Global climate changes have enormous influences on vegetation growth, meanwhile, response of vegetation to climate express space diversity and time-lag effects, which account for spatial-temporal disparities of climate change and spatial heterogeneity of ecosystem. Revelation of this phenomenon will help us further understanding the impact of climate change on vegetation. Assessment and forecast of global environmental change can be also improved under further climate change. Here we present space diversity and time-lag effects patterns of global vegetation respond to three climate factors (temperature, precipitation and solar radiation) based on quantitative analysis of satellite data (NDVI) and Climate data (Climate Research Unit). We assessed the time-lag effects of global vegetation to main climate factors based on the great correlation fitness between NDVI and the three climate factors respectively among 0-12 months' temporal lags. On this basis, integrated response model of NDVI and the three climate factors was built to analyze contribution of different climate factors to vegetation growth with multiple regression model and partial correlation model. In the result, different vegetation types have distinct temporal lags to the three climate factors. For the precipitation, temporal lags of grasslands are the shortest while the evergreen broad-leaf forests are the longest, which means that grasslands are more sensitive to precipitation than evergreen broad-leaf forests. Analysis of different climate factors' contribution to vegetation reveal that vegetation are dominated by temperature in the high northern latitudes; they are mainly restricted by precipitation in arid and semi-arid areas (Australia, Western America); in humid areas of low and intermediate latitudes (Amazon, Eastern America), vegetation are mainly influenced by solar radiation. Our results reveal the time-lag effects and major driving factors of global vegetation growth and explain the spatiotemporal variations of global vegetation in last 30 years. Significantly, it is as well as in forecasting and assessing the influences of future climate change on the vegetation dynamics. This work was supported by the High Technology Research and Development Program of China (Grant NO.2013AA122801).

Teleconnections in the Presence of Climate Change: A Case Study of the Annular Modes

NASA Astrophysics Data System (ADS)

Gerber, Edwin; Baldwin, Mark

2010-05-01

Long model integrations of future and past climates present a problem for defining teleconnection patterns through Empirical Orthogonal Function (EOF) or correlation analysis when trends in the underlying climate begin to dominate the covariance structure. Similar issues may soon appear in observations as the record becomes longer, especially if climate trends accelerate. The Northern and Southern Annular Modes provide a prime example, because the poleward shift of the jet streams strongly projects onto these patterns, particularly in the Southern Hemisphere. Climate forecasts of the 21st century by chemistry climate models provide a case study. Computation of the annular modes in these long data sets with secular trends requires refinement of the standard definition of the annular mode, and a more robust procedure that allows for slowly varying trends is established and verified. The new procedure involves two key changes. First, the global mean geopotential height is removed at each time step before computing anomalies. This is particularly important high in the atmosphere, where seasonal variations in geopotential height become significant, and filters out trends due to changes in the temperature structure of the atmosphere. Pattern definition can be very sensitive near the tropopause, as regions of the atmosphere that used to be more of stratospheric character begin to take on tropospheric characteristics as the tropopause rises. The second change is to define anomalies relative to a slowly evolving seasonal climatology, so that the covariance structure reflects internal variability. Once these changes are accounted for, it is found that the zonal mean variability of the atmosphere stays remarkably constant, despite significant changes in the baseline climate forecast for the rest of the century. This stability of the internal variability makes it possible to relate trends in climate to teleconnections.

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.

Climate Change Impacts on Worldwide Coffee Production

NASA Astrophysics Data System (ADS)

Foreman, T.; Rising, J. A.

2015-12-01

Coffee (Coffea arabica and Coffea canephora) plays a vital role in many countries' economies, providing necessary income to 25 million members of tropical countries, and supporting a $81 billion industry, making it one of the most valuable commodities in the world. At the same time, coffee is at the center of many issues of sustainability. It is vulnerable to climate change, with disease outbreaks becoming more common and suitable regions beginning to shift. We develop a statistical production model for coffee which incorporates temperature, precipitation, frost, and humidity effects using a new database of worldwide coffee production. We then use this model to project coffee yields and production into the future based on a variety of climate forecasts. This model can then be used together with a market model to forecast the locations of future coffee production as well as future prices, supply, and demand.

An Integrated Urban Flood Analysis System in South Korea

NASA Astrophysics Data System (ADS)

Moon, Young-Il; Kim, Min-Seok; Yoon, Tae-Hyung; Choi, Ji-Hyeok

2017-04-01

Due to climate change and the rapid growth of urbanization, the frequency of concentrated heavy rainfall has caused urban floods. As a result, we studied climate change in Korea and developed an integrated flood analysis system that systematized technology to quantify flood risk and flood forecasting in urban areas. This system supports synthetic decision-making through real-time monitoring and prediction on flash rain or short-term rainfall by using radar and satellite information. As part of the measures to deal with the increase of inland flood damage, we have found it necessary to build a systematic city flood prevention system that systematizes technology to quantify flood risk as well as flood forecast, taking into consideration both inland and river water. This combined inland-river flood analysis system conducts prediction on flash rain or short-term rainfall by using radar and satellite information and performs prompt and accurate prediction on the inland flooded area. In addition, flood forecasts should be accurate and immediate. Accurate flood forecasts signify that the prediction of the watch, warning time and water level is precise. Immediate flood forecasts represent the forecasts lead time which is the time needed to evacuate. Therefore, in this study, in order to apply rainfall-runoff method to medium and small urban stream for flood forecasts, short-term rainfall forecasting using radar is applied to improve immediacy. Finally, it supports synthetic decision-making for prevention of flood disaster through real-time monitoring. Keywords: Urban Flood, Integrated flood analysis system, Rainfall forecasting, Korea Acknowledgments This research was supported by a grant (16AWMP-B066744-04) from Advanced Water Management Research Program (AWMP) funded by Ministry of Land, Infrastructure and Transport of Korean government.

Application of satellite information for decision of scientific and applied tasks

NASA Astrophysics Data System (ADS)

Lyalko, Vadim

Scientific Center for Aerospace Research of the Earth GSI NASU (CASRE) has developed methods of satellite observation of the earth surface for assessment of separate anthropogenic and natural safety parameters. 1. Space audit of greenhouse gases balance It was created space technology of forecasting and monitoring of emissions changes and carbon dioxide absorption by vegetation over the territory of Ukraine with the aim to make recommendations on efficient correction of climate-protective measures. The results of these investigations became a part of the Proposal of NASU to Cabinet of Ministries of Ukraine as for execution of its Decree from 05.03.2009 "Development of the strategic forecasting of climate change, consequences of such change for sectors of economics. . . " Recently scientists of NASU establishments, especially collaborating with international scientific community, obtained significant results in assessment and forecast not only of climate change but also geosystems condition not only of Ukraine but the whole Eastern Europe. According to obtained materials maps and diagrams of emissions' distribution and carbon dioxide absorption by vegetation at administrative regions were firstly built for Ukraine. According to this it's possible to make administrative decisions on rational management of nature and greenhouse gases' quotas trading according to Kyoto Protocol. Obtained results allow making next decisions: — Predominance of industrial CO2 emissions over its absorption by vegetation canopy almost twice is characteristic for the territory of Ukraine; — It's traced a certain regularity of zones' localization of the most intensive increase of CO2 concentration over industrially developed regions; — Negative biotic compensation of anthropogenic influence is observed on the considerable territory of Ukraine. It means that nat-ural environment has not time to renew those resources which human being uses in the process of his vital activity. As a rule these are territories with high density of population and this is connected with unequal population and industry allocation on the territory of Ukraine. During 2009 it was conducted comparative data analysis about changes of greenhouse gases concen-tration over the territory of Ukraine from satellites ENVISAT (ESA, sensor SCIAMACHI) and Aqua (NASA, sensor AIRS). The analysis showed that data from different satellites complement one another and correspond to general tendencies which were fixed by ground methods. Those it was obtained the opportunity considerably increase the reliability of satellite assessments. UNO International Conference on Climate Changes which came to an end in December 2009 in Copenhagen confirmed the necessity to decrease greenhouse gases emissions for all coun-tries and continue to implement measures, according to requirements of Kyoto Protocol, for the society adaptation to climate changes. Thereby it's reasonable to carry out international researches within the framework of correspondent project, using obtained experience of our previous results in this sphere. The aim of these works is creation of the system of space audit monitoring of greenhouse gases balance for the reliable grounding and specification of their quotas for different countries and assessment of potential opportunities for quotas trading, in particular by Ukraine. To do this it's necessary to base the role of future Ukrainian satellites with correspondent sensors and systems of tested polygons. 2. Analysis of biophysical and spectral parameters changes for the geosystems with the aim of ecological forecasting. It was created the scientific base for risks forecasting of hydrologi-cal emergency situations (i.e. floods) and events induced by them (slides and submergences) during the further 30 years. The development is based on the application of energy-mass ex-change modeling in the geosystems and changes of climatic indications. Implementation of the developed approaches showed that at the resent time it needs to expect the escalation of these dangerous phenomena. Moreover the extension and dynamic for the submergences will be more dangerous in South regions of Ukraine. The risk control method of such processes needs implementation of economic measures firstly insurance strategies which provide decrease of special-purpose financing for overcoming of emergency subsequences. This analysis of biophysical and spectral parameters changes for the geosystems with the aim of ecological forecasting allowed: — to estimate risks of flooded and submergence processes; — to assess and forecast the quality of surface waters of the Western Bug under conditions of emergency situations of natural origin; — to forecast risks of bioproductivity loses for the landscapes to 2025.

Do quantitative decadal forecasts from GCMs provide decision relevant skill?

NASA Astrophysics Data System (ADS)

Suckling, E. B.; Smith, L. A.

2012-04-01

It is widely held that only physics-based simulation models can capture the dynamics required to provide decision-relevant probabilistic climate predictions. This fact in itself provides no evidence that predictions from today's GCMs are fit for purpose. Empirical (data-based) models are employed to make probability forecasts on decadal timescales, where it is argued that these 'physics free' forecasts provide a quantitative 'zero skill' target for the evaluation of forecasts based on more complicated models. It is demonstrated that these zero skill models are competitive with GCMs on decadal scales for probability forecasts evaluated over the last 50 years. Complications of statistical interpretation due to the 'hindcast' nature of this experiment, and the likely relevance of arguments that the lack of hindcast skill is irrelevant as the signal will soon 'come out of the noise' are discussed. A lack of decision relevant quantiative skill does not bring the science-based insights of anthropogenic warming into doubt, but it does call for a clear quantification of limits, as a function of lead time, for spatial and temporal scales on which decisions based on such model output are expected to prove maladaptive. Failing to do so may risk the credibility of science in support of policy in the long term. The performance amongst a collection of simulation models is evaluated, having transformed ensembles of point forecasts into probability distributions through the kernel dressing procedure [1], according to a selection of proper skill scores [2] and contrasted with purely data-based empirical models. Data-based models are unlikely to yield realistic forecasts for future climate change if the Earth system moves away from the conditions observed in the past, upon which the models are constructed; in this sense the empirical model defines zero skill. When should a decision relevant simulation model be expected to significantly outperform such empirical models? Probability forecasts up to ten years ahead (decadal forecasts) are considered, both on global and regional spatial scales for surface air temperature. Such decadal forecasts are not only important in terms of providing information on the impacts of near-term climate change, but also from the perspective of climate model validation, as hindcast experiments and a sufficient database of historical observations allow standard forecast verification methods to be used. Simulation models from the ENSEMBLES hindcast experiment [3] are evaluated and contrasted with static forecasts of the observed climatology, persistence forecasts and against simple statistical models, called dynamic climatology (DC). It is argued that DC is a more apropriate benchmark in the case of a non-stationary climate. It is found that the ENSEMBLES models do not demonstrate a significant increase in skill relative to the empirical models even at global scales over any lead time up to a decade ahead. It is suggested that the contsruction and co-evaluation with the data-based models become a regular component of the reporting of large simulation model forecasts. The methodology presented may easily be adapted to other forecasting experiments and is expected to influence the design of future experiments. The inclusion of comparisons with dynamic climatology and other data-based approaches provide important information to both scientists and decision makers on which aspects of state-of-the-art simulation forecasts are likely to be fit for purpose. [1] J. Bröcker and L. A. Smith. From ensemble forecasts to predictive distributions, Tellus A, 60(4), 663-678 (2007). [2] J. Bröcker and L. A. Smith. Scoring probabilistic forecasts: The importance of being proper, Weather and Forecasting, 22, 382-388 (2006). [3] F. J. Doblas-Reyes, A. Weisheimer, T. N. Palmer, J. M. Murphy and D. Smith. Forecast quality asessment of the ENSEMBLES seasonal-to-decadal stream 2 hindcasts, ECMWF Technical Memorandum, 621 (2010).

Effects of Forecasted Climate Change on Stream Temperatures in the Nooksack River Basin

NASA Astrophysics Data System (ADS)

Truitt, S. E.; Mitchell, R. J.; Yearsley, J. R.; Grah, O. J.

2017-12-01

The Nooksack River in northwest Washington State provides valuable habitat for endangered salmon species, as such it is critical to understand how stream temperatures will be affected by forecasted climate change. The Middle and North Forks basins of the Nooksack are high-relief and glaciated, whereas the South Fork is a lower relief rain and snow dominated basin. Due to a moderate Pacific maritime climate, snowpack in the basins is sensitive to temperature increases. Previous modeling studies in the upper Nooksack basins indicate a reduction in snowpack and spring runoff, and a recession of glaciers into the 21st century. How stream temperatures will respond to these changes is unknown. We use the Distributed Hydrology Soil Vegetation Model (DHSVM) coupled with a glacier dynamics model and the River Basin Model (RBM) to simulate hydrology and stream temperature from present to the year 2100. We calibrate the DHSVM and RBM to the three forks in the upper 1550 km2 of the Nooksack basin, which contain an estimated 3400 hectares of glacial ice. We employ observed stream-temperature data collected over the past decade and hydrologic data from the four USGS streamflow monitoring sites within the basin and observed gridded climate data developed by Linveh et al. (2013). Field work was conducted in the summer of 2016 to determine stream morphology, discharge, and stream temperatures at a number of stream segments for the RBM calibration. We simulate forecast climate change impacts, using gridded daily downscaled data from global climate models of the CMIP5 with RCP4.5 and RCP8.5 forcing scenarios developed using the multivariate adaptive constructed analogs method (MACA; Abatzoglou and Brown, 2011). Simulation results project a trending increase in stream temperature as a result of lower snowmelt and higher air temperatures into the 21st century, especially in the lower relief, unglaciated South Fork basin.

Climate Central World Weather Attribution (WWA) project: Real-time extreme weather event attribution analysis

NASA Astrophysics Data System (ADS)

Haustein, Karsten; Otto, Friederike; Uhe, Peter; Allen, Myles; Cullen, Heidi

2015-04-01

Extreme weather detection and attribution analysis has emerged as a core theme in climate science over the last decade or so. By using a combination of observational data and climate models it is possible to identify the role of climate change in certain types of extreme weather events such as sea level rise and its contribution to storm surges, extreme heat events and droughts or heavy rainfall and flood events. These analyses are usually carried out after an extreme event has occurred when reanalysis and observational data become available. The Climate Central WWA project will exploit the increasing forecast skill of seasonal forecast prediction systems such as the UK MetOffice GloSea5 (Global seasonal forecasting system) ensemble forecasting method. This way, the current weather can be fed into climate models to simulate large ensembles of possible weather scenarios before an event has fully emerged yet. This effort runs along parallel and intersecting tracks of science and communications that involve research, message development and testing, staged socialization of attribution science with key audiences, and dissemination. The method we employ uses a very large ensemble of simulations of regional climate models to run two different analyses: one to represent the current climate as it was observed, and one to represent the same events in the world that might have been without human-induced climate change. For the weather "as observed" experiment, the atmospheric model uses observed sea surface temperature (SST) data from GloSea5 (currently) and present-day atmospheric gas concentrations to simulate weather events that are possible given the observed climate conditions. The weather in the "world that might have been" experiments is obtained by removing the anthropogenic forcing from the observed SSTs, thereby simulating a counterfactual world without human activity. The anthropogenic forcing is obtained by comparing the CMIP5 historical and natural simulations from a variety of CMIP5 model ensembles. Here, we present results for the UK 2013/14 winter floods as proof of concept and we show validation and testing results that demonstrate the robustness of our method. We also revisit the record temperatures over Europe in 2014 and present a detailed analysis of this attribution exercise as it is one of the events to demonstrate that we can make a sensible statement of how the odds for such a year to occur have changed while it still unfolds.

Climate services in the tourism sector - examples and market research

NASA Astrophysics Data System (ADS)

Damm, Andrea; Köberl, Judith; Prettenthaler, Franz; Kortschak, Dominik; Hofer, Marianne; Winkler, Claudia

2017-04-01

Tourism is one of the most weather-sensitive sectors. Hence, dealing with weather and climate risks is an important part of operational risk management. WEDDA® (WEather Driven Demand Analysis), developed by Joanneum Research, represents a comprehensive and flexible toolbox for managing weather and climate risks. Modelling the demand for products or services of a particular economic sector or company and its weather and climate sensitivity usually forms the starting and central point of WEDDA®. Coupling the calibrated demand models to either long-term climate scenarios or short-term weather forecasts enables the use of WEDDA® for the following areas of application: (i) implementing short-term forecasting systems for the prediction of the considered indicator; (ii) quantifying the weather risk of a particular economic sector or company using parameters from finance (e.g. Value-at-Risk); (iii) assessing the potential impacts of changing climatic conditions on a particular economic sector or company. WEDDA® for short-term forecasts on the demand for products or services is currently used by various tourism businesses, such as open-air swimming pools, ski areas, and restaurants. It supports tourism and recreation facilities to better cope with (increasing) weather variability by optimizing the disposability of staff, resources and merchandise according to expected demand. Since coping with increasing weather variability forms one of the challenges with respect to climate change, WEDDA® may become an important component within a whole pool of weather and climate services designed to support tourism and recreation facilities to adapt to climate change. Climate change impact assessments at European scale, as conducted in the EU-FP7 project IMPACT2C, provide basic information of climate change impacts on tourism demand not only for individual tourism businesses, but also for regional and national tourism planners and policy makers interested in benchmarks for the vulnerability of their tourism destination. In this project we analysed the impacts of +2 °C global warming on winter tourism demand in ski tourism related regions in Europe. In order to achieve the climate targets, tailored climate information services - for individual businesses as well as at the regional and national level - play an important role. The current market, however, is still in the early stages. In the ongoing H2020 projects EU-MACS (www.eu-macs.eu) and MARCO (www.marco-h2020.eu) (Nov 2016 - Oct 2018) Joanneum Research explores the climate services market in the tourism sector. The current use of climate services is reviewed in detail and in an interactive process key market barriers and enablers will be identified in close collaboration with stakeholders from the tourism industry. The analysis and co-development of new climate services concepts for the tourism sector aims to reduce the gaps between climate services supply and demand.

Climate forecasting services: coming down from the ivory tower

NASA Astrophysics Data System (ADS)

Doblas-Reyes, F. J.; Caron, L. P.; Cortesi, N.; Soret, A.; Torralba, V.; Turco, M.; González Reviriego, N.; Jiménez, I.; Terrado, M.

2016-12-01

Subseasonal-to-seasonal (S2S) climate forecasts are increasingly used across a range of application areas (energy, water management, agriculture, health, insurance) through tailored services using the climate services paradigm. In this contribution we show the value of climate forecasting services through several examples of their application in the energy, reinsurance and agriculture sectors. Climate services aim at making climate information action oriented. In a climate forecasting context the task starts with the identification of climate variables, thresholds and events relevant to the users. These elements are then analysed to determine whether they can be both reliably and skilfully predicted at appropriate time scales. In this contribution we assess climate predictions of precipitation, temperature and wind indices from state-of-the-art operational multi-model forecast systems and if they respond to the expectations and requests from a range of users. This requires going beyond the more traditional assessment of monthly mean values to include assessments of global forecast quality of the frequency of warm, cold, windy and wet extremes (e.g. [1], [2]), as well as of using tools like the Euro-Atlantic weather regimes [3]. The forecast quality of extremes is generally similar to or slightly lower than that of monthly or seasonal averages, but offers a kind of information closer to what some users require. In addition to considering local climate variables, we also explore the use of large-scale climate indices, such as ENSO and NAO, that are associated with large regional synchronous variations of wind or tropical storm frequency. These indices help illustrating the relative merits of climate forecast information to users and are the cornerstone of climate stories that engage them in the co-production of climate information. [1] Doblas-Reyes et al, WIREs, 2013 [2] Pepler et al, Weather and Climate Extremes, 2015 [3] Pavan and Doblas-Reyes, Clim Dyn, 2013

Project Ukko - Design of a climate service visualisation interface for seasonal wind forecasts

NASA Astrophysics Data System (ADS)

Hemment, Drew; Stefaner, Moritz; Makri, Stephann; Buontempo, Carlo; Christel, Isadora; Torralba-Fernandez, Veronica; Gonzalez-Reviriego, Nube; Doblas-Reyes, Francisco; de Matos, Paula; Dykes, Jason

2016-04-01

Project Ukko is a prototype climate service to visually communicate probabilistic seasonal wind forecasts for the energy sector. In Project Ukko, an interactive visualisation enhances the accessibility and readability to the latests advances in seasonal wind speed predictions developed as part of the RESILIENCE prototype of the EUPORIAS (EC FP7) project. Climate services provide made-to-measure climate information, tailored to the specific requirements of different users and industries. In the wind energy sector, understanding of wind conditions in the next few months has high economic value, for instance, for the energy traders. Current energy practices use retrospective climatology, but access to reliable seasonal predictions based in the recent advances in global climate models has potential to improve their resilience to climate variability and change. Despite their potential benefits, a barrier to the development of commercially viable services is the complexity of the probabilistic forecast information, and the challenge of communicating complex and uncertain information to decision makers in industry. Project Ukko consists of an interactive climate service interface for wind energy users to explore probabilistic wind speed predictions for the coming season. This interface enables fast visual detection and exploration of interesting features and regions likely to experience unusual changes in wind speed in the coming months.The aim is not only to support users to better understand the future variability in wind power resources, but also to bridge the gap between practitioners' traditional approach and the advanced prediction systems developed by the climate science community. Project Ukko is presented as a case study of cross-disciplinary collaboration between climate science and design, for the development of climate services that are useful, usable and effective for industry users. The presentation will reflect on the challenge of developing a climate service for industry users in the wind energy sector, the background to this challenge, our approach, and the evaluation of the visualisation interface.

Improving the forecast for biodiversity under climate change.

PubMed

Urban, M C; Bocedi, G; Hendry, A P; Mihoub, J-B; Pe'er, G; Singer, A; Bridle, J R; Crozier, L G; De Meester, L; Godsoe, W; Gonzalez, A; Hellmann, J J; Holt, R D; Huth, A; Johst, K; Krug, C B; Leadley, P W; Palmer, S C F; Pantel, J H; Schmitz, A; Zollner, P A; Travis, J M J

2016-09-09

New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species' responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity. Copyright © 2016, American Association for the Advancement of Science.

The value of model averaging and dynamical climate model predictions for improving statistical seasonal streamflow forecasts over Australia

NASA Astrophysics Data System (ADS)

Pokhrel, Prafulla; Wang, Q. J.; Robertson, David E.

2013-10-01

Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the "best" model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

Phenological cues intrinsic in indigenous knowledge systems for forecasting seasonal climate in the Delta State of Nigeria

NASA Astrophysics Data System (ADS)

Fitchett, Jennifer M.; Ebhuoma, Eromose

2018-06-01

Shifts in the timing of phenological events in plants and animals are cited as one of the most robust bioindicators of climate change. Much effort has thus been placed on the collection of phenological datasets, the quantification of the rates of phenological shifts and the association of these shifts with recorded meteorological data. These outputs are of value both in tracking the severity of climate change and in facilitating more robust management approaches in forestry and agriculture to changing climatic conditions. However, such an approach requires meteorological and phenological records spanning multiple decades. For communities in the Delta State of Nigeria, small-scale farming communities do not have access to meteorological records, and the dissemination of government issued daily to seasonal forecasts has only taken place in recent years. Their ability to survive inter-annual to inter-decadal climatic variability and longer-term climatic change has thus relied on well-entrenched indigenous knowledge systems (IKS). An analysis of the environmental cues that are used to infer the timing and amount of rainfall by farmers from three communities in the Delta State reveals a reliance on phenological events, including the croaking of frogs, the appearance of red millipedes and the emergence of fresh rubber tree and cassava leaves. These represent the first recorded awareness of phenology within the Delta State of Nigeria, and a potentially valuable source of phenological data. However, the reliance of these indicators is of concern given the rapid phenological shifts occurring in response to climate change.

Phenological cues intrinsic in indigenous knowledge systems for forecasting seasonal climate in the Delta State of Nigeria

NASA Astrophysics Data System (ADS)

Fitchett, Jennifer M.; Ebhuoma, Eromose

2017-12-01

Shifts in the timing of phenological events in plants and animals are cited as one of the most robust bioindicators of climate change. Much effort has thus been placed on the collection of phenological datasets, the quantification of the rates of phenological shifts and the association of these shifts with recorded meteorological data. These outputs are of value both in tracking the severity of climate change and in facilitating more robust management approaches in forestry and agriculture to changing climatic conditions. However, such an approach requires meteorological and phenological records spanning multiple decades. For communities in the Delta State of Nigeria, small-scale farming communities do not have access to meteorological records, and the dissemination of government issued daily to seasonal forecasts has only taken place in recent years. Their ability to survive inter-annual to inter-decadal climatic variability and longer-term climatic change has thus relied on well-entrenched indigenous knowledge systems (IKS). An analysis of the environmental cues that are used to infer the timing and amount of rainfall by farmers from three communities in the Delta State reveals a reliance on phenological events, including the croaking of frogs, the appearance of red millipedes and the emergence of fresh rubber tree and cassava leaves. These represent the first recorded awareness of phenology within the Delta State of Nigeria, and a potentially valuable source of phenological data. However, the reliance of these indicators is of concern given the rapid phenological shifts occurring in response to climate change.

Phenological cues intrinsic in indigenous knowledge systems for forecasting seasonal climate in the Delta State of Nigeria.

PubMed

Fitchett, Jennifer M; Ebhuoma, Eromose

2018-06-01

Shifts in the timing of phenological events in plants and animals are cited as one of the most robust bioindicators of climate change. Much effort has thus been placed on the collection of phenological datasets, the quantification of the rates of phenological shifts and the association of these shifts with recorded meteorological data. These outputs are of value both in tracking the severity of climate change and in facilitating more robust management approaches in forestry and agriculture to changing climatic conditions. However, such an approach requires meteorological and phenological records spanning multiple decades. For communities in the Delta State of Nigeria, small-scale farming communities do not have access to meteorological records, and the dissemination of government issued daily to seasonal forecasts has only taken place in recent years. Their ability to survive inter-annual to inter-decadal climatic variability and longer-term climatic change has thus relied on well-entrenched indigenous knowledge systems (IKS). An analysis of the environmental cues that are used to infer the timing and amount of rainfall by farmers from three communities in the Delta State reveals a reliance on phenological events, including the croaking of frogs, the appearance of red millipedes and the emergence of fresh rubber tree and cassava leaves. These represent the first recorded awareness of phenology within the Delta State of Nigeria, and a potentially valuable source of phenological data. However, the reliance of these indicators is of concern given the rapid phenological shifts occurring in response to climate change.

Assessment of climate change impacts on climate variables using probabilistic ensemble modeling and trend analysis

NASA Astrophysics Data System (ADS)

Safavi, Hamid R.; Sajjadi, Sayed Mahdi; Raghibi, Vahid

2017-10-01

Water resources in snow-dependent regions have undergone significant changes due to climate change. Snow measurements in these regions have revealed alarming declines in snowfall over the past few years. The Zayandeh-Rud River in central Iran chiefly depends on winter falls as snow for supplying water from wet regions in high Zagrous Mountains to the downstream, (semi-)arid, low-lying lands. In this study, the historical records (baseline: 1971-2000) of climate variables (temperature and precipitation) in the wet region were chosen to construct a probabilistic ensemble model using 15 GCMs in order to forecast future trends and changes while the Long Ashton Research Station Weather Generator (LARS-WG) was utilized to project climate variables under two A2 and B1 scenarios to a future period (2015-2044). Since future snow water equivalent (SWE) forecasts by GCMs were not available for the study area, an artificial neural network (ANN) was implemented to build a relationship between climate variables and snow water equivalent for the baseline period to estimate future snowfall amounts. As a last step, homogeneity and trend tests were performed to evaluate the robustness of the data series and changes were examined to detect past and future variations. Results indicate different characteristics of the climate variables at upstream stations. A shift is observed in the type of precipitation from snow to rain as well as in its quantities across the subregions. The key role in these shifts and the subsequent side effects such as water losses is played by temperature.

Long-Term Climate Forcing in Loggerhead Sea Turtle Nesting

PubMed Central

Van Houtan, Kyle S.; Halley, John M.

2011-01-01

The long-term variability of marine turtle populations remains poorly understood, limiting science and management. Here we use basin-scale climate indices and regional surface temperatures to estimate loggerhead sea turtle (Caretta caretta) nesting at a variety of spatial and temporal scales. Borrowing from fisheries research, our models investigate how oceanographic processes influence juvenile recruitment and regulate population dynamics. This novel approach finds local populations in the North Pacific and Northwest Atlantic are regionally synchronized and strongly correlated to ocean conditions—such that climate models alone explain up to 88% of the observed changes over the past several decades. In addition to its performance, climate-based modeling also provides mechanistic forecasts of historical and future population changes. Hindcasts in both regions indicate climatic conditions may have been a factor in recent declines, but future forecasts are mixed. Available climatic data suggests the Pacific population will be significantly reduced by 2040, but indicates the Atlantic population may increase substantially. These results do not exonerate anthropogenic impacts, but highlight the significance of bottom-up oceanographic processes to marine organisms. Future studies should consider environmental baselines in assessments of marine turtle population variability and persistence. PMID:21589639

Long-term climate forcing in loggerhead sea turtle nesting.

PubMed

Van Houtan, Kyle S; Halley, John M

2011-04-27

The long-term variability of marine turtle populations remains poorly understood, limiting science and management. Here we use basin-scale climate indices and regional surface temperatures to estimate loggerhead sea turtle (Caretta caretta) nesting at a variety of spatial and temporal scales. Borrowing from fisheries research, our models investigate how oceanographic processes influence juvenile recruitment and regulate population dynamics. This novel approach finds local populations in the North Pacific and Northwest Atlantic are regionally synchronized and strongly correlated to ocean conditions--such that climate models alone explain up to 88% of the observed changes over the past several decades. In addition to its performance, climate-based modeling also provides mechanistic forecasts of historical and future population changes. Hindcasts in both regions indicate climatic conditions may have been a factor in recent declines, but future forecasts are mixed. Available climatic data suggests the Pacific population will be significantly reduced by 2040, but indicates the Atlantic population may increase substantially. These results do not exonerate anthropogenic impacts, but highlight the significance of bottom-up oceanographic processes to marine organisms. Future studies should consider environmental baselines in assessments of marine turtle population variability and persistence.

Modelling both dominance and species distribution provides a more complete picture of changes to mangrove ecosystems under climate change.

PubMed

Crase, Beth; Vesk, Peter A; Liedloff, Adam; Wintle, Brendan A

2015-08-01

Dominant species influence the composition and abundance of other species present in ecosystems. However, forecasts of distributional change under future climates have predominantly focused on changes in species distribution and ignored possible changes in spatial and temporal patterns of dominance. We develop forecasts of spatial changes for the distribution of species dominance, defined in terms of basal area, and for species occurrence, in response to sea level rise for three tree taxa within an extensive mangrove ecosystem in northern Australia. Three new metrics are provided, indicating the area expected to be suitable under future conditions (Eoccupied ), the instability of suitable area (Einstability ) and the overlap between the current and future spatial distribution (Eoverlap ). The current dominance and occurrence were modelled in relation to a set of environmental variables using boosted regression tree (BRT) models, under two scenarios of seedling establishment: unrestricted and highly restricted. While forecasts of spatial change were qualitatively similar for species occurrence and dominance, the models of species dominance exhibited higher metrics of model fit and predictive performance, and the spatial pattern of future dominance was less similar to the current pattern than was the case for the distributions of species occurrence. This highlights the possibility of greater changes in the spatial patterning of mangrove tree species dominance under future sea level rise. Under the restricted seedling establishment scenario, the area occupied by or dominated by a species declined between 42.1% and 93.8%, while for unrestricted seedling establishment, the area suitable for dominance or occurrence of each species varied from a decline of 68.4% to an expansion of 99.5%. As changes in the spatial patterning of dominance are likely to cause a cascade of effects throughout the ecosystem, forecasting spatial changes in dominance provides new and complementary information in addition to that provided by forecasts of species occurrence. © 2015 John Wiley & Sons Ltd.

Flow regime, temperature, and biotic interactions drive differential declines of trout species under climate change [includes Supporting Information

Treesearch

Seth J. Wenger; Daniel J. Isaak; Charlie Luce; Helen M. Neville; Kurt D. Fausch; Jason B. Dunham; Daniel C. Dauwalter; Michael K. Young; Marketa M. Elsner; Bruce E. Rieman; Alan F. Hamlet; Jack E. Williams

2011-01-01

Broad-scale studies of climate change effects on freshwater species have focused mainly on temperature, ignoring critical drivers such as flow regime and biotic interactions. We use downscaled outputs from general circulation models coupled with a hydrologic model to forecast the effects of altered flows and increased temperatures on four interacting species of trout...

Climate forecasts for corn producer decision making

USDA-ARS?s Scientific Manuscript database

Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertai...

Time series modelling of global mean temperature for managerial decision-making.

PubMed

Romilly, Peter

2005-07-01

Climate change has important implications for business and economic activity. Effective management of climate change impacts will depend on the availability of accurate and cost-effective forecasts. This paper uses univariate time series techniques to model the properties of a global mean temperature dataset in order to develop a parsimonious forecasting model for managerial decision-making over the short-term horizon. Although the model is estimated on global temperature data, the methodology could also be applied to temperature data at more localised levels. The statistical techniques include seasonal and non-seasonal unit root testing with and without structural breaks, as well as ARIMA and GARCH modelling. A forecasting evaluation shows that the chosen model performs well against rival models. The estimation results confirm the findings of a number of previous studies, namely that global mean temperatures increased significantly throughout the 20th century. The use of GARCH modelling also shows the presence of volatility clustering in the temperature data, and a positive association between volatility and global mean temperature.

Third National Aeronautics and Space Administration Weather and climate program science review

NASA Technical Reports Server (NTRS)

Kreins, E. R. (Editor)

1977-01-01

Research results of developing experimental and prototype operational systems, sensors, and space facilities for monitoring, and understanding the atmosphere are reported. Major aspects include: (1) detection, monitoring, and prediction of severe storms; (2) improvement of global forecasting; and (3) monitoring and prediction of climate change.

Scaling Properties and Spatial Interpolation of Soil Moisture

DTIC Science & Technology

2004-08-24

the sensitivities is useful not only for characterizing soil moisture but also for forecasting the vulnerability of a region’s water cycle to climate...regional water balance was presented that can be used to assess the impact of climatic fluctuations and changes on the water cycle of a region. In

Controlling vector-borne disease and adapting to climate change with novel research on disease forecasting to target new vector control materials and technologies

USDA-ARS?s Scientific Manuscript database

Population growth, frontier agricultural expansion, and urbanization transform the landscape and the surrounding ecosystem, affecting climate and interactions between animals and humans, and significantly influencing the transmission dynamics and geographic distribution of malaria, dengue and other ...

Reducing Our Carbon Footprint: Frontiers in Climate Forecasting (LBNL Science at the Theater)

ScienceCinema

Collins, Bill [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2018-06-07

Bill Collins directs Berkeley Lab's research dedicated to atmospheric and climate science. Previously, he headed the development of one of the leading climate models used in international studies of global warming. His work has confirmed that man-made greenhouse gases are probably the main culprits of recent warming and future warming poses very real challenges for the environment and society. A lead author of the most recent assessment of the science of climate change by the United Nations' Intergovernmental Panel on Climate Change, Collins wants to create a new kind of climate model, one that will integrate cutting-edge climate science with accurate predictions people can use to plan their lives

Using Climate Regionalization to Understand Climate Forecast System Version 2 (CFSv2) Precipitation Performance for the Conterminous United States (CONUS)

NASA Technical Reports Server (NTRS)

Regonda, Satish K.; Zaitchik, Benjamin F.; Badr, Hamada S.; Rodell, Matthew

2016-01-01

Dynamically based seasonal forecasts are prone to systematic spatial biases due to imperfections in the underlying global climate model (GCM). This can result in low-forecast skill when the GCM misplaces teleconnections or fails to resolve geographic barriers, even if the prediction of large-scale dynamics is accurate. To characterize and address this issue, this study applies objective climate regionalization to identify discrepancies between the Climate Forecast SystemVersion 2 (CFSv2) and precipitation observations across the Contiguous United States (CONUS). Regionalization shows that CFSv2 1 month forecasts capture the general spatial character of warm season precipitation variability but that forecast regions systematically differ from observation in some transition zones. CFSv2 predictive skill for these misclassified areas is systematically reduced relative to correctly regionalized areas and CONUS as a whole. In these incorrectly regionalized areas, higher skill can be obtained by using a regional-scale forecast in place of the local grid cell prediction.

Mesocosms Reveal Ecological Surprises from Climate Change.

PubMed

Fordham, Damien A

2015-12-01

Understanding, predicting, and mitigating the impacts of climate change on biodiversity poses one of the most crucial challenges this century. Currently, we know more about how future climates are likely to shift across the globe than about how species will respond to these changes. Two recent studies show how mesocosm experiments can hasten understanding of the ecological consequences of climate change on species' extinction risk, community structure, and ecosystem functions. Using a large-scale terrestrial warming experiment, Bestion et al. provide the first direct evidence that future global warming can increase extinction risk for temperate ectotherms. Using aquatic mesocosms, Yvon-Durocher et al. show that human-induced climate change could, in some cases, actually enhance the diversity of local communities, increasing productivity. Blending these theoretical and empirical results with computational models will improve forecasts of biodiversity loss and altered ecosystem processes due to climate change.

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.

Utilizing Climate Forecasts for Improving Water and Power Systems Coordination

NASA Astrophysics Data System (ADS)

Arumugam, S.; Queiroz, A.; Patskoski, J.; Mahinthakumar, K.; DeCarolis, J.

2016-12-01

Climate forecasts, typically monthly-to-seasonal precipitation forecasts, are commonly used to develop streamflow forecasts for improving reservoir management. Irrespective of their high skill in forecasting, temperature forecasts in developing power demand forecasts are not often considered along with streamflow forecasts for improving water and power systems coordination. In this study, we consider a prototype system to analyze the utility of climate forecasts, both precipitation and temperature, for improving water and power systems coordination. The prototype system, a unit-commitment model that schedules power generation from various sources, is considered and its performance is compared with an energy system model having an equivalent reservoir representation. Different skill sets of streamflow forecasts and power demand forecasts are forced on both water and power systems representations for understanding the level of model complexity required for utilizing monthly-to-seasonal climate forecasts to improve coordination between these two systems. The analyses also identify various decision-making strategies - forward purchasing of fuel stocks, scheduled maintenance of various power systems and tradeoff on water appropriation between hydropower and other uses - in the context of various water and power systems configurations. Potential application of such analyses for integrating large power systems with multiple river basins is also discussed.

Post-processing Seasonal Precipitation Forecasts via Integrating Climate Indices and the Analog Approach

NASA Astrophysics Data System (ADS)

Liu, Y.; Zhang, Y.; Wood, A.; Lee, H. S.; Wu, L.; Schaake, J. C.

2016-12-01

Seasonal precipitation forecasts are a primary driver for seasonal streamflow prediction that is critical for a range of water resources applications, such as reservoir operations and drought management. However, it is well known that seasonal precipitation forecasts from climate models are often biased and also too coarse in spatial resolution for hydrologic applications. Therefore, post-processing procedures such as downscaling and bias correction are often needed. In this presentation, we discuss results from a recent study that applies a two-step methodology to downscale and correct the ensemble mean precipitation forecasts from the Climate Forecast System (CFS). First, CFS forecasts are downscaled and bias corrected using monthly reforecast analogs: we identify past precipitation forecasts that are similar to the current forecast, and then use the finer-scale observational analysis fields from the corresponding dates to represent the post-processed ensemble forecasts. Second, we construct the posterior distribution of forecast precipitation from the post-processed ensemble by integrating climate indices: a correlation analysis is performed to identify dominant climate indices for the study region, which are then used to weight the analysis analogs selected in the first step using a Bayesian approach. The methodology is applied to the California Nevada River Forecast Center (CNRFC) and the Middle Atlantic River Forecast Center (MARFC) regions for 1982-2015, using the North American Land Data Assimilation System (NLDAS-2) precipitation as the analysis. The results from cross validation show that the post-processed CFS precipitation forecast are considerably more skillful than the raw CFS with the analog approach only. Integrating climate indices can further improve the skill if the number of ensemble members considered is large enough; however, the improvement is generally limited to the first couple of months when compared against climatology. Impacts of various factors such as ensemble size, lead time, and choice of climate indices will also be discussed.

Climate Change Impacts on Sediment Transport In a Lowland Watershed System: Controlling Processes and Projection

NASA Astrophysics Data System (ADS)

al Aamery, N. M. H.; Mahoney, D. T.; Fox, J.

2017-12-01

Future climate change projections suggest extreme impacts on watershed hydrologic systems for some regions of the world including pronounced increases in surface runoff and instream flows. Yet, there remains a lack of research focused on how future changes in hydrologic extremes, as well as relative hydrologic mean changes, impact sediment redistribution within a watershed and sediment flux from a watershed. The authors hypothesized that variations in mean and extreme changes in turn may impact sediments in depositional and erosional dominance in a manner that may not be obvious to the watershed manager. Therefore, the objectives of this study were to investigate the inner processes connecting the combined effect of extreme climate change projections on the vegetation, upland erosion, and instream processes to produce changes in sediment redistribution within watersheds. To do so, research methods were carried out by the authors including simulating sediment processes in forecast and hindcast periods for a lowland watershed system. Publically available climate realizations from several climate factors and the Soil Water Assessment Tool (SWAT) were used to predict hydrologic conditions for the South Elkhorn Watershed in central Kentucky, USA to 2050. The results of the simulated extreme and mean hydrological components were used in simulating upland erosion with the connectivity processes consideration and thereafter used in building and simulating the instream erosion and deposition of sediment processes with the consideration of surface fine grain lamina (SFGL) layer controlling the benthic ecosystem. Results are used to suggest the dominance of erosional and depositional redistribution of sediments under different scenarios associated with extreme and mean hydrologic forecasting. The results are discussed in reference to the benthic ecology of the stream system providing insight on how water managers might consider sediment redistribution in a changing climate.

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.

Climatic Forecasting of Net Infiltration at Yucca Mountain, Using Analogue Meteorological Data

NASA Astrophysics Data System (ADS)

Faybishenko, B.

2005-12-01

Net infiltration is a key hydrologic parameter that, throughout the unsaturated zone, controls the rate of deep percolation, the groundwater recharge, radionuclide transport, and seepage into underground tunnels. Because net infiltration is largely affected by climatic conditions, future changes in climatic conditions will potentially alter net infiltration. The objectives of this presentation are to: (1) Present a conceptual model and a semi-empirical approach for regional climatic forecasting of net infiltration, based on precipitation and temperature data from analogue meteorological stations; and (2) Demonstrate the results of forecasting net infiltration for future climates - interglacial, monsoon and glacial - over the Yucca Mountain region for a period of 500,000 years. Calculations of net infiltration were performed using a modified Budyko's water-balance model, and potential evapotranspiration was evaluated from the temperature-based Thornthwaite formula. (Both Budyko's and Thornthwaite's formulae have been used broadly in hydrological studies.) The results of these calculations were used for ranking net infiltration, along with aridity and precipitation-effectiveness (P-E) indices, for future climatic scenarios. Using this approach, we determined a general trend of increasing net infiltration from the present-day (interglacial) climate to the monsoon, intermediate (glacial transition) climate, a trend that continued into the glacial climate time frame. The ranking of aridity and P-E indices is practically the same as that for net infiltration. Validation of the computed net infiltration rates yielded a good match with other field and modeling study results related to groundwater recharge and net infiltration evaluation.

A stochastic multicloud convective parameterization in the NCEP Climate Forecast System (CFSv2) : implementation and calibration.

NASA Astrophysics Data System (ADS)

Goswami, B. B.; Khouider, B.; Krishna, R. P. M.; Mukhopadhyay, P.; Majda, A.

2017-12-01

A stochastic multicloud (SMCM) cumulus parameterization is implemented in the National Centres for Environmental Predictions (NCEP) Climate Forecast System version 2 (CFSv2) model, named as the CFSsmcm model. We present here results from a systematic attempt to understand the CFSsmcm model's sensitivity to the SMCM parameters. To asses the model-sentivity to the different SMCM parameters, we have analized a set of 14 5-year long climate simulations produced by the CFSsmcm model. The model is found to be resilient to minor changes in the parameter values. The middle tropospheric dryness (MTD) and the stratiform cloud decay timescale are found to be most crucial parameters in the SMCM formulation in the CFSsmcm model.

Monthly means of selected climate variables for 1985 - 1989

NASA Technical Reports Server (NTRS)

Schubert, S.; Wu, C.-Y.; Zero, J.; Schemm, J.-K.; Park, C.-K.; Suarez, M.

1992-01-01

Meteorologists are accustomed to viewing instantaneous weather maps, since these contain the most relevant information for the task of producing short-range weather forecasts. Climatologists, on the other hand, tend to deal with long-term means, which portray the average climate. The recent emphasis on dynamical extended-range forecasting and, in particular measuring and predicting short term climate change makes it important that we become accustomed to looking at variations on monthly and longer time scales. A convenient toll for researchers to familiarize themselves with the variability which occurs in selected parameters on these time scales is provided. The format of the document was chosen to help facilitate the intercomparison of various parameters and highlight the year-to-year variability in monthly means.

Visualization of uncertainties and forecast skill in user-tailored seasonal climate predictions for agriculture

NASA Astrophysics Data System (ADS)

Sedlmeier, Katrin; Gubler, Stefanie; Spierig, Christoph; Flubacher, Moritz; Maurer, Felix; Quevedo, Karim; Escajadillo, Yury; Avalos, Griña; Liniger, Mark A.; Schwierz, Cornelia

2017-04-01

Seasonal climate forecast products potentially have a high value for users of different sectors. During the first phase (2012-2015) of the project CLIMANDES (a pilot project of the Global Framework for Climate Services led by WMO [http://www.wmo.int/gfcs/climandes]), a demand study conducted with Peruvian farmers indicated a large interest in seasonal climate information for agriculture. The study further showed that the required information should by precise, timely, and understandable. In addition to the actual forecast, two complex measures are essential to understand seasonal climate predictions and their limitations correctly: forecast uncertainty and forecast skill. The former can be sampled by using an ensemble of climate simulations, the latter derived by comparing forecasts of past time periods to observations. Including uncertainty and skill information in an understandable way for end-users (who are often not technically educated) poses a great challenge. However, neglecting this information would lead to a false sense of determinism which could prove fatal to the credibility of climate information. Within the second phase (2016-2018) of the project CLIMANDES, one goal is to develop a prototype of a user-tailored seasonal forecast for the agricultural sector in Peru. In this local context, the basic education level of the rural farming community presents a major challenge for the communication of seasonal climate predictions. This contribution proposes different graphical presentations of climate forecasts along with possible approaches to visualize and communicate the associated skill and uncertainties, considering end users with varying levels of technical knowledge.

Development of a drought forecasting model for the Asia-Pacific region using remote sensing and climate data: Focusing on Indonesia

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Kim, Gayoung; Im, Jungho

2017-04-01

Three regions of Indonesia with different rainfall characteristics were chosen to develop drought forecast models based on machine learning. The 6-month Standardized Precipitation Index (SPI6) was selected as the target variable. The models' forecast skill was compared to the skill of long-range climate forecast models in terms of drought accuracy and regression mean absolute error (MAE). Indonesian droughts are known to be related to El Nino Southern Oscillation (ENSO) variability despite of regional differences as well as monsoon, local sea surface temperature (SST), other large-scale atmosphere-ocean interactions such as Indian Ocean Dipole (IOD) and Southern Pacific Convergence Zone (SPCZ), and local factors including topography and elevation. Machine learning models are thus to enhance drought forecast skill by combining local and remote SST and remote sensing information reflecting initial drought conditions to the long-range climate forecast model results. A total of 126 machine learning models were developed for the three regions of West Java (JB), West Sumatra (SB), and Gorontalo (GO) and six long-range climate forecast models of MSC_CanCM3, MSC_CanCM4, NCEP, NASA, PNU, POAMA as well as one climatology model based on remote sensing precipitation data, and 1 to 6-month lead times. When compared the results between the machine learning models and the long-range climate forecast models, West Java and Gorontalo regions showed similar characteristics in terms of drought accuracy. Drought accuracy of the long-range climate forecast models were generally higher than the machine learning models with short lead times but the opposite appeared for longer lead times. For West Sumatra, however, the machine learning models and the long-range climate forecast models showed similar drought accuracy. The machine learning models showed smaller regression errors for all three regions especially with longer lead times. Among the three regions, the machine learning models developed for Gorontalo showed the highest drought accuracy and the lowest regression error. West Java showed higher drought accuracy compared to West Sumatra, while West Sumatra showed lower regression error compared to West Java. The lower error in West Sumatra may be because of the smaller sample size used for training and evaluation for the region. Regional differences of forecast skill are determined by the effect of ENSO and the following forecast skill of the long-range climate forecast models. While shown somewhat high in West Sumatra, relative importance of remote sensing variables was mostly low in most cases. High importance of the variables based on long-range climate forecast models indicates that the forecast skill of the machine learning models are mostly determined by the forecast skill of the climate models.

Water quality observations of ice-covered, stagnant, eutrophic water bodies and analysis of influence of ice-covered period on water quality

NASA Astrophysics Data System (ADS)

sugihara, K.; Nakatsugawa, M.

2013-12-01

The water quality characteristics of ice-covered, stagnant, eutrophic water bodies have not been clarified because of insufficient observations. It has been pointed out that climate change has been shortening the duration of ice-cover; however, the influence of climate change on water quality has not been clarified. This study clarifies the water quality characteristics of stagnant, eutrophic water bodies that freeze in winter, based on our surveys and simulations, and examines how climate change may influence those characteristics. We made fixed-point observation using self-registering equipment and vertical water sampling. Self-registering equipment measured water temperature and dissolved oxygen(DO).vertical water sampling analyzed biological oxygen demand(BOD), total nitrogen(T-N), nitrate nitrogen(NO3-N), nitrite nitrogen(NO2-N), ammonium nitrogen(NH4-N), total phosphorus(TP), orthophosphoric phosphorus(PO4-P) and chlorophyll-a(Chl-a). The survey found that climate-change-related increases in water temperature were suppressed by ice covering the water area, which also blocked oxygen supply. It was also clarified that the bottom sediment consumed oxygen and turned the water layers anaerobic beginning from the bottom layer, and that nutrient salts eluted from the bottom sediment. The eluted nutrient salts were stored in the water body until the ice melted. The ice-covered period of water bodies has been shortening, a finding based on the analysis of weather and water quality data from 1998 to 2008. Climate change was surveyed as having caused decreases in nutrient salts concentration because of the shortened ice-covered period. However, BOD in spring showed a tendency to increase because of the proliferation of phytoplankton that was promoted by the climate-change-related increase in water temperature. To forecast the water quality by using these findings, particularly the influence of climate change, we constructed a water quality simulation model that incorporates the freezing-over of water bodies. The constructed model shows good temporal and spatial reproducibility and enables water quality to be forecast throughout the year, including during the ice-covered period. The forecasts using the model agree well with the survey results of shortened ice period and climate-change-related increase in the BOD in spring. From the result of calculations and observations, it is suggested that water quality of spring has been deteriorate because of freezing period to be shortened due to temperature rising.

A global empirical system for probabilistic seasonal climate prediction

NASA Astrophysics Data System (ADS)

Eden, J. M.; van Oldenborgh, G. J.; Hawkins, E.; Suckling, E. B.

2015-12-01

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961-2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño-Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

An empirical system for probabilistic seasonal climate prediction

NASA Astrophysics Data System (ADS)

Eden, Jonathan; van Oldenborgh, Geert Jan; Hawkins, Ed; Suckling, Emma

2016-04-01

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961-2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño-Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

Intraseasonal Variability in the Atmosphere-Ocean Climate System. Second Edition

NASA Technical Reports Server (NTRS)

Lau, William K. M.; Waliser, Duane E.

2011-01-01

Understanding and predicting the intraseasonal variability (ISV) of the ocean and atmosphere is crucial to improving long-range environmental forecasts and the reliability of climate change projections through climate models. This updated, comprehensive and authoritative second edition has a balance of observation, theory and modeling and provides a single source of reference for all those interested in this important multi-faceted natural phenomenon and its relation to major short-term climatic variations.

Forecasting the combined effects of urbanization and climate change on stream ecosystems: from impacts to management options

PubMed Central

Nelson, Kären C; Palmer, Margaret A; Pizzuto, James E; Moglen, Glenn E; Angermeier, Paul L; Hilderbrand, Robert H; Dettinger, Michael; Hayhoe, Katharine

2009-01-01

Streams collect runoff, heat, and sediment from their watersheds, making them highly vulnerable to anthropogenic disturbances such as urbanization and climate change. Forecasting the effects of these disturbances using process-based models is critical to identifying the form and magnitude of likely impacts. Here, we integrate a new biotic model with four previously developed physical models (downscaled climate projections, stream hydrology, geomorphology, and water temperature) to predict how stream fish growth and reproduction will most probably respond to shifts in climate and urbanization over the next several decades. The biotic submodel couples dynamics in fish populations and habitat suitability to predict fish assemblage composition, based on readily available biotic information (preferences for habitat, temperature, and food, and characteristics of spawning) and day-to-day variability in stream conditions. We illustrate the model using Piedmont headwater streams in the Chesapeake Bay watershed of the USA, projecting ten scenarios: Baseline (low urbanization; no on-going construction; and present-day climate); one Urbanization scenario (higher impervious surface, lower forest cover, significant construction activity); four future climate change scenarios [Hadley CM3 and Parallel Climate Models under medium-high (A2) and medium-low (B2) emissions scenarios]; and the same four climate change scenarios plus Urbanization. Urbanization alone depressed growth or reproduction of 8 of 39 species, while climate change alone depressed 22 to 29 species. Almost every recreationally important species (i.e. trouts, basses, sunfishes) and six of the ten currently most common species were predicted to be significantly stressed. The combined effect of climate change and urbanization on adult growth was sometimes large compared to the effect of either stressor alone. Thus, the model predicts considerable change in fish assemblage composition, including loss of diversity. Synthesis and applications. The interaction of climate change and urban growth may entail significant reconfiguring of headwater streams, including a loss of ecosystem structure and services, which will be more costly than climate change alone. On local scales, stakeholders cannot control climate drivers but they can mitigate stream impacts via careful land use. Therefore, to conserve stream ecosystems, we recommend that proactive measures be taken to insure against species loss or severe population declines. Delays will inevitably exacerbate the impacts of both climate change and urbanization on headwater systems. PMID:19536343

Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: Constrained forecast with data assimilation: Forecast With Data Assimilation

DOE PAGES

Huang, Yuanyuan; Jiang, Jiang; Ma, Shuang; ...

2017-08-18

We report that accurate simulation of soil thermal dynamics is essential for realistic prediction of soil biogeochemical responses to climate change. To facilitate ecological forecasting at the Spruce and Peatland Responses Under Climatic and Environmental change site, we incorporated a soil temperature module into a Terrestrial ECOsystem (TECO) model by accounting for surface energy budget, snow dynamics, and heat transfer among soil layers and during freeze-thaw events. We conditioned TECO with detailed soil temperature and snow depth observations through data assimilation before the model was used for forecasting. The constrained model reproduced variations in observed temperature from different soil layers,more » the magnitude of snow depth, the timing of snowfall and snowmelt, and the range of frozen depth. The conditioned TECO forecasted probabilistic distributions of soil temperature dynamics in six soil layers, snow, and frozen depths under temperature treatments of +0.0, +2.25, +4.5, +6.75, and +9.0°C. Air warming caused stronger elevation in soil temperature during summer than winter due to winter snow and ice. And soil temperature increased more in shallow soil layers in summer in response to air warming. Whole ecosystem warming (peat + air warmings) generally reduced snow and frozen depths. The accuracy of forecasted snow and frozen depths relied on the precision of weather forcing. Uncertainty is smaller for forecasting soil temperature but large for snow and frozen depths. Lastly, timely and effective soil thermal forecast, constrained through data assimilation that combines process-based understanding and detailed observations, provides boundary conditions for better predictions of future biogeochemical cycles.« less

Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: Constrained forecast with data assimilation: Forecast With Data Assimilation

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

Huang, Yuanyuan; Jiang, Jiang; Ma, Shuang

We report that accurate simulation of soil thermal dynamics is essential for realistic prediction of soil biogeochemical responses to climate change. To facilitate ecological forecasting at the Spruce and Peatland Responses Under Climatic and Environmental change site, we incorporated a soil temperature module into a Terrestrial ECOsystem (TECO) model by accounting for surface energy budget, snow dynamics, and heat transfer among soil layers and during freeze-thaw events. We conditioned TECO with detailed soil temperature and snow depth observations through data assimilation before the model was used for forecasting. The constrained model reproduced variations in observed temperature from different soil layers,more » the magnitude of snow depth, the timing of snowfall and snowmelt, and the range of frozen depth. The conditioned TECO forecasted probabilistic distributions of soil temperature dynamics in six soil layers, snow, and frozen depths under temperature treatments of +0.0, +2.25, +4.5, +6.75, and +9.0°C. Air warming caused stronger elevation in soil temperature during summer than winter due to winter snow and ice. And soil temperature increased more in shallow soil layers in summer in response to air warming. Whole ecosystem warming (peat + air warmings) generally reduced snow and frozen depths. The accuracy of forecasted snow and frozen depths relied on the precision of weather forcing. Uncertainty is smaller for forecasting soil temperature but large for snow and frozen depths. Lastly, timely and effective soil thermal forecast, constrained through data assimilation that combines process-based understanding and detailed observations, provides boundary conditions for better predictions of future biogeochemical cycles.« less

Global change and terrestrial plant community dynamics

DOE PAGES

Franklin, Janet; Serra-Diaz, Josep M.; Syphard, Alexandra D.; ...

2016-02-29

Anthropogenic drivers of global change include rising atmospheric concentrations of carbon dioxide and other greenhouse gasses and resulting changes in the climate, as well as nitrogen deposition, biotic invasions, altered disturbance regimes, and land-use change. Predicting the effects of global change on terrestrial plant communities is crucial because of the ecosystem services vegetation provides, from climate regulation to forest products. In this article, we present a framework for detecting vegetation changes and attributing them to global change drivers that incorporates multiple lines of evidence from spatially extensive monitoring networks, distributed experiments, remotely sensed data, and historical records. Based on amore » literature review, we summarize observed changes and then describe modeling tools that can forecast the impacts of multiple drivers on plant communities in an era of rapid change. Observed responses to changes in temperature, water, nutrients, land use, and disturbance show strong sensitivity of ecosystem productivity and plant population dynamics to water balance and long-lasting effects of disturbance on plant community dynamics. Persistent effects of land-use change and human-altered fire regimes on vegetation can overshadow or interact with climate change impacts. Models forecasting plant community responses to global change incorporate shifting ecological niches, population dynamics, species interactions, spatially explicit disturbance, ecosystem processes, and plant functional responses. Lastly, monitoring, experiments, and models evaluating multiple change drivers are needed to detect and predict vegetation changes in response to 21st century global change.« less

Global change and terrestrial plant community dynamics

PubMed Central

Franklin, Janet; Serra-Diaz, Josep M.; Syphard, Alexandra D.; Regan, Helen M.

2016-01-01

Anthropogenic drivers of global change include rising atmospheric concentrations of carbon dioxide and other greenhouse gasses and resulting changes in the climate, as well as nitrogen deposition, biotic invasions, altered disturbance regimes, and land-use change. Predicting the effects of global change on terrestrial plant communities is crucial because of the ecosystem services vegetation provides, from climate regulation to forest products. In this paper, we present a framework for detecting vegetation changes and attributing them to global change drivers that incorporates multiple lines of evidence from spatially extensive monitoring networks, distributed experiments, remotely sensed data, and historical records. Based on a literature review, we summarize observed changes and then describe modeling tools that can forecast the impacts of multiple drivers on plant communities in an era of rapid change. Observed responses to changes in temperature, water, nutrients, land use, and disturbance show strong sensitivity of ecosystem productivity and plant population dynamics to water balance and long-lasting effects of disturbance on plant community dynamics. Persistent effects of land-use change and human-altered fire regimes on vegetation can overshadow or interact with climate change impacts. Models forecasting plant community responses to global change incorporate shifting ecological niches, population dynamics, species interactions, spatially explicit disturbance, ecosystem processes, and plant functional responses. Monitoring, experiments, and models evaluating multiple change drivers are needed to detect and predict vegetation changes in response to 21st century global change. PMID:26929338

Predicting the Distribution of Commercially Important Invertebrate Stocks under Future Climate

PubMed Central

Russell, Bayden D.; Connell, Sean D.; Mellin, Camille; Brook, Barry W.; Burnell, Owen W.; Fordham, Damien A.

2012-01-01

The future management of commercially exploited species is challenging because techniques used to predict the future distribution of stocks under climate change are currently inadequate. We projected the future distribution and abundance of two commercially harvested abalone species (blacklip abalone, Haliotis rubra and greenlip abalone, H. laevigata) inhabiting coastal South Australia, using multiple species distribution models (SDM) and for decadal time slices through to 2100. Projections are based on two contrasting global greenhouse gas emissions scenarios. The SDMs identified August (winter) Sea Surface Temperature (SST) as the best descriptor of abundance and forecast that warming of winter temperatures under both scenarios may be beneficial to both species by allowing increased abundance and expansion into previously uninhabited coasts. This range expansion is unlikely to be realised, however, as projected warming of March SST is projected to exceed temperatures which cause up to 10-fold increases in juvenile mortality. By linking fine-resolution forecasts of sea surface temperature under different climate change scenarios to SDMs and physiological experiments, we provide a practical first approximation of the potential impact of climate-induced change on two species of marine invertebrates in the same fishery. PMID:23251326

Understanding land use change impacts on microclimate using Weather Research and Forecasting (WRF) model

NASA Astrophysics Data System (ADS)

Li, Xia; Mitra, Chandana; Dong, Li; Yang, Qichun

2018-02-01

To explore potential climatic consequences of land cover change in the Kolkata Metropolitan Development area, we projected microclimate conditions in this area using the Weather Research and Forecasting (WRF) model driven by future land use scenarios. Specifically, we considered two land conversion scenarios including an urbanization scenario that all the wetlands and croplands would be converted to built-up areas, and an irrigation expansion scenario in which all wetlands and dry croplands would be replaced by irrigated croplands. Results indicated that land use and land cover (LULC) change would dramatically increase regional temperature in this area under the urbanization scenario, but expanded irrigation tended to have a cooling effect. In the urbanization scenario, precipitation center tended to move eastward and lead to increased rainfall in eastern parts of this region. Increased irrigation stimulated rainfall in central and eastern areas but reduced rainfall in southwestern and northwestern parts of the study area. This study also demonstrated that urbanization significantly reduced latent heat fluxes and albedo of land surface; while increased sensible heat flux changes following urbanization suggested that developed land surfaces mainly acted as heat sources. In this study, climate change projection not only predicts future spatiotemporal patterns of multiple climate factors, but also provides valuable insights into policy making related to land use management, water resource management, and agriculture management to adapt and mitigate future climate changes in this populous region.

Scenarios of large mammal loss in Europe for the 21st century.

PubMed

Rondinini, Carlo; Visconti, Piero

2015-08-01

Distributions and populations of large mammals are declining globally, leading to an increase in their extinction risk. We forecasted the distribution of extant European large mammals (17 carnivores and 10 ungulates) based on 2 Rio+20 scenarios of socioeconomic development: business as usual and reduced impact through changes in human consumption of natural resources. These scenarios are linked to scenarios of land-use change and climate change through the spatial allocation of land conversion up to 2050. We used a hierarchical framework to forecast the extent and distribution of mammal habitat based on species' habitat preferences (as described in the International Union for Conservation of Nature Red List database) within a suitable climatic space fitted to the species' current geographic range. We analyzed the geographic and taxonomic variation of habitat loss for large mammals and the potential effect of the reduced impact policy on loss mitigation. Averaging across scenarios, European large mammals were predicted to lose 10% of their habitat by 2050 (25% in the worst-case scenario). Predicted loss was much higher for species in northwestern Europe, where habitat is expected to be lost due to climate and land-use change. Change in human consumption patterns was predicted to substantially improve the conservation of habitat for European large mammals, but not enough to reduce extinction risk if species cannot adapt locally to climate change or disperse. © 2015 Society for Conservation Biology.

Prediction of sea ice thickness cluster in the Northern Hemisphere

NASA Astrophysics Data System (ADS)

Fuckar, Neven-Stjepan; Guemas, Virginie; Johnson, Nathaniel; Doblas-Reyes, Francisco

2016-04-01

Sea ice thickness (SIT) has a potential to contain substantial climate memory and predictability in the northern hemisphere (NH) sea ice system. We use 5-member NH SIT, reconstructed with an ocean-sea-ice general circulation model (NEMOv3.3 with LIM2) with a simple data assimilation routine, to determine NH SIT modes of variability disentangled from the long-term climate change. Specifically, we apply the K-means cluster analysis - one of nonhierarchical clustering methods that partition data into modes or clusters based on their distances in the physical - to determine optimal number of NH SIT clusters (K=3) and their historical variability. To examine prediction skill of NH SIT clusters in EC-Earth2.3, a state-of-the-art coupled climate forecast system, we use 5-member ocean and sea ice initial conditions (IC) from the same ocean-sea-ice historical reconstruction and atmospheric IC from ERA-Interim reanalysis. We focus on May 1st and Nov 1st start dates from 1979 to 2010. Common skill metrics of probability forecast, such as rank probability skill core and ROC (relative operating characteristics - hit rate versus false alarm rate) and reliability diagrams show that our dynamical model predominately perform better than the 1st order Marko chain forecast (that beats climatological forecast) over the first forecast year. On average May 1st start dates initially have lower skill than Nov 1st start dates, but their skill is degraded at slower rate than skill of forecast started on Nov 1st.

Coupled Downscaled Climate Models and Ecophysiological Metrics Forecast Habitat Compression for an Endangered Estuarine Fish

PubMed Central

Brown, Larry R.; Komoroske, Lisa M.; Wagner, R. Wayne; Morgan-King, Tara; May, Jason T.; Connon, Richard E.; Fangue, Nann A.

2016-01-01

Climate change is driving rapid changes in environmental conditions and affecting population and species’ persistence across spatial and temporal scales. Integrating climate change assessments into biological resource management, such as conserving endangered species, is a substantial challenge, partly due to a mismatch between global climate forecasts and local or regional conservation planning. Here, we demonstrate how outputs of global climate change models can be downscaled to the watershed scale, and then coupled with ecophysiological metrics to assess climate change effects on organisms of conservation concern. We employed models to estimate future water temperatures (2010–2099) under several climate change scenarios within the large heterogeneous San Francisco Estuary. We then assessed the warming effects on the endangered, endemic Delta Smelt, Hypomesus transpacificus, by integrating localized projected water temperatures with thermal sensitivity metrics (tolerance, spawning and maturation windows, and sublethal stress thresholds) across life stages. Lethal temperatures occurred under several scenarios, but sublethal effects resulting from chronic stressful temperatures were more common across the estuary (median >60 days above threshold for >50% locations by the end of the century). Behavioral avoidance of such stressful temperatures would make a large portion of the potential range of Delta Smelt unavailable during the summer and fall. Since Delta Smelt are not likely to migrate to other estuaries, these changes are likely to result in substantial habitat compression. Additionally, the Delta Smelt maturation window was shortened by 18–85 days, revealing cumulative effects of stressful summer and fall temperatures with early initiation of spring spawning that may negatively impact fitness. Our findings highlight the value of integrating sublethal thresholds, life history, and in situ thermal heterogeneity into global change impact assessments. As downscaled climate models are becoming widely available, we conclude that similar assessments at management-relevant scales will improve the scientific basis for resource management decisions. PMID:26796147

Coupled Downscaled Climate Models and Ecophysiological Metrics Forecast Habitat Compression for an Endangered Estuarine Fish.

PubMed

Brown, Larry R; Komoroske, Lisa M; Wagner, R Wayne; Morgan-King, Tara; May, Jason T; Connon, Richard E; Fangue, Nann A

2016-01-01

Climate change is driving rapid changes in environmental conditions and affecting population and species' persistence across spatial and temporal scales. Integrating climate change assessments into biological resource management, such as conserving endangered species, is a substantial challenge, partly due to a mismatch between global climate forecasts and local or regional conservation planning. Here, we demonstrate how outputs of global climate change models can be downscaled to the watershed scale, and then coupled with ecophysiological metrics to assess climate change effects on organisms of conservation concern. We employed models to estimate future water temperatures (2010-2099) under several climate change scenarios within the large heterogeneous San Francisco Estuary. We then assessed the warming effects on the endangered, endemic Delta Smelt, Hypomesus transpacificus, by integrating localized projected water temperatures with thermal sensitivity metrics (tolerance, spawning and maturation windows, and sublethal stress thresholds) across life stages. Lethal temperatures occurred under several scenarios, but sublethal effects resulting from chronic stressful temperatures were more common across the estuary (median >60 days above threshold for >50% locations by the end of the century). Behavioral avoidance of such stressful temperatures would make a large portion of the potential range of Delta Smelt unavailable during the summer and fall. Since Delta Smelt are not likely to migrate to other estuaries, these changes are likely to result in substantial habitat compression. Additionally, the Delta Smelt maturation window was shortened by 18-85 days, revealing cumulative effects of stressful summer and fall temperatures with early initiation of spring spawning that may negatively impact fitness. Our findings highlight the value of integrating sublethal thresholds, life history, and in situ thermal heterogeneity into global change impact assessments. As downscaled climate models are becoming widely available, we conclude that similar assessments at management-relevant scales will improve the scientific basis for resource management decisions.

Coupled downscaled climate models and ecophysiological metrics forecast habitat compression for an endangered estuarine fish

USGS Publications Warehouse

Brown, Larry R.; Komoroske, Lisa M; Wagner, R Wayne; Morgan-King, Tara; May, Jason T.; Connon, Richard E; Fangue, Nann A.

2016-01-01

Climate change is driving rapid changes in environmental conditions and affecting population and species’ persistence across spatial and temporal scales. Integrating climate change assessments into biological resource management, such as conserving endangered species, is a substantial challenge, partly due to a mismatch between global climate forecasts and local or regional conservation planning. Here, we demonstrate how outputs of global climate change models can be downscaled to the watershed scale, and then coupled with ecophysiological metrics to assess climate change effects on organisms of conservation concern. We employed models to estimate future water temperatures (2010–2099) under several climate change scenarios within the large heterogeneous San Francisco Estuary. We then assessed the warming effects on the endangered, endemic Delta Smelt, Hypomesus transpacificus, by integrating localized projected water temperatures with thermal sensitivity metrics (tolerance, spawning and maturation windows, and sublethal stress thresholds) across life stages. Lethal temperatures occurred under several scenarios, but sublethal effects resulting from chronic stressful temperatures were more common across the estuary (median >60 days above threshold for >50% locations by the end of the century). Behavioral avoidance of such stressful temperatures would make a large portion of the potential range of Delta Smelt unavailable during the summer and fall. Since Delta Smelt are not likely to migrate to other estuaries, these changes are likely to result in substantial habitat compression. Additionally, the Delta Smelt maturation window was shortened by 18–85 days, revealing cumulative effects of stressful summer and fall temperatures with early initiation of spring spawning that may negatively impact fitness. Our findings highlight the value of integrating sublethal thresholds, life history, and in situ thermal heterogeneity into global change impact assessments. As downscaled climate models are becoming widely available, we conclude that similar assessments at management-relevant scales will improve the scientific basis for resource management decisions.

Improving the representation of clouds, radiation, and precipitation using spectral nudging in the Weather Research and Forecasting model

NASA Astrophysics Data System (ADS)

Spero, Tanya L.; Otte, Martin J.; Bowden, Jared H.; Nolte, Christopher G.

2014-10-01

Spectral nudging—a scale-selective interior constraint technique—is commonly used in regional climate models to maintain consistency with large-scale forcing while permitting mesoscale features to develop in the downscaled simulations. Several studies have demonstrated that spectral nudging improves the representation of regional climate in reanalysis-forced simulations compared with not using nudging in the interior of the domain. However, in the Weather Research and Forecasting (WRF) model, spectral nudging tends to produce degraded precipitation simulations when compared to analysis nudging—an interior constraint technique that is scale indiscriminate but also operates on moisture fields which until now could not be altered directly by spectral nudging. Since analysis nudging is less desirable for regional climate modeling because it dampens fine-scale variability, changes are proposed to the spectral nudging methodology to capitalize on differences between the nudging techniques and aim to improve the representation of clouds, radiation, and precipitation without compromising other fields. These changes include adding spectral nudging toward moisture, limiting nudging to below the tropopause, and increasing the nudging time scale for potential temperature, all of which collectively improve the representation of mean and extreme precipitation, 2 m temperature, clouds, and radiation, as demonstrated using a model-simulated 20 year historical period. Such improvements to WRF may increase the fidelity of regional climate data used to assess the potential impacts of climate change on human health and the environment and aid in climate change mitigation and adaptation studies.

Assessment of extreme flood events in a changing climate for a long-term planning of socio-economic infrastructure in the Russian Arctic

NASA Astrophysics Data System (ADS)

Shevnina, Elena; Kourzeneva, Ekaterina; Kovalenko, Viktor; Vihma, Timo

2017-05-01

Climate warming has been more acute in the Arctic than at lower latitudes and this tendency is expected to continue. This generates major challenges for economic activity in the region. Among other issues is the long-term planning and development of socio-economic infrastructure (dams, bridges, roads, etc.), which require climate-based forecasts of the frequency and magnitude of detrimental flood events. To estimate the cost of the infrastructure and operational risk, a probabilistic form of long-term forecasting is preferable. In this study, a probabilistic model to simulate the parameters of the probability density function (PDF) for multi-year runoff based on a projected climatology is applied to evaluate changes in extreme floods for the territory of the Russian Arctic. The model is validated by cross-comparison of the modelled and empirical PDFs using observations from 23 sites located in northern Russia. The mean values and coefficients of variation (CVs) of the spring flood depth of runoff are evaluated under four climate scenarios, using simulations of six climate models for the period 2010-2039. Regions with substantial expected changes in the means and CVs of spring flood depth of runoff are outlined. For the sites located within such regions, it is suggested to account for the future climate change in calculating the maximal discharges of rare occurrence. An example of engineering calculations for maximal discharges with 1 % exceedance probability is provided for the Nadym River at Nadym.

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

Franklin, Janet; Serra-Diaz, Josep M.; Syphard, Alexandra D.

Anthropogenic drivers of global change include rising atmospheric concentrations of carbon dioxide and other greenhouse gasses and resulting changes in the climate, as well as nitrogen deposition, biotic invasions, altered disturbance regimes, and land-use change. Predicting the effects of global change on terrestrial plant communities is crucial because of the ecosystem services vegetation provides, from climate regulation to forest products. In this article, we present a framework for detecting vegetation changes and attributing them to global change drivers that incorporates multiple lines of evidence from spatially extensive monitoring networks, distributed experiments, remotely sensed data, and historical records. Based on amore » literature review, we summarize observed changes and then describe modeling tools that can forecast the impacts of multiple drivers on plant communities in an era of rapid change. Observed responses to changes in temperature, water, nutrients, land use, and disturbance show strong sensitivity of ecosystem productivity and plant population dynamics to water balance and long-lasting effects of disturbance on plant community dynamics. Persistent effects of land-use change and human-altered fire regimes on vegetation can overshadow or interact with climate change impacts. Models forecasting plant community responses to global change incorporate shifting ecological niches, population dynamics, species interactions, spatially explicit disturbance, ecosystem processes, and plant functional responses. Lastly, monitoring, experiments, and models evaluating multiple change drivers are needed to detect and predict vegetation changes in response to 21st century global change.« less

Environmental prediction, risk assessment and extreme events: adaptation strategies for the developing world

PubMed Central

Webster, Peter J.; Jian, Jun

2011-01-01

The uncertainty associated with predicting extreme weather events has serious implications for the developing world, owing to the greater societal vulnerability to such events. Continual exposure to unanticipated extreme events is a contributing factor for the descent into perpetual and structural rural poverty. We provide two examples of how probabilistic environmental prediction of extreme weather events can support dynamic adaptation. In the current climate era, we describe how short-term flood forecasts have been developed and implemented in Bangladesh. Forecasts of impending floods with horizons of 10 days are used to change agricultural practices and planning, store food and household items and evacuate those in peril. For the first time in Bangladesh, floods were anticipated in 2007 and 2008, with broad actions taking place in advance of the floods, grossing agricultural and household savings measured in units of annual income. We argue that probabilistic environmental forecasts disseminated to an informed user community can reduce poverty caused by exposure to unanticipated extreme events. Second, it is also realized that not all decisions in the future can be made at the village level and that grand plans for water resource management require extensive planning and funding. Based on imperfect models and scenarios of economic and population growth, we further suggest that flood frequency and intensity will increase in the Ganges, Brahmaputra and Yangtze catchments as greenhouse-gas concentrations increase. However, irrespective of the climate-change scenario chosen, the availability of fresh water in the latter half of the twenty-first century seems to be dominated by population increases that far outweigh climate-change effects. Paradoxically, fresh water availability may become more critical if there is no climate change. PMID:22042897

24 CFR 242.16 - Applications.

Code of Federal Regulations, 2011 CFR

2011-04-01

... mix, market trends, population forecasts, and business climate; (v) The hospital's demonstrated... if, during the year, there is a major change in the circumstances that caused HUD to determine that...

24 CFR 242.16 - Applications.

Code of Federal Regulations, 2012 CFR

2012-04-01

... mix, market trends, population forecasts, and business climate; (v) The hospital's demonstrated... if, during the year, there is a major change in the circumstances that caused HUD to determine that...

24 CFR 242.16 - Applications.

Code of Federal Regulations, 2014 CFR

2014-04-01

... mix, market trends, population forecasts, and business climate; (v) The hospital's demonstrated... if, during the year, there is a major change in the circumstances that caused HUD to determine that...

24 CFR 242.16 - Applications.

Code of Federal Regulations, 2010 CFR

2010-04-01

... mix, market trends, population forecasts, and business climate; (v) The hospital's demonstrated... if, during the year, there is a major change in the circumstances that caused HUD to determine that...

Multi-model comparison highlights consistency in predicted effect of warming on a semi-arid shrub

USGS Publications Warehouse

Renwick, Katherine M.; Curtis, Caroline; Kleinhesselink, Andrew R.; Schlaepfer, Daniel R.; Bradley, Bethany A.; Aldridge, Cameron L.; Poulter, Benjamin; Adler, Peter B.

2018-01-01

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi-model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi-model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.

Forecasting the response of Earth's surface to future climatic and land use changes: A review of methods and research needs

DOE PAGES

Pelletier, Jon D.; Murray, A. Brad; Pierce, Jennifer L.; ...

2015-07-14

In the future, Earth will be warmer, precipitation events will be more extreme, global mean sea level will rise, and many arid and semiarid regions will be drier. Human modifications of landscapes will also occur at an accelerated rate as developed areas increase in size and population density. We now have gridded global forecasts, being continually improved, of the climatic and land use changes (C&LUC) that are likely to occur in the coming decades. However, besides a few exceptions, consensus forecasts do not exist for how these C&LUC will likely impact Earth-surface processes and hazards. In some cases, we havemore » the tools to forecast the geomorphic responses to likely future C&LUC. Fully exploiting these models and utilizing these tools will require close collaboration among Earth-surface scientists and Earth-system modelers. This paper assesses the state-of-the-art tools and data that are being used or could be used to forecast changes in the state of Earth's surface as a result of likely future C&LUC. We also propose strategies for filling key knowledge gaps, emphasizing where additional basic research and/or collaboration across disciplines are necessary. The main body of the paper addresses cross-cutting issues, including the importance of nonlinear/threshold-dominated interactions among topography, vegetation, and sediment transport, as well as the importance of alternate stable states and extreme, rare events for understanding and forecasting Earth-surface response to C&LUC. Five supplements delve into different scales or process zones (global-scale assessments and fluvial, aeolian, glacial/periglacial, and coastal process zones) in detail.« less

Sustainable Water Resources for Communities under Climate Change: Can State-of-the-Art Forecasting Inform Decision-Making in Data Sparse Regions?

NASA Astrophysics Data System (ADS)

Mayer, A.; Vivoni, E.; Halvorsen, K.; Robles-Morua, A.; Dana, K.; Che, D.; Mirchi, A.; Kossak, D.; Casteneda, M.

2013-05-01

In this project, we are studying decision-making for water resources management in anticipation of climate change in the Sonora River Basin, Mexico as a case study for the broader arid and semiarid southwestern North America. The goal of the proposed project is to determine whether water resources systems modeling, developed within a participatory framework, can contribute to the building of management strategies in a context of water scarcity, conflicting water uses and highly variable and changing climate conditions. The participatory modeling approach will be conducted through a series of three workshops, designed to encourage substantive participation from a broad range of actors, including representatives from federal and local government agencies, water use sectors, non-governmental organizations, and academics. Participants will guide the design of supply- and demand-side management strategies and selection of climate change and infrastructure management scenarios using state-of-the-art engineering tools. These tools include a water resources systems framework, a spatially-explicit hydrologic model, the use of forecasted climate scenarios under 21st century climate change, and observations obtained from field and satellite sensors. Through the theory of planned behavior, the participatory modeling process will be evaluated to understand if, and to what extent, the engineering tools are useful in the uncertain and politically-complex setting. Pre- and post-workshop surveys will be used in this evaluation. For this contribution, we present the results of the first collaborative modeling workshop that will be held in March 2013, where we will develop the initial modeling framework in collaboration with workshop participants.

Lags in the response of mountain plant communities to climate change

PubMed Central

Alexander, Jake M.; Chalmandrier, Loïc; Lenoir, Jonathan; Burgess, Treena I.; Essl, Franz; Haider, Sylvia; Kueffer, Christoph; McDougall, Keith; Milbau, Ann; Nuñez, Martin A.; Pauchard, Aníbal; Rabitsch, Wolfgang; Rew, Lisa J.; Sanders, Nathan J.; Pellissier, Loïc

2018-01-01

Rapid climatic changes and increasing human influence at high elevations around the world will have profound impacts on mountain biodiversity. However, forecasts from statistical models (e.g. species distribution models) rarely consider that plant community changes could substantially lag behind climatic changes, hindering our ability to make temporally realistic projections for the coming century. Indeed, the magnitudes of lags, and the relative importance of the different factors giving rise to them, remain poorly understood. We review evidence for three types of lag: “dispersal lags” affecting plant species’ spread along elevational gradients, “establishment lags” following their arrival in recipient communities, and “extinction lags” of resident species. Variation in lags is explained by variation among species in physiological and demographic responses, by effects of altered biotic interactions, and by aspects of the physical environment. Of these, altered biotic interactions could contribute substantially to establishment and extinction lags, yet impacts of biotic interactions on range dynamics are poorly understood. We develop a mechanistic community model to illustrate how species turnover in future communities might lag behind simple expectations based on species’ range shifts with unlimited dispersal. The model shows a combined contribution of altered biotic interactions and dispersal lags to plant community turnover along an elevational gradient following climate warming. Our review and simulation support the view that accounting for disequilibrium range dynamics will be essential for realistic forecasts of patterns of biodiversity under climate change, with implications for the conservation of mountain species and the ecosystem functions they provide. PMID:29112781

Atmospheric and oceanographic research review, 1978. [global weather, ocean/air interactions, and climate

NASA Technical Reports Server (NTRS)

1978-01-01

Research activities related to global weather, ocean/air interactions, and climate are reported. The global weather research is aimed at improving the assimilation of satellite-derived data in weather forecast models, developing analysis/forecast models that can more fully utilize satellite data, and developing new measures of forecast skill to properly assess the impact of satellite data on weather forecasting. The oceanographic research goal is to understand and model the processes that determine the general circulation of the oceans, focusing on those processes that affect sea surface temperature and oceanic heat storage, which are the oceanographic variables with the greatest influence on climate. The climate research objective is to support the development and effective utilization of space-acquired data systems in climate forecast models and to conduct sensitivity studies to determine the affect of lower boundary conditions on climate and predictability studies to determine which global climate features can be modeled either deterministically or statistically.

A seasonal hydrologic ensemble prediction system for water resource management

NASA Astrophysics Data System (ADS)

Luo, L.; Wood, E. F.

2006-12-01

A seasonal hydrologic ensemble prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing ensemble prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically downscaled, seasonal forecast from dynamic climate models. The seasonal hydrologic ensemble prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.

The Copernicus Climate Change Service (C3S): Open Access to a Climate Data Store

NASA Astrophysics Data System (ADS)

Thepaut, Jean-Noel; Dee, Dick

2016-04-01

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 monitor 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. At the heart of the Service is the provision of open access to a one stop shop (the Climate Data Store) of climate data and modelling, analysing more than 20 Essential Climate Variables to build a global picture of our past, present and future climate and developing customisable climate indicators for key economic sectors, such as energy, water management, agriculture, insurance, health… This talk will focus on the Climate Data Store facility, designed as a distributed system, providing improved access to existing datasets though a unified web interface. This service will accommodate the needs of the highly diverse set of users, from policy makers to expert practitioners and scientists.

Coupled Teleconnections and River Dynamics for Enhanced Hydrologic Forecasting in the Upper Colorado River Basin USA

NASA Astrophysics Data System (ADS)

Matter, M. A.; Garcia, L. A.; Fontane, D. G.

2005-12-01

Accuracy of water supply forecasts has improved for some river basins in the western U.S.A. by integrating knowledge of climate teleconnections, such as El Niño/Southern Oscillation (ENSO), into forecasting routines, but in other basins, such as the Colorado River Basin (CRB), forecast accuracy has declined (Pagano et al. 2004). Longer lead time and more accurate seasonal forecasts, particularly during floods or drought, could help reduce uncertainty and risk in decision-making and lengthen the period for planning more efficient and effective strategies for water use and ecosystem management. The goal of this research is to extend the lead time for snowmelt hydrograph estimation by 4-6 months (from spring to the preceding fall), and at the same time increase the accuracy of snowmelt runoff estimates in the Upper CRB (UCRB). We hypothesize that: (1) UCRB snowpack accumulation and melt are driven by large scale climate modes, including ENSO, PDO and AMO, that establish by fall and persist into early spring; (2) forecast analysis may begin in the fall prior to the start of the primary snow accumulation period and when energy to change the climate system is decreasing; and (3) between fall and early spring, streamflow hydrographs will amplify precipitation and temperature signals, and thus will evolve characteristically in response to wet, dry or average hydroclimatic conditions. Historical in situ records from largely unregulated river reaches and undeveloped time periods of the UCRB are used to test this hypothesis. Preliminary results show that, beginning in the fall (e.g., October or November) streamflow characteristics, including magnitude, rate of change and variability, as well as timing and magnitude of fall/early winter and late winter/early spring season flow volumes, are directly correlated with the magnitude of the upcoming snowmelt runoff (or annual basin yield). The use of climate teleconnections to determine characteristic streamflow responses in the UCRB advances understanding of atmosphere/land surface processes and interactions in complex terrain and subsequent effects on snowpack development and runoff (i.e., water supply), and may be used to improve seasonal forecast accuracy and extend lead time to develop more efficient and effective management strategies for water resources and ecosystems.

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Contacts Change Log Events Calendar Numerical Forecast Systems NCEP Model Analysis and Guidance Page [< Modeling Center NOAA Center for Weather and Climate Prediction (NCWCP) 5830 University Research Court

Forecasting Fire Season Severity in South America Using Sea Surface Temperature Anomalies

NASA Technical Reports Server (NTRS)

Chen, Yang; Randerson, James T.; Morton, Douglas C.; DeFries, Ruth S.; Collatz, G. James; Kasibhatla, Prasad S.; Giglio, Louis; Jin, Yufang; Marlier, Miriam E.

2011-01-01

Fires in South America cause forest degradation and contribute to carbon emissions associated with land use change. We investigated the relationship between year-to-year changes in fire activity in South America and sea surface temperatures. We found that the Oceanic Ni o Index was correlated with interannual fire activity in the eastern Amazon, whereas the Atlantic Multidecadal Oscillation index was more closely linked with fires in the southern and southwestern Amazon. Combining these two climate indices, we developed an empirical model to forecast regional fire season severity with lead times of 3 to 5 months. Our approach may contribute to the development of an early warning system for anticipating the vulnerability of Amazon forests to fires, thus enabling more effective management with benefits for climate and air quality.

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

Changing flood frequencies under opposing late Pleistocene eastern Mediterranean climates.

PubMed

Ben Dor, Yoav; Armon, Moshe; Ahlborn, Marieke; Morin, Efrat; Erel, Yigal; Brauer, Achim; Schwab, Markus Julius; Tjallingii, Rik; Enzel, Yehouda

2018-05-31

Floods comprise a dominant hydroclimatic phenomenon in aridlands with significant implications for humans, infrastructure, and landscape evolution worldwide. The study of short-term hydroclimatic variability, such as floods, and its forecasting for episodes of changing climate therefore poses a dominant challenge for the scientific community, and predominantly relies on modeling. Testing the capabilities of climate models to properly describe past and forecast future short-term hydroclimatic phenomena such as floods requires verification against suitable geological archives. However, determining flood frequency during changing climate is rarely achieved, because modern and paleoflood records, especially in arid regions, are often too short or discontinuous. Thus, coeval independent climate reconstructions and paleoflood records are required to further understand the impact of climate change on flood generation. Dead Sea lake levels reflect the mean centennial-millennial hydrological budget in the eastern Mediterranean. In contrast, floods in the large watersheds draining directly into the Dead Sea, are linked to short-term synoptic circulation patterns reflecting hydroclimatic variability. These two very different records are combined in this study to resolve flood frequency during opposing mean climates. Two 700-year-long, seasonally-resolved flood time series constructed from late Pleistocene Dead Sea varved sediments, coeval with significant Dead Sea lake level variations are reported. These series demonstrate that episodes of rising lake levels are characterized by higher frequency of floods, shorter intervals between years of multiple floods, and asignificantly larger number of years that experienced multiple floods. In addition, floods cluster into intervals of intense flooding, characterized by 75% and 20% increased frequency above their respective background frequencies during rising and falling lake-levels, respectively. Mean centennial precipitation in the eastern Mediterranean is therefore coupled with drastic changes in flood frequencies. These drastic changes in flood frequencies are linked to changes in the track, depth, and frequency of mid-latitude eastern Mediterranean cyclones, determining mean climatology resulting in wetter and drier regional climatic episodes.

Phylogenetic approaches reveal biodiversity threats under climate change

NASA Astrophysics Data System (ADS)

González-Orozco, Carlos E.; Pollock, Laura J.; Thornhill, Andrew H.; Mishler, Brent D.; Knerr, Nunzio; Laffan, Shawn W.; Miller, Joseph T.; Rosauer, Dan F.; Faith, Daniel P.; Nipperess, David A.; Kujala, Heini; Linke, Simon; Butt, Nathalie; Külheim, Carsten; Crisp, Michael D.; Gruber, Bernd

2016-12-01

Predicting the consequences of climate change for biodiversity is critical to conservation efforts. Extensive range losses have been predicted for thousands of individual species, but less is known about how climate change might impact whole clades and landscape-scale patterns of biodiversity. Here, we show that climate change scenarios imply significant changes in phylogenetic diversity and phylogenetic endemism at a continental scale in Australia using the hyper-diverse clade of eucalypts. We predict that within the next 60 years the vast majority of species distributions (91%) across Australia will shrink in size (on average by 51%) and shift south on the basis of projected suitable climatic space. Geographic areas currently with high phylogenetic diversity and endemism are predicted to change substantially in future climate scenarios. Approximately 90% of the current areas with concentrations of palaeo-endemism (that is, places with old evolutionary diversity) are predicted to disappear or shift their location. These findings show that climate change threatens whole clades of the phylogenetic tree, and that the outlined approach can be used to forecast areas of biodiversity losses and continental-scale impacts of climate change.

The future of terrestrial mammals in the Mediterranean basin under climate change

PubMed Central

Maiorano, Luigi; Falcucci, Alessandra; Zimmermann, Niklaus E.; Psomas, Achilleas; Pottier, Julien; Baisero, Daniele; Rondinini, Carlo; Guisan, Antoine; Boitani, Luigi

2011-01-01

The Mediterranean basin is considered a hotspot of biological diversity with a long history of modification of natural ecosystems by human activities, and is one of the regions that will face extensive changes in climate. For 181 terrestrial mammals (68% of all Mediterranean mammals), we used an ensemble forecasting approach to model the future (approx. 2100) potential distribution under climate change considering five climate change model outputs for two climate scenarios. Overall, a substantial number of Mediterranean mammals will be severely threatened by future climate change, particularly endemic species. Moreover, we found important changes in potential species richness owing to climate change, with some areas (e.g. montane region in central Italy) gaining species, while most of the region will be losing species (mainly Spain and North Africa). Existing protected areas (PAs) will probably be strongly influenced by climate change, with most PAs in Africa, the Middle East and Spain losing a substantial number of species, and those PAs gaining species (e.g. central Italy and southern France) will experience a substantial shift in species composition. PMID:21844047

Using Scaling to Understand, Model and Predict Global Scale Anthropogenic and Natural Climate Change

NASA Astrophysics Data System (ADS)

Lovejoy, S.; del Rio Amador, L.

2014-12-01

The atmosphere is variable over twenty orders of magnitude in time (≈10-3 to 1017 s) and almost all of the variance is in the spectral "background" which we show can be divided into five scaling regimes: weather, macroweather, climate, macroclimate and megaclimate. We illustrate this with instrumental and paleo data. Based the signs of the fluctuation exponent H, we argue that while the weather is "what you get" (H>0: fluctuations increasing with scale), that it is macroweather (H<0: fluctuations decreasing with scale) - not climate - "that you expect". The conventional framework that treats the background as close to white noise and focuses on quasi-periodic variability assumes a spectrum that is in error by a factor of a quadrillion (≈ 1015). Using this scaling framework, we can quantify the natural variability, distinguish it from anthropogenic variability, test various statistical hypotheses and make stochastic climate forecasts. For example, we estimate the probability that the warming is simply a giant century long natural fluctuation is less than 1%, most likely less than 0.1% and estimate return periods for natural warming events of different strengths and durations, including the slow down ("pause") in the warming since 1998. The return period for the pause was found to be 20-50 years i.e. not very unusual; however it immediately follows a 6 year "pre-pause" warming event of almost the same magnitude with a similar return period (30 - 40 years). To improve on these unconditional estimates, we can use scaling models to exploit the long range memory of the climate process to make accurate stochastic forecasts of the climate including the pause. We illustrate stochastic forecasts on monthly and annual scale series of global and northern hemisphere surface temperatures. We obtain forecast skill nearly as high as the theoretical (scaling) predictability limits allow: for example, using hindcasts we find that at 10 year forecast horizons we can still explain ≈ 15% of the anomaly variance. These scaling hindcasts have comparable - or smaller - RMS errors than existing GCM's. We discuss how these be further improved by going beyond time series forecasts to space-time.

Assessing land ownership as a driver of change in the distribution, structure, and composition of California's forests.

NASA Astrophysics Data System (ADS)

Easterday, K.; Kelly, M.; McIntyre, P. J.

2015-12-01

Climate change is forecasted to have considerable influence on the distribution, structure, and function of California's forests. However, human interactions with forested landscapes (e.g. fire suppression, resource extraction and etc.) have complicated scientific understanding of the relative contributions of climate change and anthropogenic land management practices as drivers of change. Observed changes in forest structure towards smaller, denser forests across California have been attributed to both climate change (e.g. increased temperatures and declining water availability) and management practices (e.g. fire suppression and logging). Disentangling how these drivers of change act both together and apart is important to developing sustainable policy and land management practices as well as enhancing knowledge of human and natural system interactions. To that end, a comprehensive historical dataset - the Vegetation Type Mapping project (VTM) - and a modern forest inventory dataset (FIA) are used to analyze how spatial variations in vegetation composition and structure over a ~100 year period can be explained by land ownership.Climate change is forecasted to have considerable influence on the distribution, structure, and function of California's forests. However, human interactions with forested landscapes (e.g. fire suppression, resource extraction and etc.) have complicated scientific understanding of the relative contributions of climate change and anthropogenic land management practices as drivers of change. Observed changes in forest structure towards smaller, denser forests across California have been attributed to both climate change (e.g. increased temperatures and declining water availability) and management practices (e.g. fire suppression and logging). Disentangling how these drivers of change act both together and apart is important to developing sustainable policy and land management practices as well as enhancing knowledge of human and natural system interactions. To that end, a comprehensive historical dataset - the Vegetation Type Mapping project (VTM) - and a modern forest inventory dataset (FIA) are used to analyze how spatial variations in vegetation composition and structure over a ~100 year period can be explained by land ownership.

Process-based modeling of species' responses to climate change - a proof of concept using western North American trees

NASA Astrophysics Data System (ADS)

Evans, M. E.; Merow, C.; Record, S.; Menlove, J.; Gray, A.; Cundiff, J.; McMahon, S.; Enquist, B. J.

2013-12-01

Current attempts to forecast how species' distributions will change in response to climate change suffer under a fundamental trade-off: between modeling many species superficially vs. few species in detail (between correlative vs. mechanistic models). The goals of this talk are two-fold: first, we present a Bayesian multilevel modeling framework, dynamic range modeling (DRM), for building process-based forecasts of many species' distributions at a time, designed to address the trade-off between detail and number of distribution forecasts. In contrast to 'species distribution modeling' or 'niche modeling', which uses only species' occurrence data and environmental data, DRMs draw upon demographic data, abundance data, trait data, occurrence data, and GIS layers of climate in a single framework to account for two processes known to influence range dynamics - demography and dispersal. The vision is to use extensive databases on plant demography, distributions, and traits - in the Botanical Information and Ecology Network, the Forest Inventory and Analysis database (FIA), and the International Tree Ring Data Bank - to develop DRMs for North American trees. Second, we present preliminary results from building the core submodel of a DRM - an integral projection model (IPM) - for a sample of dominant tree species in western North America. IPMs are used to infer demographic niches - i.e., the set of environmental conditions under which population growth rate is positive - and project population dynamics through time. Based on >550,000 data points derived from FIA for nine tree species in western North America, we show IPM-based models of their current and future distributions, and discuss how IPMs can be used to forecast future forest productivity, mortality patterns, and inform efforts at assisted migration.

Some Advances in Downscaling Probabilistic Climate Forecasts for Agricultural Decision Support

NASA Astrophysics Data System (ADS)

Han, E.; Ines, A.

2015-12-01

Seasonal climate forecasts, commonly provided in tercile-probabilities format (below-, near- and above-normal), need to be translated into more meaningful information for decision support of practitioners in agriculture. In this paper, we will present two new novel approaches to temporally downscale probabilistic seasonal climate forecasts: one non-parametric and another parametric method. First, the non-parametric downscaling approach called FResampler1 uses the concept of 'conditional block sampling' of weather data to create daily weather realizations of a tercile-based seasonal climate forecasts. FResampler1 randomly draws time series of daily weather parameters (e.g., rainfall, maximum and minimum temperature and solar radiation) from historical records, for the season of interest from years that belong to a certain rainfall tercile category (e.g., being below-, near- and above-normal). In this way, FResampler1 preserves the covariance between rainfall and other weather parameters as if conditionally sampling maximum and minimum temperature and solar radiation if that day is wet or dry. The second approach called predictWTD is a parametric method based on a conditional stochastic weather generator. The tercile-based seasonal climate forecast is converted into a theoretical forecast cumulative probability curve. Then the deviates for each percentile is converted into rainfall amount or frequency or intensity to downscale the 'full' distribution of probabilistic seasonal climate forecasts. Those seasonal deviates are then disaggregated on a monthly basis and used to constrain the downscaling of forecast realizations at different percentile values of the theoretical forecast curve. As well as the theoretical basis of the approaches we will discuss sensitivity analysis (length of data and size of samples) of them. In addition their potential applications for managing climate-related risks in agriculture will be shown through a couple of case studies based on actual seasonal climate forecasts for: rice cropping in the Philippines and maize cropping in India and Kenya.

Rapid climate fluctuations over the past millennium: evidence from a lacustrine record of Basomtso Lake, southeastern Tibetan Plateau

PubMed Central

Li, Kai; Liu, Xingqi; Herzschuh, Ulrike; Wang, Yongbo

2016-01-01

Abrupt climate changes and fluctuations over short time scales are superimposed on long-term climate changes. Understanding rapid climate fluctuations at the decadal time scale over the past millennium will enhance our understanding of patterns of climate variability and aid in forecasting climate changes in the future. In this study, climate changes on the southeastern Tibetan Plateau over the past millennium were determined from a 4.82-m-long sediment core from Basomtso Lake. At the centennial time scale, the Medieval Climate Anomaly (MCA), Little Ice Age (LIA) and Current Warm Period (CWP) are distinct in the Basomtso region. Rapid climate fluctuations inferred from five episodes with higher sediment input and likely warmer conditions, as well as seven episodes with lower sediment input and likely colder conditions, were well preserved in our record. These episodes with higher and lower sediment input are characterized by abrupt climate changes and short time durations. Spectral analysis indicates that the climate variations at the centennial scale on the southeastern Tibetan Plateau are influenced by solar activity during the past millennium. PMID:27091591

An expert system-based approach to prediction of year-to-year climatic variations in the North Atlantic region

NASA Astrophysics Data System (ADS)

Rodionov, S. N.; Martin, J. H.

1999-07-01

A novel approach to climate forecasting on an interannual time scale is described. The approach is based on concepts and techniques from artificial intelligence and expert systems. The suitability of this approach to climate diagnostics and forecasting problems and its advantages compared with conventional forecasting techniques are discussed. The article highlights some practical aspects of the development of climatic expert systems (CESs) and describes an implementation of such a system for the North Atlantic (CESNA). Particular attention is paid to the content of CESNA's knowledge base and those conditions that make climatic forecasts one to several years in advance possible. A detailed evaluation of the quality of the experimental real-time forecasts made by CESNA for the winters of 1995-1996, 1996-1997 and 1997-1998 are presented.

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post Contacts Change Log Events Calendar People Numerical Forecast Systems Ensemble and Post Processing Team

Regeneration potential of Taxodium distichum swamps and climate change

USGS Publications Warehouse

Middleton, B.A.

2009-01-01

Seed bank densities respond to factors across local to landscape scales, and therefore, knowledge of these responses may be necessary in forecasting the effects of climate change on the regeneration of species. This study relates the seed bank densities of species of Taxodium distichum swamps to local water regime and regional climate factors at five latitudes across the Mississippi River Alluvial Valley from southern Illinois to Louisiana. In an outdoor nursery setting, the seed banks of twenty-five swamps were exposed to non-flooded (freely drained) or flooded treatments, and the number and species of seeds germinating were recorded from each swamp during one growing season. Based on ANOVA analysis, the majority of dominant species had a higher rate of germination in non-flooded versus flooded treatments. Similarly, an NMS comparison, which considered the local water regime and regional climate of the swamps, found that the species of seeds germinating, almost completely shifted under non-flooded versus flooded treatments. For example, in wetter northern swamps, seeds of Taxodium distichum germinated in non-flooded conditions, but did not germinate from the same seed banks in flooded conditions. In wetter southern swamps, seeds of Eleocharis cellulosa germinated in flooded conditions, but did not germinate in non-flooded conditions. The strong relationship of seed germination and density relationships with local water regime and regional climate variables suggests that the forecasting of climate change effects on swamps and other wetlands needs to consider a variety of interrelated variables to make adequate projections of the regeneration responses of species to climate change. Because regeneration is an important aspect of species maintenance and restoration, climate drying could influence the species distribution of these swamps in the future. ?? 2008 Springer Science+Business Media B.V.

Forecasting domestic water demand in the Haihe river basin under changing environment

NASA Astrophysics Data System (ADS)

Wang, Xiao-Jun; Zhang, Jian-Yun; Shahid, Shamsuddin; Xie, Yu-Xuan; Zhang, Xu

2018-02-01

A statistical model has been developed for forecasting domestic water demand in Haihe river basin of China due to population growth, technological advances and climate change. Historical records of domestic water use, climate, population and urbanization are used for the development of model. An ensemble of seven general circulation models (GCMs) namely, BCC-CSM1-1, BNU-ESM, CNRM-CM5, GISS-E2-R, MIROC-ESM, PI-ESM-LR, MRI-CGCM3 were used for the projection of climate and the changes in water demand in the Haihe River basin under Representative Concentration Pathways (RCPs) 4.5. The results showed that domestic water demand in different sub-basins of the Haihe river basin will gradually increase due to continuous increase of population and rise in temperature. It is projected to increase maximum 136.22 × 108 m3 by GCM BNU-ESM and the minimum 107.25 × 108 m3 by CNRM-CM5 in 2030. In spite of uncertainty in projection, it can be remarked that climate change and population growth would cause increase in water demand and consequently, reduce the gap between water supply and demand, which eventually aggravate the condition of existing water stress in the basin. Water demand management should be emphasized for adaptation to ever increasing water demand and mitigation of the impacts of environmental changes.

Evaluating Biodiversity Response toForecasted Land Use Change: A Case Study in the South Platte River Basin, Colorado

EPA Science Inventory

Effects of future land use change on watersheds have important management implications. Seamless, national-scale land-use-change scenarios for developed land were acquired from the U.S. Environmental Protection Agency Integrated Climate and Land Use Scenarios (lCLUS) project and...

Regional moisture balance control of landslide motion: implications for landslide forecasting in a changing climate

USGS Publications Warehouse

Coe, Jeffrey A.

2012-01-01

I correlated 12 years of annual movement of 18 points on a large, continuously moving, deep-seated landslide with a regional moisture balance index (moisture balance drought index, MBDI). I used MBDI values calculated from a combination of historical precipitation and air temperature data from A.D. 1895 to 2010, and downscaled climate projections using the Intergovernmental Panel on Climate Change A2 emissions scenario for 2011–2099. At the landslide, temperature is projected to increase ~0.5 °C/10 yr between 2011 and 2099, while precipitation decreases at a rate of ~2 mm/10 yr. Landslide movement correlated with the MBDI with integration periods of 12 and 48 months. The correlation between movement and MBDI suggests that the MBDI functions as a proxy for groundwater pore pressures and landslide mobility. I used the correlation to forecast decreasing landslide movement between 2011 and 2099, with the head of the landslide expected to stop moving in the mid-21st century. The MBDI, or a similar moisture balance index that accounts for evapotranspiration, has considerable potential as a tool for forecasting the magnitude of ongoing deep-seated landslide movement, and for assessing the onset or likelihood of regional, deep-seated landslide activity.

Seasonal Water Balance Forecasts for Drought Early Warning in Ethiopia

NASA Astrophysics Data System (ADS)

Spirig, Christoph; Bhend, Jonas; Liniger, Mark

2016-04-01

Droughts severely impact Ethiopian agricultural production. Successful early warning for drought conditions in the upcoming harvest season therefore contributes to better managing food shortages arising from adverse climatic conditions. So far, however, meteorological seasonal forecasts have not been used in Ethiopia's national food security early warning system (i.e. the LEAP platform). Here we analyse the forecast quality of seasonal forecasts of total rainfall and of the meteorological water balance as a proxy for plant available water. We analyse forecast skill of June to September rainfall and water balance from dynamical seasonal forecast systems, the ECMWF System4 and EC-EARTH global forecasting systems. Rainfall forecasts outperform forecasts assuming a stationary climate mainly in north-eastern Ethiopia - an area that is particularly vulnerable to droughts. Forecasts of the water balance index seem to be even more skilful and thus more useful than pure rainfall forecasts. The results vary though for different lead times and skill measures employed. We further explore the potential added value of dynamically downscaling the forecasts through several dynamical regional climate models made available through the EU FP7 project EUPORIAS. Preliminary results suggest that dynamically downscaled seasonal forecasts are not significantly better compared with seasonal forecasts from the global models. We conclude that seasonal forecasts of a simple climate index such as the water balance have the potential to benefit drought early warning in Ethiopia, both due to its positive predictive skill and higher usefulness than seasonal mean quantities.

Abrupt climate-independent fire regime changes

USGS Publications Warehouse

Pausas, Juli G.; Keeley, Jon E.

2014-01-01

Wildfires have played a determining role in distribution, composition and structure of many ecosystems worldwide and climatic changes are widely considered to be a major driver of future fire regime changes. However, forecasting future climatic change induced impacts on fire regimes will require a clearer understanding of other drivers of abrupt fire regime changes. Here, we focus on evidence from different environmental and temporal settings of fire regimes changes that are not directly attributed to climatic changes. We review key cases of these abrupt fire regime changes at different spatial and temporal scales, including those directly driven (i) by fauna, (ii) by invasive plant species, and (iii) by socio-economic and policy changes. All these drivers might generate non-linear effects of landscape changes in fuel structure; that is, they generate fuel changes that can cross thresholds of landscape continuity, and thus drastically change fire activity. Although climatic changes might contribute to some of these changes, there are also many instances that are not primarily linked to climatic shifts. Understanding the mechanism driving fire regime changes should contribute to our ability to better assess future fire regimes.

The interactive effects of climate change, riparian management, and a non-native predators on stream-rearing salmon

USGS Publications Warehouse

Lawrence, David J.; Stewart-Koster, Ben; Olden, Julian D.; Ruesch, Aaron S.; Torgersen, Christian E.; Lawler, Joshua J.; Butcher, Don P.; Crown, Julia K.

2014-01-01

Predicting how climate change is likely to interact with myriad other stressors that threaten species of conservation concern is an essential challenge in aquatic ecosystems. This study provides a framework to accomplish this task in salmon-bearing streams of the northwestern United States, where land-use related reductions in riparian shading have caused changes in stream thermal regimes, and additional warming from projected climate change may result in significant losses of coldwater fish habitat over the next century. Predatory non-native smallmouth bass have also been introduced into many northwestern streams and their range is likely to expand as streams warm, presenting an additional challenge to the persistence of threatened Pacific salmon. The goal of this work was to forecast the interactive effects of climate change, riparian management, and non-native species on stream-rearing salmon, and to evaluate the capacity of restoration to mitigate these effects. We intersected downscaled global climate forecasts with a local-scale water temperature model to predict mid- and end-of-century temperatures in streams in the Columbia River basin; we compared one stream that is thermally impaired due to the loss of riparian vegetation and another that is cooler and has a largely intact riparian corridor. Using the forecasted stream temperatures in conjunction with fish-habitat models, we predicted how stream-rearing Chinook salmon and bass distributions would change as each stream warmed. In the highly modified stream, end-of-century warming may cause near total loss of Chinook salmon rearing habitat and a complete invasion of the upper watershed by bass. In the less modified stream, bass were thermally restricted from the upstream-most areas. In both systems, temperature increases resulted in higher predicted spatial overlap between stream-rearing Chinook salmon and potentially predatory bass in the early summer (2-4-fold increase) and greater abundance of bass. We found that riparian restoration could prevent the extirpation of Chinook salmon from the more altered stream, and could also restrict bass from occupying the upper 31 km of salmon rearing habitat. The proposed methodology and model predictions are critical for prioritizing climate-change adaptation strategies before salmonids are exposed to both warmer water and greater predation risk by non-native species.

The interactive effects of climate change, riparian management, and a nonnative predator on stream-rearing salmon.

PubMed

Lawrence, David J; Stewart-Koster, Ben; Olden, Julian D; Ruesch, Aaron S; Torgersen, Christian E; Lawler, Joshua J; Butcher, Don P; Crown, Julia K

2014-06-01

Predicting how climate change is likely to interact with myriad other stressors that threaten species of conservation concern is an essential challenge in aquatic ecosystems. This study provides a framework to accomplish this task in salmon-bearing streams of the northwestern United States, where land-use-related reductions in riparian shading have caused changes in stream thermal regimes, and additional warming from projected climate change may result in significant losses of coldwater fish habitat over the next century. Predatory, nonnative smallmouth bass have also been introduced into many northwestern streams, and their range is likely to expand as streams warm, presenting an additional challenge to the persistence of threatened Pacific salmon. The goal of this work was to forecast the interactive effects of climate change, riparian management, and nonnative species on stream-rearing salmon and to evaluate the capacity of restoration to mitigate these effects. We intersected downscaled global climate forecasts with a local-scale water temperature model to predict mid- and end-of-century temperatures in streams in the Columbia River basin. We compared one stream that is thermally impaired due to the loss of riparian vegetation and another that is cooler and has a largely intact riparian corridor. Using the forecasted stream temperatures in conjunction with fish-habitat models, we predicted how stream-rearing chinook salmon and bass distributions would change as each stream warmed. In the highly modified stream, end-of-century warming may cause near total loss of chinook salmon-rearing habitat and a complete invasion of the upper watershed by bass. In the less modified stream, bass were thermally restricted from the upstream-most areas. In both systems, temperature increases resulted in higher predicted spatial overlap between stream-rearing chinook salmon and potentially predatory bass in the early summer (two- to fourfold increase) and greater abundance of bass. We found that riparian restoration could prevent the extirpation of chinook salmon from the more altered stream and could also restrict bass from occupying the upper 31 km of salmon-rearing habitat. The proposed methodology and model predictions are critical for prioritizing climate-change adaptation strategies before salmonids are exposed to both warmer water and greater predation risk by nonnative species.

Methodology of risk assessment of loss of water resources due to climate changes

NASA Astrophysics Data System (ADS)

Israfilov, Yusif; Israfilov, Rauf; Guliyev, Hatam; Afandiyev, Galib

2016-04-01

For sustainable development and management of rational use of water resources of Azerbaijan Republic it is actual to forecast their changes taking into account different scenarios of climate changes and assessment of possible risks of loss of sections of water resources. The major part of the Azerbaijani territory is located in the arid climate and the vast majority of water is used in the national economic production. An optimal use of conditional groundwater and surface water is of great strategic importance for economy of the country in terms of lack of common water resources. Low annual rate of sediments, high evaporation and complex natural and hydrogeological conditions prevent sustainable formation of conditioned resources of ground and surface water. In addition, reserves of fresh water resources are not equally distributed throughout the Azerbaijani territory. The lack of the common water balance creates tension in the rational use of fresh water resources in various sectors of the national economy, especially in agriculture, and as a result, in food security of the republic. However, the fresh water resources of the republic have direct proportional dependence on climatic factors. 75-85% of the resources of ground stratum-pore water of piedmont plains and fracture-vein water of mountain regions are formed by the infiltration of rainfall and condensate water. Changes of climate parameters involve changes in the hydrological cycle of the hydrosphere and as a rule, are reflected on their resources. Forecasting changes of water resources of the hydrosphere with different scenarios of climate change in regional mathematical models allowed estimating the extent of their relationship and improving the quality of decisions. At the same time, it is extremely necessary to obtain additional data for risk assessment and management to reduce water resources for a detailed analysis, forecasting the quantitative and qualitative parameters of resources, and also for optimization the use of water resources. In this regard, we have developed the methodology of risk assessment including statistical fuzzy analysis of the relationship "probability-consequences", classification of probabilities, the consequences on degree of severity and risk. The current methodology allow providing the possibility of practical use of the obtained results and giving effectual help in the sustainable development and reduction of risk degree of optimal use of water resources of the republic and, as a consequence, the national strategy of economic development.

Soil-mediated effects of global change on plant communities depend on plant growth form

USDA-ARS?s Scientific Manuscript database

(1) Understanding why species respond to climate change is critical for forecasting invasions, diversity, and productivity of communities. Although researchers often predict species’ distributions and productivity based on direct physiological responses to environments, theory suggests that striking...

Smooth Sailing for Weather Forecasting

NASA Technical Reports Server (NTRS)

2002-01-01

Through a cooperative venture with NASA's Stennis Space Center, WorldWinds, Inc., developed a unique weather and wave vector map using space-based radar satellite information and traditional weather observations. Called WorldWinds, the product provides accurate, near real-time, high-resolution weather forecasts. It was developed for commercial and scientific users. In addition to weather forecasting, the product's applications include maritime and terrestrial transportation, aviation operations, precision farming, offshore oil and gas operations, and coastal hazard response support. Target commercial markets include the operational maritime and aviation communities, oil and gas providers, and recreational yachting interests. Science applications include global long-term prediction and climate change, land-cover and land-use change, and natural hazard issues. Commercial airlines have expressed interest in the product, as it can provide forecasts over remote areas. WorldWinds, Inc., is currently providing its product to commercial weather outlets.

WSN system design by using an innovative neural network model to perform thermals forecasting in a urban canyon scenario

NASA Astrophysics Data System (ADS)

Giuseppina, Nicolosi; Salvatore, Tirrito

2015-12-01

Wireless Sensor Networks (WSNs) were studied by researchers in order to manage Heating, Ventilating and Air-Conditioning (HVAC) indoor systems. WSN can be useful specially to regulate indoor confort in a urban canyon scenario, where the thermal parameters vary rapidly, influenced by outdoor climate changing. This paper shows an innovative neural network approach, by using WSN data collected, in order to forecast the indoor temperature to varying the outdoor conditions based on climate parameters and boundary conditions typically of urban canyon. In this work more attention will be done to influence of traffic jam and number of vehicles in queue.

Seasonal Prediction of Hydro-Climatic Extremes in the Greater Horn of Africa Under Evolving Climate Conditions to Support Adaptation Strategies

NASA Astrophysics Data System (ADS)

Tadesse, T.; Zaitchik, B. F.; Habib, S.; Funk, C. C.; Senay, G. B.; Dinku, T.; Policelli, F. S.; Block, P.; Baigorria, G. A.; Beyene, S.; Wardlow, B.; Hayes, M. J.

2014-12-01

The development of effective strategies to adapt to changes in the character of droughts and floods in Africa will rely on improved seasonal prediction systems that are robust to an evolving climate baseline and can be integrated into disaster preparedness and response. Many efforts have been made to build models to improve seasonal forecasts in the Greater Horn of Africa region (GHA) using satellite and climate data, but these efforts and models must be improved and translated into future conditions under evolving climate conditions. This has considerable social significance, but is challenged by the nature of climate predictability and the adaptability of coupled natural and human systems facing exposure to climate extremes. To address these issues, work is in progress under a project funded by NASA. The objectives of the project include: 1) Characterize and explain large-scale drivers in the ocean-atmosphere-land system associated with years of extreme flood or drought in the GHA. 2) Evaluate the performance of state-of-the-art seasonal forecast methods for prediction of decision-relevant metrics of hydrologic extremes. 3) Apply seasonal forecast systems to prediction of socially relevant impacts on crops, flood risk, and economic outcomes, and assess the value of these predictions to decision makers. 4) Evaluate the robustness of seasonal prediction systems to evolving climate conditions. The National Drought Mitigation Center (University of Nebraska-Lincoln, USA) is leading this project in collaboration with the USGS, Johns Hopkins University, University of Wisconsin-Madison, the International Research Institute for Climate and Society, NASA, and GHA local experts. The project is also designed to have active engagement of end users in various sectors, university researchers, and extension agents in GHA through workshops and/or webinars. This project is expected improve and implement new and existing climate- and remote sensing-based agricultural, meteorological, and hydrologic drought and flood monitoring products (or indicators) that can enhance the preparedness for extreme climate events and climate change adaptation and mitigation strategies in the GHA. Even though this project is in its first year, the preliminary results and future plans to carry out the objectives will be presented.

The potential of air-sea interactions for improving summertime North Atlantic seasonal forecasts

NASA Astrophysics Data System (ADS)

Ossó, Albert; Shaffrey, Len; Dong, Buwen; Sutton, Rowan

2017-04-01

Delivering skillful summertime seasonal forecasts of the Northern Hemisphere (NH) mid-latitude climate is a key unresolved issue for the climate science community. Current climate models have some skill in forecasting the wintertime NH mid-latitude circulation but very limited skill during summertime. To explore the potential predictability of the summertime climate we analyze lagged correlation patterns between the SSTs and summer atmospheric circulation in the North Atlantic both in observations and climate model outputs. We find observational evidence in the ERA-Interim (1979-2015) reanalysis and the HadSLP2 and HadISST data of an SST pattern forced by late winter atmospheric circulation persisting from winter to early summer that excites an anticyclonic summer SLP anomaly west of the British Isles. We show that the atmospheric response is driven through the action of turbulent heat fluxes and changes on the background baroclinicity. The lagged atmospheric response to the SSTs could be exploited for summertime predictability over Western Europe. We find a statistical significant correlation of over 0.6 between April-May North Atlantic SSTs and the June-August North Atlantic SLP anomaly. The previous findings are further explored using 120 years of coupled ocean-atmosphere HadGEM3-GC2 model simulation. The climate model qualitatively reproduces the observed spatial relationship between the late winter and spring SSTs and summertime circulation, although the correlations are substantially weaker than observed.

Forecasted range shifts of arid-land fishes in response to climate change

USGS Publications Warehouse

Whitney, James E.; Whittier, Joanna B.; Paukert, Craig P.; Olden, Julian D.; Strecker, Angela L.

2017-01-01

Climate change is poised to alter the distributional limits, center, and size of many species. Traits may influence different aspects of range shifts, with trophic generality facilitating shifts at the leading edge, and greater thermal tolerance limiting contractions at the trailing edge. The generality of relationships between traits and range shifts remains ambiguous however, especially for imperiled fishes residing in xeric riverscapes. Our objectives were to quantify contemporary fish distributions in the Lower Colorado River Basin, forecast climate change by 2085 using two general circulation models, and quantify shifts in the limits, center, and size of fish elevational ranges according to fish traits. We examined relationships among traits and range shift metrics either singly using univariate linear modeling or combined with multivariate redundancy analysis. We found that trophic and dispersal traits were associated with shifts at the leading and trailing edges, respectively, although projected range shifts were largely unexplained by traits. As expected, piscivores and omnivores with broader diets shifted upslope most at the leading edge while more specialized invertivores exhibited minimal changes. Fishes that were more mobile shifted upslope most at the trailing edge, defying predictions. No traits explained changes in range center or size. Finally, current preference explained multivariate range shifts, as fishes with faster current preferences exhibited smaller multivariate changes. Although range shifts were largely unexplained by traits, more specialized invertivorous fishes with lower dispersal propensity or greater current preference may require the greatest conservation efforts because of their limited capacity to shift ranges under climate change.

Responding to the Consequences of Climate Change

NASA Technical Reports Server (NTRS)

Hildebrand, Peter H.

2011-01-01

The talk addresses the scientific consensus concerning climate change, and outlines the many paths that are open to mitigate climate change and its effects on human activities. Diverse aspects of the changing water cycle on Earth are used to illustrate the reality climate change. These include melting snowpack, glaciers, and sea ice; changes in runoff; rising sea level; moving ecosystems, an more. Human forcing of climate change is then explained, including: greenhouse gasses, atmospheric aerosols, and changes in land use. Natural forcing effects are briefly discussed, including volcanoes and changes in the solar cycle. Returning to Earth's water cycle, the effects of climate-induced changes in water resources is presented. Examples include wildfires, floods and droughts, changes in the production and availability of food, and human social reactions to these effects. The lk then passes to a discussion of common human reactions to these forecasts of climate change effects, with a summary of recent research on the subject, plus several recent historical examples of large-scale changes in human behavior that affect the climate and ecosystems. Finally, in the face for needed action on climate, the many options for mitigation of climate change and adaptation to its effects are presented, with examples of the ability to take affordable, and profitable action at most all levels, from the local, through national.

Achieving the NOAA Arctic Action Plan: The Missing Permafrost Element - Permafrost Forecasting Listening Session Results

NASA Astrophysics Data System (ADS)

Buxbaum, T. M.; Thoman, R.; Romanovsky, V. E.

2015-12-01

Permafrost is ground at or below freezing for at least two consecutive years. It currently occupies 80% of Alaska. Permafrost temperature and active layer thickness (ALT) are key climatic variables for monitoring permafrost conditions. Active layer thickness is the depth that the top layer of ground above the permafrost thaws each summer season and permafrost temperature is the temperature of the frozen permafrost under this active layer. Knowing permafrost conditions is key for those individuals working and living in Alaska and the Arctic. The results of climate models predict vast changes and potential permafrost degradation across Alaska and the Arctic. NOAA is working to implement its 2014 Arctic Action Plan and permafrost forecasting is a missing piece of this plan. The Alaska Center for Climate Assessment and Policy (ACCAP), using our webinar software and our diverse network of statewide stakeholder contacts, hosted a listening session to bring together a select group of key stakeholders. During this listening session the National Weather Service (NWS) and key permafrost researchers explained what is possible in the realm of permafrost forecasting and participants had the opportunity to discuss and share with the group (NWS, researchers, other stakeholders) what is needed for usable permafrost forecasting. This listening session aimed to answer the questions: Is permafrost forecasting needed? If so, what spatial scale is needed by stakeholders? What temporal scales do stakeholders need/want? Are there key times (winter, fall freeze-up, etc.) or locations (North Slope, key oil development areas, etc.) where forecasting would be most applicable and useful? Are there other considerations or priority needs we haven't thought of regarding permafrost forecasting? This presentation will present the results of that listening session.

Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling

NASA Astrophysics Data System (ADS)

Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè

2017-04-01

Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.

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.

Hindcasting and forecasting of climatology for Gilbert Bay, Labrador: A marine protected area

NASA Astrophysics Data System (ADS)

Best, Sara J.

Gilbert Bay is a marine protected area (MPA) on the southeastern coast of Labrador, Canada. The MPA was created to conserve a genetically distinctive population of Atlantic cod, Gadus morhua. Future climate change in the region is expected to have an impact on the coastal marine environment and local communities in the future. This thesis presents results from a hindcast and forecasts study of physical oceanographic conditions for Gilbert Bay. The first section of this thesis examines the interannual variability in atmospheric and physical oceanographic characteristics of Gilbert Bay over the period 1949-2006. The seasonal and interannual variability of the near surface atmospheric parameters are described. Seawater temperature, salinity and sea-ice thickness in winter are simulated with a physical ocean model, the General Ocean Turbulence Model (GOTM). The results of the hindcast model suggest that the atmospheric interannual variability of the Gilbert Bay region is linked to the North Atlantic Oscillation (NAO). A warming trend observed in the subpolar North Atlantic was influenced by the local climate of coastal Labrador during the recent decade of 1995-2005. The second section of this thesis presents a model forecast of the impact of climate change on the physical conditions within Gilbert Bay over the next century. Climate scenarios from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment and the US Climate Change Science Program Project (US CCSP), specifically the Special Report on Emission Scenarios (SRES), were used. Atmospheric parameters and related changes in seawater temperature, salinity and sea-ice thickness in winter for three SRES are simulated with the GOTM, and are then compared to the hindcast study results. The results suggest that the water column during future winters will become warmer in the second half of the 21st century. In the summer the atmosphere will be warmer and more humid. Cloudiness and precipitation are expected to increase. This will have an impact on the vertical stratification of the water column. The surface mixed layer is expected to become warmer, fresher and much shallower than seen in the past. The stratification below the seasonal thermocline will weaken and vertical mixing will intensify. A significant change in surface sea-ice coverage is also suggested by the forecast. Continuing reduction in sea-ice formation during the winter months as highlighted by the hindcast study is expected to affect living conditions of the neighbouring coastal communities around the bay, specifically by increasing the danger of travelling across the bay. A warming Gilbert Bay ecosystem may be favourable for cod growth, but reduced sea-ice formation during the winter months increases the danger of travelling across the bay by snowmobile.

Northern Hemisphere climate trends in reanalysis and forecast model predictions: The 500 hPa annual means

NASA Astrophysics Data System (ADS)

Bordi, I.; Fraedrich, K.; Sutera, A.

2010-06-01

The lead time dependent climates of the ECMWF weather prediction model, initialized with ERA-40 reanalysis, are analysed using 44 years of day-1 to day-10 forecasts of the northern hemispheric 500-hPa geopotential height fields. The study addresses the question whether short-term tendencies have an impact on long-term trends. Comparing climate trends of ERA-40 with those of the forecasts, it seems that the forecast model rapidly loses the memory of initial conditions creating its own climate. All forecast trends show a high degree of consistency. Comparison results suggest that: (i) Only centers characterized by an upward trend are statistical significant when increasing the lead time. (ii) In midilatitudes an upward trend larger than the one observed in the reanalysis characterizes the forecasts, while in the tropics there is a good agreement. (iii) The downward trend in reanalysis at high latitudes characterizes also the day-1 forecast which, however, increasing lead time approaches zero.

The Value of Seasonal Climate Forecasts in Managing Energy Resources.

NASA Astrophysics Data System (ADS)

Brown Weiss, Edith

1982-04-01

Research and interviews with officials of the United States energy industry and a systems analysis of decision making in a natural gas utility lead to the conclusion that seasonal climate forecasts would only have limited value in fine tuning the management of energy supply, even if the forecasts were more reliable and detailed than at present.On the other hand, reliable forecasts could be useful to state and local governments both as a signal to adopt long-term measures to increase the efficiency of energy use and to initiate short-term measures to reduce energy demand in anticipation of a weather-induced energy crisis.To be useful for these purposes, state governments would need better data on energy demand patterns and available energy supplies, staff competent to interpret climate forecasts, and greater incentive to conserve. The use of seasonal climate forecasts is not likely to be constrained by fear of legal action by those claiming to be injured by a possible incorrect forecast.

New Satellite Constellation Uses Radio Occultation to Monitor Space Weather

NASA Astrophysics Data System (ADS)

Kumar, Mohi

2006-05-01

A constellation of six satellites, expected to enhance space weather research, improve terrestrial meteorology forecasts, and monitor climate change, were launched 15 April from Vandenberg Air Force Base, Calif.

Visualizing the uncertainty in the relationship between seasonal average climate and malaria risk.

PubMed

MacLeod, D A; Morse, A P

2014-12-02

Around $1.6 billion per year is spent financing anti-malaria initiatives, and though malaria morbidity is falling, the impact of annual epidemics remains significant. Whilst malaria risk may increase with climate change, projections are highly uncertain and to sidestep this intractable uncertainty, adaptation efforts should improve societal ability to anticipate and mitigate individual events. Anticipation of climate-related events is made possible by seasonal climate forecasting, from which warnings of anomalous seasonal average temperature and rainfall, months in advance are possible. Seasonal climate hindcasts have been used to drive climate-based models for malaria, showing significant skill for observed malaria incidence. However, the relationship between seasonal average climate and malaria risk remains unquantified. Here we explore this relationship, using a dynamic weather-driven malaria model. We also quantify key uncertainty in the malaria model, by introducing variability in one of the first order uncertainties in model formulation. Results are visualized as location-specific impact surfaces: easily integrated with ensemble seasonal climate forecasts, and intuitively communicating quantified uncertainty. Methods are demonstrated for two epidemic regions, and are not limited to malaria modeling; the visualization method could be applied to any climate impact.

Visualizing the uncertainty in the relationship between seasonal average climate and malaria risk

NASA Astrophysics Data System (ADS)

MacLeod, D. A.; Morse, A. P.

2014-12-01

Around $1.6 billion per year is spent financing anti-malaria initiatives, and though malaria morbidity is falling, the impact of annual epidemics remains significant. Whilst malaria risk may increase with climate change, projections are highly uncertain and to sidestep this intractable uncertainty, adaptation efforts should improve societal ability to anticipate and mitigate individual events. Anticipation of climate-related events is made possible by seasonal climate forecasting, from which warnings of anomalous seasonal average temperature and rainfall, months in advance are possible. Seasonal climate hindcasts have been used to drive climate-based models for malaria, showing significant skill for observed malaria incidence. However, the relationship between seasonal average climate and malaria risk remains unquantified. Here we explore this relationship, using a dynamic weather-driven malaria model. We also quantify key uncertainty in the malaria model, by introducing variability in one of the first order uncertainties in model formulation. Results are visualized as location-specific impact surfaces: easily integrated with ensemble seasonal climate forecasts, and intuitively communicating quantified uncertainty. Methods are demonstrated for two epidemic regions, and are not limited to malaria modeling; the visualization method could be applied to any climate impact.

Arctic shrubification mediates the impacts of warming climate on changes to tundra vegetation

NASA Astrophysics Data System (ADS)

Mod, Heidi K.; Luoto, Miska

2016-12-01

Climate change has been observed to expand distributions of woody plants in many areas of arctic and alpine environments—a phenomenon called shrubification. New spatial arrangements of shrubs cause further changes in vegetation via changing dynamics of biotic interactions. However, the mediating influence of shrubification is rarely acknowledged in predictions of tundra vegetation change. Here, we examine possible warming-induced landscape-level vegetation changes in a high-latitude environment using species distribution modelling (SDM), specifically concentrating on the impacts of shrubification on ambient vegetation. First, we produced estimates of current shrub and tree cover and forecasts of their expansion under climate change scenarios to be incorporated to SDMs of 116 vascular plants. Second, the predictions of vegetation change based on the models including only abiotic predictors and the models including abiotic, shrub and tree predictors were compared in a representative test area. Based on our model predictions, abundance of woody plants will expand, thus decreasing predicted species richness, amplifying species turnover and increasing the local extinction risk for ambient vegetation. However, the spatial variation demonstrated in our predictions highlights that tundra vegetation can be expected to show a wide variety of different responses to the combined effects of warming and shrubification, depending on the original plant species pool and environmental conditions. We conclude that realistic forecasts of the future require acknowledging the role of shrubification in warming-induced tundra vegetation change.

Forecast Mekong: navigating changing waters

USGS Publications Warehouse

Powell, Janine

2011-01-01

The U.S. Geological Survey (USGS) is using research and data from the Mekong River Delta in Southeast Asia to compare restoration, conservation, and management efforts there with those done in other major river deltas, such as the Mississippi River Delta in the United States. The project provides a forum to engage regional partners in the Mekong Basin countries to share data and support local research efforts. Ultimately, Forecast Mekong will lead to more informed decisions about how to make the Mekong and Mississippi Deltas resilient in the face of climate change, economic stresses, and other impacts.

Breaks in MODIS time series portend vegetation change: verification using long-term data in an arid grassland ecosystem

USDA-ARS?s Scientific Manuscript database

Frequency and severity of extreme climatic events are forecast to increase in the 21st century. Predicting how managed ecosystems may respond to climatic extremes is intensified by uncertainty associated with knowing when, where, and how long effects of the extreme events will be manifest in the eco...

Using NMME in Region-Specific Operational Seasonal Climate Forecasts

NASA Astrophysics Data System (ADS)

Gronewold, A.; Bolinger, R. A.; Fry, L. M.; Kompoltowicz, K.

2015-12-01

The National Oceanic and Atmospheric Administration's Climate Prediction Center (NOAA/CPC) provides access to a suite of real-time monthly climate forecasts that comprise the North American Multi-Model Ensemble (NMME) in an attempt to meet increasing demands for monthly to seasonal climate prediction. While the graphical map forecasts of the NMME are informative, there is a need to provide decision-makers with probabilistic forecasts specific to their region of interest. Here, we demonstrate the potential application of the NMME to address regional climate projection needs by developing new forecasts of temperature and precipitation for the North American Great Lakes, the largest system of lakes on Earth. Regional opertional water budget forecasts rely on these outlooks to initiate monthly forecasts not only of the water budget, but of monthly lake water levels as well. More specifically, we present an alternative for improving existing operational protocols that currently involve a relatively time-consuming and subjective procedure based on interpreting the maps of the NMME. In addition, all forecasts are currently presented in the NMME in a probabilistic format, with equal weighting given to each member of the ensemble. In our new evolution of this product, we provide historical context for the forecasts by superimposing them (in an on-line graphical user interface) with the historical range of observations. Implementation of this new tool has already led to noticeable advantages in regional water budget forecasting, and has the potential to be transferred to other regional decision-making authorities as well.

The Importance of Hurricane Research to Life, Property, the Economy, and National Security.

NASA Astrophysics Data System (ADS)

Busalacchi, A. J.

2017-12-01

The devastating 2017 Atlantic hurricane season has brought into stark relief how much hurricane forecasts have improved - and how important it is to make them even better. Whereas the error in 48-hour track forecasts has been reduced by more than half, according to the National Hurricane Center, intensity forecasts remain challenging, especially with storms such as Harvey that strengthened from a tropical depression to a Category 4 hurricane in less than three days. The unusually active season, with Hurricane Irma sustaining 185-mph winds for a record 36 hours and two Atlantic hurricanes reaching 150-mph winds simultaneously for the first time, also highlighted what we do, and do not, know about how tropical cyclones will change as the climate warms. The extraordinary toll of Hurricanes Harvey, Irma, and Maria - which may ultimately be responsible for hundreds of deaths and an estimated $200 billion or more in damages - underscores why investments into improved forecasting must be a national priority. At NCAR and UCAR, scientists are working with their colleagues at federal agencies, the private sector, and the university community to advance our understanding of these deadly storms. Among their many projects, NCAR researchers are making experimental tropical cyclone forecasts using an innovative Earth system model that allows for variable resolution. We are working with NOAA to issue flooding, inundation, and streamflow forecasts for areas hit by hurricanes, and we have used extremely high-resolution regional models to simulate successfully the rapid hurricane intensification that has proved so difficult to predict. We are assessing ways to better predict the damage potential of tropical cyclones by looking beyond wind speed to consider such important factors as the size and forward motion of the storm. On the important question of climate change, scientists have experimented with running coupled climate models at a high enough resolution to spin up a hurricane, and we have used a convection-permitting regional model to examine how named storms of the past might look if they were to formed in a warmer, wetter future. Finally, research is also being performed to better communicate forecasts to help residents make informed choices when a damaging storm approaches.

Finding "Models" in Clouds

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

Leung, Ruby

2017-05-01

Internationally recognized Climate Scientist Ruby Leung is a cloud gazer. But rather than looking for shapes, Ruby’s life’s calling is to develop regional atmospheric models to better predict and understand the effects of global climate change at scales relevant to humans and the environment. Ruby’s accomplishments include developing novel methods for modeling mountain clouds and precipitation in climate models, and improving understanding of hydroclimate variability and change. She also has led efforts to develop regional climate modeling capabilities in the Weather Research and Forecasting model that is widely adopted by scientists worldwide. Ruby is part of a team of PNNLmore » researchers studying the impacts of global warming.« less

NASA Products to Enhance Energy Utility Load Forecasting

NASA Technical Reports Server (NTRS)

Lough, G.; Zell, E.; Engel-Cox, J.; Fungard, Y.; Jedlovec, G.; Stackhouse, P.; Homer, R.; Biley, S.

2012-01-01

Existing energy load forecasting tools rely upon historical load and forecasted weather to predict load within energy company service areas. The shortcomings of load forecasts are often the result of weather forecasts that are not at a fine enough spatial or temporal resolution to capture local-scale weather events. This project aims to improve the performance of load forecasting tools through the integration of high-resolution, weather-related NASA Earth Science Data, such as temperature, relative humidity, and wind speed. Three companies are participating in operational testing one natural gas company, and two electric providers. Operational results comparing load forecasts with and without NASA weather forecasts have been generated since March 2010. We have worked with end users at the three companies to refine selection of weather forecast information and optimize load forecast model performance. The project will conclude in 2012 with transitioning documented improvements from the inclusion of NASA forecasts for sustained use by energy utilities nationwide in a variety of load forecasting tools. In addition, Battelle has consulted with energy companies nationwide to document their information needs for long-term planning, in light of climate change and regulatory impacts.

Potential effects of climate change on aquatic ecosystems of the Great Plains of North America

USGS Publications Warehouse

Covich, A.P.; Fritz, S.C.; Lamb, P.J.; Marzolf, R.D.; Matthews, W.J.; Poiani, K.A.; Prepas, E.E.; Richman, M.B.; Winter, T.C.

1997-01-01

The Great Plains landscape is less topographically complex than most other regions within North America, but diverse aquatic ecosystems, such as playas, pothole lakes, ox-bow lakes, springs, groundwater aquifers, intermittent and ephemeral streams, as well as large rivers and wetlands, are highly dynamic and responsive to extreme climatic fluctuations. We review the evidence for climatic change that demonstrates the historical importance of extremes in north-south differences in summer temperatures and east-west differences in aridity across four large subregions. These physical driving forces alter density stratification, deoxygenation, decomposition and salinity. Biotic community composition and associated ecosystem processes of productivity and nutrient cycling respond rapidly to these climatically driven dynamics. Ecosystem processes also respond to cultural effects such as dams and diversions of water for irrigation, waste dilution and urban demands for drinking water and industrial uses. Distinguishing climatic from cultural effects in future models of aquatic ecosystem functioning will require more refinement in both climatic and economic forecasting. There is a need, for example, to predict how long-term climatic forecasts (based on both ENSO and global warming simulations) relate to the permanence and productivity of shallow water ecosystems. Aquatic ecologists, hydrologists, climatologists and geographers have much to discuss regarding the synthesis of available data and the design of future interdisciplinary research. ?? 1997 by John Wiley & Sons, Ltd.

How seasonal forecast could help a decision maker: an example of climate service for water resource management

NASA Astrophysics Data System (ADS)

Viel, Christian; Beaulant, Anne-Lise; Soubeyroux, Jean-Michel; Céron, Jean-Pierre

2016-04-01

The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

On a collision course: competition and dispersal differences create no-analogue communities and cause extinctions during climate change

PubMed Central

Urban, Mark C.; Tewksbury, Josh J.; Sheldon, Kimberly S.

2012-01-01

Most climate change predictions omit species interactions and interspecific variation in dispersal. Here, we develop a model of multiple competing species along a warming climatic gradient that includes temperature-dependent competition, differences in niche breadth and interspecific differences in dispersal ability. Competition and dispersal differences decreased diversity and produced so-called ‘no-analogue’ communities, defined as a novel combination of species that does not currently co-occur. Climate change altered community richness the most when species had narrow niches, when mean community-wide dispersal rates were low and when species differed in dispersal abilities. With high interspecific dispersal variance, the best dispersers tracked climate change, out-competed slower dispersers and caused their extinction. Overall, competition slowed the advance of colonists into newly suitable habitats, creating lags in climate tracking. We predict that climate change will most threaten communities of species that have narrow niches (e.g. tropics), vary in dispersal (most communities) and compete strongly. Current forecasts probably underestimate climate change impacts on biodiversity by neglecting competition and dispersal differences. PMID:22217718

Researchers focus attention on coastal response to climate change

NASA Astrophysics Data System (ADS)

Anderson, John; Rodriguez, Antonio; Fletcher, Charles; Fitzgerald, Duncan

The world's population has been steadily migrating toward coastal cities, resulting in severe stress on coastal environments. But the most severe human impact on coastal regions may lie ahead as the rate of global sea-level rise accelerates and the impacts of global warming on coastal climates and oceanographic dynamics increase [Varekamp and Thomas, 1998; Hinrichsen, 1999; Goodwin et al., 2000]. Little is currently being done to forecast the impact of global climate change on coasts during the next century and beyond. Indeed, there are still many politicians, and even some scientists, who doubt that global change is a real threat to society.

Understanding land use change impacts on microclimate using Weather Research and Forecasting (WRF) model

DOE PAGES

Li, Xia; Mitra, Chandana; Dong, Li; ...

2017-02-02

In order to explore potential climatic consequences of land cover change in the Kolkata Metropolitan Development area, we projected microclimate conditions in this area using the Weather Research and Forecasting (WRF) model driven by future land use scenarios. Specifically, we considered two land conversion scenarios including an urbanization scenario that all the wetlands and croplands would be converted to built-up areas, and an irrigation expansion scenario in which all wetlands and dry croplands would be replaced by irrigated croplands. Our results indicated that land use and land cover (LULC) change would dramatically increase regional temperature in this area under themore » urbanization scenario, but expanded irrigation tended to have a cooling effect. In the urbanization scenario, precipitation center tended to move eastward and lead to increased rainfall in eastern parts of this region. Increased irrigation stimulated rainfall in central and eastern areas but reduced rainfall in southwestern and northwestern parts of the study area. Our study also demonstrated that urbanization significantly reduced latent heat fluxes and albedo of land surface; while increased sensible heat flux changes following urbanization suggested that developed land surfaces mainly acted as heat sources. In this study, climate change projection not only predicts future spatiotemporal patterns of multiple climate factors, but also provides valuable insights into policy making related to land use management, water resource management, and agriculture management to adapt and mitigate future climate changes in this populous region.« less

Understanding land use change impacts on microclimate using Weather Research and Forecasting (WRF) model

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

Li, Xia; Mitra, Chandana; Dong, Li

In order to explore potential climatic consequences of land cover change in the Kolkata Metropolitan Development area, we projected microclimate conditions in this area using the Weather Research and Forecasting (WRF) model driven by future land use scenarios. Specifically, we considered two land conversion scenarios including an urbanization scenario that all the wetlands and croplands would be converted to built-up areas, and an irrigation expansion scenario in which all wetlands and dry croplands would be replaced by irrigated croplands. Our results indicated that land use and land cover (LULC) change would dramatically increase regional temperature in this area under themore » urbanization scenario, but expanded irrigation tended to have a cooling effect. In the urbanization scenario, precipitation center tended to move eastward and lead to increased rainfall in eastern parts of this region. Increased irrigation stimulated rainfall in central and eastern areas but reduced rainfall in southwestern and northwestern parts of the study area. Our study also demonstrated that urbanization significantly reduced latent heat fluxes and albedo of land surface; while increased sensible heat flux changes following urbanization suggested that developed land surfaces mainly acted as heat sources. In this study, climate change projection not only predicts future spatiotemporal patterns of multiple climate factors, but also provides valuable insights into policy making related to land use management, water resource management, and agriculture management to adapt and mitigate future climate changes in this populous region.« less

Understanding land use change impacts on microclimate using Weather Research and Forecasting (WRF) model

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

Li, Xia; Mitra, Chandana; Dong, Li

To explore potential climatic consequences of land cover change in the Kolkata Metropolitan Development area, we projected microclimate conditions in this area using the Weather Research and Forecasting (WRF) model driven by future land use scenarios. Specifically, we considered two land conversion scenarios including an urbanization scenario that all the wetlands and croplands would be converted to built-up areas, and an irrigation expansion scenario in which all wetlands and dry croplands would be replaced by irrigated croplands. Results indicated that land use and land cover (LULC) change would dramatically increase regional temperature in this area under the urbanization scenario, butmore » expanded irrigation tended to have a cooling effect. In the urbanization scenario, precipitation center tended to move eastward and lead to increased rainfall in eastern parts of this region. Increased irrigation stimulated rainfall in central and eastern areas but reduced rainfall in southwestern and northwestern parts of the study area. This study also demonstrated that urbanization significantly reduced latent heat fluxes and albedo of land surface; while increased sensible heat flux changes following urbanization suggested that developed land surfaces mainly acted as heat sources. In this study, climate change projection not only predicts future spatiotemporal patterns of multiple climate factors, but also provides valuable insights into policy making related to land use management, water resource management, and agriculture management to adapt and mitigate future climate changes in this populous region. (C) 2017 Elsevier Ltd. All rights reserved.« less

Rapid changes in the range limits of Scots pine 4000 years ago

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

Gear, A.J.; Huntley, B.

Paleoecological data provide estimates of response rates to past climate changes. Fossil Pinus sylvestris stumps in far northern Scotland demonstrate former presence of pine trees where conventional pollen evidence of pine forests is lacking. Radiocarbon, dendrochronological, and fine temporal-resolution palynological data show that pine forest were present for about four centuries some 4,000 years ago; the forests expanded and then retreated rapidly some 70 to 80 kilometers. Despite the rapidity of this response to climate change, it occurred at rates slower by an order of magnitude than those necessary to maintain equilibrium with forecast climate changes attributed to the greenhousemore » effect.« less

Modeling sediment accumulation in North American playa wetlands in response to climate change, 1940-2100

USGS Publications Warehouse

Burris, Lucy; Skagen, Susan K.

2013-01-01

Playa wetlands on the west-central Great Plains of North America are vulnerable to sediment infilling from upland agriculture, putting at risk several important ecosystem services as well as essential habitats and food resources of diverse wetland-dependent biota. Climate predictions for this semi-arid area indicate reduced precipitation which may alter rates of erosion, runoff, and sedimentation of playas. We forecasted erosion rates, sediment depths, and resultant playa wetland depths across the west-central Great Plains and examined the relative roles of land use context and projected changes in precipitation in the sedimentation process. We estimated erosion with the Revised Universal Soil Loss Equation (RUSLE) using historic values and downscaled precipitation predictions from three general circulation models and three emissions scenarios. We calibrated RUSLE results using field sediment measurements. RUSLE is appealing for regional scale modeling because it uses climate forecasts with monthly resolution and other widely available values including soil texture, slope and land use. Sediment accumulation rates will continue near historic levels through 2070 and will be sufficient to cause most playas (if not already filled) to fill with sediment within the next 100 years in the absence of mitigation. Land use surrounding the playa, whether grassland or tilled cropland, is more influential in sediment accumulation than climate-driven precipitation change.

Climate change and wildland firefighter health and safety.

PubMed

Withen, Patrick

2015-02-01

The author examines how climate change is impacting wildland firefighters. Climate change has made wildland fires more frequent and more intense. The increase in frequency and intensity of fires has pushed the number of fatalities and injuries higher in recent decades. The most common hazards on fires follow the trend of fire in general in that these hazards become more frequent and intense. Burnovers, heat exhaustion, tree hazards, and many other common fire hazards are more likely. The fire suppression agencies are making every effort to improve health and safety on fires by improving communication, weather forecasting, mapping, fire shelters, decision making and more. Despite these efforts, wildfires are becoming ever more hazardous because of climate change and the increasing frequency and intensity of wildfires. © 2015 SAGE Publications.

Drivers and uncertainties of forecasted range shifts for warm-water fishes under climate and land cover change

USGS Publications Warehouse

Bouska, Kristen; Whitledge, Gregory W.; Lant, Christopher; Schoof, Justin

2018-01-01

Land cover is an important determinant of aquatic habitat and is projected to shift with climate changes, yet climate-driven land cover changes are rarely factored into climate assessments. To quantify impacts and uncertainty of coupled climate and land cover change on warm-water fish species’ distributions, we used an ensemble model approach to project distributions of 14 species. For each species, current range projections were compared to 27 scenario-based projections and aggregated to visualize uncertainty. Multiple regression and model selection techniques were used to identify drivers of range change. Novel, or no-analogue, climates were assessed to evaluate transferability of models. Changes in total probability of occurrence ranged widely across species, from a 63% increase to a 65% decrease. Distributional gains and losses were largely driven by temperature and flow variables and underscore the importance of habitat heterogeneity and connectivity to facilitate adaptation to changing conditions. Finally, novel climate conditions were driven by mean annual maximum temperature, which stresses the importance of understanding the role of temperature on fish physiology and the role of temperature-mitigating management practices.

Multi-agent modelling of climate outlooks and food security on a community garden scheme in Limpopo, South Africa.

PubMed

Bharwani, Sukaina; Bithell, Mike; Downing, Thomas E; New, Mark; Washington, Richard; Ziervogel, Gina

2005-11-29

Seasonal climate outlooks provide one tool to help decision-makers allocate resources in anticipation of poor, fair or good seasons. The aim of the 'Climate Outlooks and Agent-Based Simulation of Adaptation in South Africa' project has been to investigate whether individuals, who adapt gradually to annual climate variability, are better equipped to respond to longer-term climate variability and change in a sustainable manner. Seasonal climate outlooks provide information on expected annual rainfall and thus can be used to adjust seasonal agricultural strategies to respond to expected climate conditions. A case study of smallholder farmers in a village in Vhembe district, Limpopo Province, South Africa has been used to examine how such climate outlooks might influence agricultural strategies and how this climate information can be improved to be more useful to farmers. Empirical field data has been collected using surveys, participatory approaches and computer-based knowledge elicitation tools to investigate the drivers of decision-making with a focus on the role of climate, market and livelihood needs. This data is used in an agent-based social simulation which incorporates household agents with varying adaptation options which result in differing impacts on crop yields and thus food security, as a result of using or ignoring the seasonal outlook. Key variables are the skill of the forecast, the social communication of the forecast and the range of available household and community-based risk coping strategies. This research provides a novel approach for exploring adaptation within the context of climate change.

Evaluating simplistic methods to understand current distributions and forecast distribution changes under climate change scenarios: An example with coypu (Myocastor coypus)

USGS Publications Warehouse

Jarnevich, Catherine S.; Young, Nicholas E; Sheffels, Trevor R.; Carter, Jacoby; Systma, Mark D.; Talbert, Colin

2017-01-01

Invasive species provide a unique opportunity to evaluate factors controlling biogeographic distributions; we can consider introduction success as an experiment testing suitability of environmental conditions. Predicting potential distributions of spreading species is not easy, and forecasting potential distributions with changing climate is even more difficult. Using the globally invasive coypu (Myocastor coypus [Molina, 1782]), we evaluate and compare the utility of a simplistic ecophysiological based model and a correlative model to predict current and future distribution. The ecophysiological model was based on winter temperature relationships with nutria survival. We developed correlative statistical models using the Software for Assisted Habitat Modeling and biologically relevant climate data with a global extent. We applied the ecophysiological based model to several global circulation model (GCM) predictions for mid-century. We used global coypu introduction data to evaluate these models and to explore a hypothesized physiological limitation, finding general agreement with known coypu distribution locally and globally and support for an upper thermal tolerance threshold. Global circulation model based model results showed variability in coypu predicted distribution among GCMs, but had general agreement of increasing suitable area in the USA. Our methods highlighted the dynamic nature of the edges of the coypu distribution due to climate non-equilibrium, and uncertainty associated with forecasting future distributions. Areas deemed suitable habitat, especially those on the edge of the current known range, could be used for early detection of the spread of coypu populations for management purposes. Combining approaches can be beneficial to predicting potential distributions of invasive species now and in the future and in exploring hypotheses of factors controlling distributions.

Influences of Regional Climate Change on Air Quality Across the Continental U.S. Projected from Downscaling IPCC AR5 Simulations. Chapter 2

NASA Technical Reports Server (NTRS)

Nolte, Christopher; Otte, Tanya; Pinder, Robert; Bowden, J.; Herwehe, J.; Faluvegi, Gregory; Shindell, Drew

2013-01-01

Projecting climate change scenarios to local scales is important for understanding, mitigating, and adapting to the effects of climate change on society and the environment. Many of the global climate models (GCMs) that are participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture regional-scale changes in temperatures and precipitation. We use a regional climate model (RCM) to dynamically downscale the GCM's large-scale signal to investigate the changes in regional and local extremes of temperature and precipitation that may result from a changing climate. In this paper, we show preliminary results from downscaling the NASA/GISS ModelE IPCC AR5 Representative Concentration Pathway (RCP) 6.0 scenario. We use the Weather Research and Forecasting (WRF) model as the RCM to downscale decadal time slices (1995-2005 and 2025-2035) and illustrate potential changes in regional climate for the continental U.S. that are projected by ModelE and WRF under RCP6.0. The regional climate change scenario is further processed using the Community Multiscale Air Quality modeling system to explore influences of regional climate change on air quality.

Regional Climate Change across the Continental U.S. Projected from Downscaling IPCC AR5 Simulations

NASA Astrophysics Data System (ADS)

Otte, T. L.; Nolte, C. G.; Otte, M. J.; Pinder, R. W.; Faluvegi, G.; Shindell, D. T.

2011-12-01

Projecting climate change scenarios to local scales is important for understanding and mitigating the effects of climate change on society and the environment. Many of the general circulation models (GCMs) that are participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture local changes in temperature and precipitation extremes. We seek to project the GCM's large-scale climate change signal to the local scale using a regional climate model (RCM) by applying dynamical downscaling techniques. The RCM will be used to better understand the local changes of temperature and precipitation extremes that may result from a changing climate. Preliminary results from downscaling NASA/GISS ModelE simulations of the IPCC AR5 Representative Concentration Pathway (RCP) scenario 6.0 will be shown. The Weather Research and Forecasting (WRF) model will be used as the RCM to downscale decadal time slices for ca. 2000 and ca. 2030 and illustrate potential changes in regional climate for the continental U.S. that are projected by ModelE and WRF under RCP6.0.

Decadal-Scale Forecasting of Climate Drivers for Marine Applications.

PubMed

Salinger, J; Hobday, A J; Matear, R J; O'Kane, T J; Risbey, J S; Dunstan, P; Eveson, J P; Fulton, E A; Feng, M; Plagányi, É E; Poloczanska, E S; Marshall, A G; Thompson, P A

Climate influences marine ecosystems on a range of time scales, from weather-scale (days) through to climate-scale (hundreds of years). Understanding of interannual to decadal climate variability and impacts on marine industries has received less attention. Predictability up to 10 years ahead may come from large-scale climate modes in the ocean that can persist over these time scales. In Australia the key drivers of climate variability affecting the marine environment are the Southern Annular Mode, the Indian Ocean Dipole, the El Niño/Southern Oscillation, and the Interdecadal Pacific Oscillation, each has phases that are associated with different ocean circulation patterns and regional environmental variables. The roles of these drivers are illustrated with three case studies of extreme events-a marine heatwave in Western Australia, a coral bleaching of the Great Barrier Reef, and flooding in Queensland. Statistical and dynamical approaches are described to generate forecasts of climate drivers that can subsequently be translated to useful information for marine end users making decisions at these time scales. Considerable investment is still needed to support decadal forecasting including improvement of ocean-atmosphere models, enhancement of observing systems on all scales to support initiation of forecasting models, collection of important biological data, and integration of forecasts into decision support tools. Collaboration between forecast developers and marine resource sectors-fisheries, aquaculture, tourism, biodiversity management, infrastructure-is needed to support forecast-based tactical and strategic decisions that reduce environmental risk over annual to decadal time scales. © 2016 Elsevier Ltd. All rights reserved.

Direct effects dominate responses to climate perturbations in grassland plant communities.

PubMed

Chu, Chengjin; Kleinhesselink, Andrew R; Havstad, Kris M; McClaran, Mitchel P; Peters, Debra P; Vermeire, Lance T; Wei, Haiyan; Adler, Peter B

2016-06-08

Theory predicts that strong indirect effects of environmental change will impact communities when niche differences between competitors are small and variation in the direct effects experienced by competitors is large, but empirical tests are lacking. Here we estimate negative frequency dependence, a proxy for niche differences, and quantify the direct and indirect effects of climate change on each species. Consistent with theory, in four of five communities indirect effects are strongest for species showing weak negative frequency dependence. Indirect effects are also stronger in communities where there is greater variation in direct effects. Overall responses to climate perturbations are driven primarily by direct effects, suggesting that single species models may be adequate for forecasting the impacts of climate change in these communities.

Lags in the response of mountain plant communities to climate change.

PubMed

Alexander, Jake M; Chalmandrier, Loïc; Lenoir, Jonathan; Burgess, Treena I; Essl, Franz; Haider, Sylvia; Kueffer, Christoph; McDougall, Keith; Milbau, Ann; Nuñez, Martin A; Pauchard, Aníbal; Rabitsch, Wolfgang; Rew, Lisa J; Sanders, Nathan J; Pellissier, Loïc

2018-02-01

Rapid climatic changes and increasing human influence at high elevations around the world will have profound impacts on mountain biodiversity. However, forecasts from statistical models (e.g. species distribution models) rarely consider that plant community changes could substantially lag behind climatic changes, hindering our ability to make temporally realistic projections for the coming century. Indeed, the magnitudes of lags, and the relative importance of the different factors giving rise to them, remain poorly understood. We review evidence for three types of lag: "dispersal lags" affecting plant species' spread along elevational gradients, "establishment lags" following their arrival in recipient communities, and "extinction lags" of resident species. Variation in lags is explained by variation among species in physiological and demographic responses, by effects of altered biotic interactions, and by aspects of the physical environment. Of these, altered biotic interactions could contribute substantially to establishment and extinction lags, yet impacts of biotic interactions on range dynamics are poorly understood. We develop a mechanistic community model to illustrate how species turnover in future communities might lag behind simple expectations based on species' range shifts with unlimited dispersal. The model shows a combined contribution of altered biotic interactions and dispersal lags to plant community turnover along an elevational gradient following climate warming. Our review and simulation support the view that accounting for disequilibrium range dynamics will be essential for realistic forecasts of patterns of biodiversity under climate change, with implications for the conservation of mountain species and the ecosystem functions they provide. © 2017 John Wiley & Sons Ltd.

Integrating Plant Science and Crop Modeling: Assessment of the Impact of Climate Change on Soybean and Maize Production.

PubMed

Fodor, Nándor; Challinor, Andrew; Droutsas, Ioannis; Ramirez-Villegas, Julian; Zabel, Florian; Koehler, Ann-Kristin; Foyer, Christine H

2017-11-01

Increasing global CO2 emissions have profound consequences for plant biology, not least because of direct influences on carbon gain. However, much remains uncertain regarding how our major crops will respond to a future high CO2 world. Crop model inter-comparison studies have identified large uncertainties and biases associated with climate change. The need to quantify uncertainty has drawn the fields of plant molecular physiology, crop breeding and biology, and climate change modeling closer together. Comparing data from different models that have been used to assess the potential climate change impacts on soybean and maize production, future yield losses have been predicted for both major crops. When CO2 fertilization effects are taken into account significant yield gains are predicted for soybean, together with a shift in global production from the Southern to the Northern hemisphere. Maize production is also forecast to shift northwards. However, unless plant breeders are able to produce new hybrids with improved traits, the forecasted yield losses for maize will only be mitigated by agro-management adaptations. In addition, the increasing demands of a growing world population will require larger areas of marginal land to be used for maize and soybean production. We summarize the outputs of crop models, together with mitigation options for decreasing the negative impacts of climate on the global maize and soybean production, providing an overview of projected land-use change as a major determining factor for future global crop production. © The Author 2017. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.

Forecasting climate change impacts on plant populations over large spatial extents

DOE PAGES

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.; ...

2016-10-24

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates inmore » the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.« less

Forecasting climate change impacts on plant populations over large spatial extents

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

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. Here, we overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates inmore » the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Finally, our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.« less

Forecasting climate change impacts on plant populations over large spatial extents

USGS Publications Warehouse

Tredennick, Andrew T.; Hooten, Mevin B.; Aldridge, Cameron L.; Homer, Collin G.; Kleinhesselink, Andrew R.; Adler, Peter B.

2016-01-01

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. We overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush (Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.

Projected Changes on the Global Surface Wave Drift Climate towards the END of the Twenty-First Century

NASA Astrophysics Data System (ADS)

Carrasco, Ana; Semedo, Alvaro; Behrens, Arno; Weisse, Ralf; Breivik, Øyvind; Saetra, Øyvind; Håkon Christensen, Kai

2016-04-01

The global wave-induced current (the Stokes Drift - SD) is an important feature of the ocean surface, with mean values close to 10 cm/s along the extra-tropical storm tracks in both hemispheres. Besides the horizontal displacement of large volumes of water the SD also plays an important role in the ocean mix-layer turbulence structure, particularly in stormy or high wind speed areas. The role of the wave-induced currents in the ocean mix-layer and in the sea surface temperature (SST) is currently a hot topic of air-sea interaction research, from forecast to climate ranges. The SD is mostly driven by wind sea waves and highly sensitive to changes in the overlaying wind speed and direction. The impact of climate change in the global wave-induced current climate will be presented. The wave model WAM has been forced by the global climate model (GCM) ECHAM5 wind speed (at 10 m height) and ice, for present-day and potential future climate conditions towards the end of the end of the twenty-first century, represented by the Intergovernmental Panel for Climate Change (IPCC) CMIP3 (Coupled Model Inter-comparison Project phase 3) A1B greenhouse gas emission scenario (usually referred to as a ''medium-high emissions'' scenario). Several wave parameters were stored as output in the WAM model simulations, including the wave spectra. The 6 hourly and 0.5°×0.5°, temporal and space resolution, wave spectra were used to compute the SD global climate of two 32-yr periods, representative of the end of the twentieth (1959-1990) and twenty-first (1969-2100) centuries. Comparisons of the present climate run with the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-40 reanalysis are used to assess the capability of the WAM-ECHAM5 runs to produce realistic SD results. This study is part of the WRCP-JCOMM COWCLIP (Coordinated Ocean Wave Climate Project) effort.

Improving Subtropical Boundary Layer Cloudiness in the 2011 NCEP GFS

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

Fletcher, J. K.; Bretherton, Christopher S.; Xiao, Heng

2014-09-23

The current operational version of National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) shows significant low cloud bias. These biases also appear in the Coupled Forecast System (CFS), which is developed from the GFS. These low cloud biases degrade seasonal and longer climate forecasts, particularly of short-wave cloud radiative forcing, and affect predicted sea surface temperature. Reducing this bias in the GFS will aid the development of future CFS versions and contributes to NCEP's goal of unified weather and climate modelling. Changes are made to the shallow convection and planetary boundary layer parameterisations to make them more consistentmore » with current knowledge of these processes and to reduce the low cloud bias. These changes are tested in a single-column version of GFS and in global simulations with GFS coupled to a dynamical ocean model. In the single-column model, we focus on changing parameters that set the following: the strength of shallow cumulus lateral entrainment, the conversion of updraught liquid water to precipitation and grid-scale condensate, shallow cumulus cloud top, and the effect of shallow convection in stratocumulus environments. Results show that these changes improve the single-column simulations when compared to large eddy simulations, in particular through decreasing the precipitation efficiency of boundary layer clouds. These changes, combined with a few other model improvements, also reduce boundary layer cloud and albedo biases in global coupled simulations.« less

An Integrated Modeling Framework Forecasting Ecosystem Exposure— A Systems Approach to the Cumulative Impacts of Multiple Stressors

EPA Science Inventory

Freshwater habitats provide fishable, swimmable and drinkable resources and are a nexus of geophysical and biological processes. These processes in turn influence the persistence and sustainability of populations, communities and ecosystems. Climate change and landuse change enco...

Assessing the long-term impact of subsidence and global climate change on emergency evacuation routes in coastal Louisiana.

DOT National Transportation Integrated Search

2012-12-01

Subsidence forecast models for coastal Louisiana were developed to estimate the change in surface elevations of evacuation routes for the years 2015, 2025, 2050, and 2100. Geophysical and anthropogenic subsidence estimates were derived from on-going ...

Summary Report for the Workshop on Integrating Climate Change Adaption into Air Quality Decision Making

EPA Science Inventory

Over the past few decades, air quality planners have forecasted future air pollution levels based on information about changing emissions from stationary and mobile sources, population trends, transportation demand, natural sources of emissions, and other pressures on air quality...

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.

Forecasting of hourly load by pattern recognition in a small area power system

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

Dehdashti-Shahrokh, A.

1982-01-01

An intuitive, logical, simple and efficient method of forecasting hourly load in a small area power system is presented. A pattern recognition approach is used in developing the forecasting model. Pattern recognition techniques are powerful tools in the field of artificial intelligence (cybernetics) and simulate the way the human brain operates to make decisions. Pattern recognition is generally used in analysis of processes where the total physical nature behind the process variation is unkown but specific kinds of measurements explain their behavior. In this research basic multivariate analyses, in conjunction with pattern recognition techniques, are used to develop a linearmore » deterministic model to forecast hourly load. This method assumes that load patterns in the same geographical area are direct results of climatological changes (weather sensitive load), and have occurred in the past as a result of similar climatic conditions. The algorithm described in here searches for the best possible pattern from a seasonal library of load and weather data in forecasting hourly load. To accommodate the unpredictability of weather and the resulting load, the basic twenty-four load pattern was divided into eight three-hour intervals. This division was made to make the model adaptive to sudden climatic changes. The proposed method offers flexible lead times of one to twenty-four hours. The results of actual data testing had indicated that this proposed method is computationally efficient, highly adaptive, with acceptable data storage size and accuracy that is comparable to many other existing methods.« less

Incorporating climate change and morphological uncertainty into coastal change hazard assessments

USGS Publications Warehouse

Baron, Heather M.; Ruggiero, Peter; Wood, Nathan J.; Harris, Erica L.; Allan, Jonathan; Komar, Paul D.; Corcoran, Patrick

2015-01-01

Documented and forecasted trends in rising sea levels and changes in storminess patterns have the potential to increase the frequency, magnitude, and spatial extent of coastal change hazards. To develop realistic adaptation strategies, coastal planners need information about coastal change hazards that recognizes the dynamic temporal and spatial scales of beach morphology, the climate controls on coastal change hazards, and the uncertainties surrounding the drivers and impacts of climate change. We present a probabilistic approach for quantifying and mapping coastal change hazards that incorporates the uncertainty associated with both climate change and morphological variability. To demonstrate the approach, coastal change hazard zones of arbitrary confidence levels are developed for the Tillamook County (State of Oregon, USA) coastline using a suite of simple models and a range of possible climate futures related to wave climate, sea-level rise projections, and the frequency of major El Niño events. Extreme total water levels are more influenced by wave height variability, whereas the magnitude of erosion is more influenced by sea-level rise scenarios. Morphological variability has a stronger influence on the width of coastal hazard zones than the uncertainty associated with the range of climate change scenarios.

CARICOF - The Caribbean Regional Climate Outlook Forum

NASA Astrophysics Data System (ADS)

Van Meerbeeck, Cedric

2013-04-01

Regional Climate Outlook Forums (RCOFs) are viewed as a critical building block in the Global Framework for Climate Services (GFCS) of the World Meteorological Organization (WMO). The GFCS seeks to extend RCOFs to all vulnerable regions of the world such as the Caribbean, of which the entire population is exposed to water- and heat-related natural hazards. An RCOF is initially intended to identify gaps in information and technical capability; facilitate research cooperation and data exchange within and between regions, and improve coordination within the climate forecasting community. A focus is given on variations in climate conditions on a seasonal timescale. In this view, the relevance of a Caribbean RCOF (CARICOF) is the following: while the seasonality of the climate in the Caribbean has been well documented, major gaps in knowledge exist in terms of the drivers in the shifts of amplitude and phase of seasons (as evidenced from the worst region-wide drought period in recent history during 2009-2010). To address those gaps, CARICOF has brought together National Weather Services (NWSs) from 18 territories under the coordination of the Caribbean Institute for Meteorology and Hydrology (CIMH), to produce region-wide, consensus, seasonal climate outlooks since March 2012. These outlooks include tercile rainfall forecasts, sea and air surface temperature forecasts as well as the likely evolution of the drivers of seasonal climate variability in the region, being amongst others the El Niño Southern Oscillation or tropical Atlantic and Caribbean Sea temperatures. Forecasts for both the national-scale forecasts made by the NWSs and CIMH's regional-scale forecast amalgamate output from several forecasting tools. These currently include: (1) statistical models such as Canonical Correlation Analysis run with the Climate Predictability Tool, providing tercile rainfall forecasts at weather station scale; (2) a global outlooks published by the WMO appointed Global Producing Centres (GPCs). Indications are that the current seasonal forecasting system used by CARICOF has produced reliable outlooks than previously available. Nevertheless, through its forum platform, areas for further development are continuously being defined, which are then implemented through efficient information exchanges between and hands-on training of forecasters. Finally, the disaster research and emergency management communities have shown that effective early warnings of impending hazards need to be complemented by information on the risks actually posed by the hazards and pathways for action. CARICOF is to address this issue by designing the outputs of the seasonal climate outlooks such that they can then effectively feed into an early warning information system of seasonal climate variability related hazards to its constituent countries' and territories major socio-economic sectors.

Seasonal forecasting of groundwater levels in natural aquifers in the United Kingdom

NASA Astrophysics Data System (ADS)

Mackay, Jonathan; Jackson, Christopher; Pachocka, Magdalena; Brookshaw, Anca; Scaife, Adam

2014-05-01

Groundwater aquifers comprise the world's largest freshwater resource and provide resilience to climate extremes which could become more frequent under future climate changes. Prolonged dry conditions can induce groundwater drought, often characterised by significantly low groundwater levels which may persist for months to years. In contrast, lasting wet conditions can result in anomalously high groundwater levels which result in flooding, potentially at large economic cost. Using computational models to produce groundwater level forecasts allows appropriate management strategies to be considered in advance of extreme events. The majority of groundwater level forecasting studies to date use data-based models, which exploit the long response time of groundwater levels to meteorological drivers and make forecasts based only on the current state of the system. Instead, seasonal meteorological forecasts can be used to drive hydrological models and simulate groundwater levels months into the future. Such approaches have not been used in the past due to a lack of skill in these long-range forecast products. However systems such as the latest version of the Met Office Global Seasonal Forecast System (GloSea5) are now showing increased skill up to a 3-month lead time. We demonstrate the first groundwater level ensemble forecasting system using a multi-member ensemble of hindcasts from GloSea5 between 1996 and 2009 to force 21 simple lumped conceptual groundwater models covering most of the UK's major aquifers. We present the results from this hindcasting study and demonstrate that the system can be used to forecast groundwater levels with some skill up to three months into the future.

Understanding and seasonal forecasting of hydrological drought in the Anthropocene

NASA Astrophysics Data System (ADS)

Yuan, Xing; Zhang, Miao; Wang, Linying; Zhou, Tian

2017-11-01

Hydrological drought is not only caused by natural hydroclimate variability but can also be directly altered by human interventions including reservoir operation, irrigation, groundwater exploitation, etc. Understanding and forecasting of hydrological drought in the Anthropocene are grand challenges due to complicated interactions among climate, hydrology and humans. In this paper, five decades (1961-2010) of naturalized and observed streamflow datasets are used to investigate hydrological drought characteristics in a heavily managed river basin, the Yellow River basin in north China. Human interventions decrease the correlation between hydrological and meteorological droughts, and make the hydrological drought respond to longer timescales of meteorological drought. Due to large water consumptions in the middle and lower reaches, there are 118-262 % increases in the hydrological drought frequency, up to 8-fold increases in the drought severity, 21-99 % increases in the drought duration and the drought onset is earlier. The non-stationarity due to anthropogenic climate change and human water use basically decreases the correlation between meteorological and hydrological droughts and reduces the effect of human interventions on hydrological drought frequency while increasing the effect on drought duration and severity. A set of 29-year (1982-2010) hindcasts from an established seasonal hydrological forecasting system are used to assess the forecast skill of hydrological drought. In the naturalized condition, the climate-model-based approach outperforms the climatology method in predicting the 2001 severe hydrological drought event. Based on the 29-year hindcasts, the former method has a Brier skill score of 11-26 % against the latter for the probabilistic hydrological drought forecasting. In the Anthropocene, the skill for both approaches increases due to the dominant influence of human interventions that have been implicitly incorporated by the hydrological post-processing, while the difference between the two predictions decreases. This suggests that human interventions can outweigh the climate variability for the hydrological drought forecasting in the Anthropocene, and the predictability for human interventions needs more attention.

Forecasting sagebrush ecosystem components and greater sage-grouse habitat for 2050: learning from past climate patterns and Landsat imagery to predict the future

USGS Publications Warehouse

Homer, Collin G.; Xian, George Z.; Aldridge, Cameron L.; Meyer, Debra K.; Loveland, Thomas R.; O'Donnell, Michael S.

2015-01-01

Sagebrush (Artemisia spp.) ecosystems constitute the largest single North American shrub ecosystem and provide vital ecological, hydrological, biological, agricultural, and recreational ecosystem services. Disturbances have altered and reduced this ecosystem historically, but climate change may ultimately represent the greatest future risk. Improved ways to quantify, monitor, and predict climate-driven gradual change in this ecosystem is vital to its future management. We examined the annual change of Daymet precipitation (daily gridded climate data) and five remote sensing ecosystem sagebrush vegetation and soil components (bare ground, herbaceous, litter, sagebrush, and shrub) from 1984 to 2011 in southwestern Wyoming. Bare ground displayed an increasing trend in abundance over time, and herbaceous, litter, shrub, and sagebrush showed a decreasing trend. Total precipitation amounts show a downward trend during the same period. We established statistically significant correlations between each sagebrush component and historical precipitation records using a simple least squares linear regression. Using the historical relationship between sagebrush component abundance and precipitation in a linear model, we forecasted the abundance of the sagebrush components in 2050 using Intergovernmental Panel on Climate Change (IPCC) precipitation scenarios A1B and A2. Bare ground was the only component that increased under both future scenarios, with a net increase of 48.98 km2 (1.1%) across the study area under the A1B scenario and 41.15 km2 (0.9%) under the A2 scenario. The remaining components decreased under both future scenarios: litter had the highest net reductions with 49.82 km2 (4.1%) under A1B and 50.8 km2 (4.2%) under A2, and herbaceous had the smallest net reductions with 39.95 km2 (3.8%) under A1B and 40.59 km2 (3.3%) under A2. We applied the 2050 forecast sagebrush component values to contemporary (circa 2006) greater sage-grouse (Centrocercus urophasianus) habitat models to evaluate the effects of potential climate-induced habitat change. Under the 2050 IPCC A1B scenario, 11.6% of currently identified nesting habitat was lost, and 0.002% of new potential habitat was gained, with 4% of summer habitat lost and 0.039% gained. Our results demonstrate the successful ability of remote sensing based sagebrush components, when coupled with precipitation, to forecast future component response using IPCC precipitation scenarios. Our approach also enables future quantification of greater sage-grouse habitat under different precipitation scenarios, and provides additional capability to identify regional precipitation influence on sagebrush component response.

Skilful seasonal forecasts of streamflow over Europe?

NASA Astrophysics Data System (ADS)

Arnal, Louise; Cloke, Hannah L.; Stephens, Elisabeth; Wetterhall, Fredrik; Prudhomme, Christel; Neumann, Jessica; Krzeminski, Blazej; Pappenberger, Florian

2018-04-01

This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.

Toward a U.S. National Phenological Assessment

NASA Astrophysics Data System (ADS)

Henebry, Geoffrey M.; Betancourt, Julio L.

2010-01-01

Third USA National Phenology Network (USA-NPN) and Research Coordination Network (RCN) Annual Meeting; Milwaukee, Wisconsin, 5-9 October 2009; Directional climate change will have profound and lasting effects throughout society that are best understood through fundamental physical and biological processes. One such process is phenology: how the timing of recurring biological events is affected by biotic and abiotic forces. Phenology is an early and integrative indicator of climate change readily understood by nonspecialists. Phenology affects the planting, maturation, and harvesting of food and fiber; pollination; timing and magnitude of allergies and disease; recreation and tourism; water quantity and quality; and ecosystem function and resilience. Thus, phenology is the gateway to climatic effects on both managed and unmanaged ecosystems. Adaptation to climatic variability and change will require integration of phenological data and models with climatic forecasts at seasonal to decadal time scales. Changes in phenologies have already manifested myriad effects of directional climate change. As these changes continue, it is critical to establish a comprehensive suite of benchmarks that can be tracked and mapped at local to continental scales with observations and climate models.

Forecasting climate change impacts to plant community composition in the Sonoran Desert region

USGS Publications Warehouse

Munson, Seth M.; Webb, Robert H.; Belnap, Jayne; Hubbard, J. Andrew; Swann, Don E.; Rutman, Sue

2012-01-01

Hotter and drier conditions projected for the southwestern United States can have a large impact on the abundance and composition of long-lived desert plant species. We used long-term vegetation monitoring results from 39 large plots across four protected sites in the Sonoran Desert region to determine how plant species have responded to past climate variability. This cross-site analysis identified the plant species and functional types susceptible to climate change, the magnitude of their responses, and potential climate thresholds. In the relatively mesic mesquite savanna communities, perennial grasses declined with a decrease in annual precipitation, cacti increased, and there was a reversal of the Prosopis velutina expansion experienced in the 20th century in response to increasing mean annual temperature (MAT). In the more xeric Arizona Upland communities, the dominant leguminous tree, Cercidium microphyllum, declined on hillslopes, and the shrub Fouquieria splendens decreased, especially on south- and west-facing slopes in response to increasing MAT. In the most xeric shrublands, the codominant species Larrea tridentata and its hemiparasite Krameria grayi decreased with a decrease in cool season precipitation and increased aridity, respectively. This regional-scale assessment of plant species response to recent climate variability is critical for forecasting future shifts in plant community composition, structure, and productivity.

An improved Multimodel Approach for Global Sea Surface Temperature Forecasts

NASA Astrophysics Data System (ADS)

Khan, M. Z. K.; Mehrotra, R.; Sharma, A.

2014-12-01

The concept of ensemble combinations for formulating improved climate forecasts has gained popularity in recent years. However, many climate models share similar physics or modeling processes, which may lead to similar (or strongly correlated) forecasts. Recent approaches for combining forecasts that take into consideration differences in model accuracy over space and time have either ignored the similarity of forecast among the models or followed a pairwise dynamic combination approach. Here we present a basis for combining model predictions, illustrating the improvements that can be achieved if procedures for factoring in inter-model dependence are utilised. The utility of the approach is demonstrated by combining sea surface temperature (SST) forecasts from five climate models over a period of 1960-2005. The variable of interest, the monthly global sea surface temperature anomalies (SSTA) at a 50´50 latitude-longitude grid, is predicted three months in advance to demonstrate the utility of the proposed algorithm. Results indicate that the proposed approach offers consistent and significant improvements for majority of grid points compared to the case where the dependence among the models is ignored. Therefore, the proposed approach of combining multiple models by taking into account the existing interdependence, provides an attractive alternative to obtain improved climate forecast. In addition, an approach to combine seasonal forecasts from multiple climate models with varying periods of availability is also demonstrated.

Evaluating the climate effects of reforestation in New England using a weather research and forecasting (WRF) model multiphysics ensemble

Treesearch

E.A. Burakowski; S.V. Ollinger; G.B. Bonan; C.P. Wake; J.E. Dibb; D.Y. Hollinger

2016-01-01

The New England region of the northeastern United States has a land use history characterized by forest clearing for agriculture and other uses during European colonization and subsequent reforestation following widespread farm abandonment. Despite these broad changes, the potential influence on local and regional climate has received relatively little attention. This...

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico.

PubMed

Johansson, Michael A; Reich, Nicholas G; Hota, Aditi; Brownstein, John S; Santillana, Mauricio

2016-09-26

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

PubMed Central

Johansson, Michael A.; Reich, Nicholas G.; Hota, Aditi; Brownstein, John S.; Santillana, Mauricio

2016-01-01

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model. PMID:27665707

Impacts of climate and land use changes on the hydrological and erosive response of a humid and dry Mediterranean catchment

NASA Astrophysics Data System (ADS)

Serpa, Dalila; Nunes, João Pedro; Santos, Juliana; Sampaio, Elsa; Jacinto, Rita; Veiga, Sandro; Lima, Júlio; Moreira, Madalena; Corte-Real, João; Keizer, Jan Jacob; Abrantes, Nelson

2016-04-01

The impacts of climate and land use changes on streamflow and sediment export were evaluated for a humid (São Lourenço) and a dry (Guadalupe) Mediterranean catchment, using the Soil and Water Assessment Tool (SWAT) model. SWAT was able to produce viable streamflow and sediment export simulations for both catchments, which provided a baseline for investigating climate and land use changes under the A1B and B1 emission scenarios for the period between 2071 and 2100. Compared to the baseline period (1971-2000), climate change scenarios forecasted a decrease in annual precipitation in both catchments (humid, both scenarios: -12%; dry, both scenarios: -8%), but with strong increases during winter. Land use changes followed a socio-economic storyline in which traditional agriculture was replaced by more profitable land uses, i.e. corn and commercial forestry at the humid site and sunflower at the dry site. Climate changes led to a decrease of streamflow in both catchments (humid, both scenarios: -13%; dry, A1B: -14%; B1: -18%), mostly as a consequence of the projected decrease in rainfall. Land use changes led to small increases in flow discharge, but a higher increase was observed for the dry site under scenario A1B (humid, A1B: +0.3%; B1: +1%; dry, A1B: +6%; B1: +0.3%). The combination of climate and land use scenarios was mostly dominated by the climatic response, since a decrease in streamflow was observed for both catchments (humid, A1B: -13%; B1: -12%; dry, A1B: -8%; B1: -18%). Regarding the erosive response, clear differences were observed between catchments mostly due to differences in both the present-day and forecasted vegetation types. Climate scenarios led to a decrease in sediment export at the humid catchment (A1B: -11%; B1: -9%) and to an increase at the dry catchment (A1B: +24%; B1: +22%) in the first case due to the predominant vegetation type (vineyards and maritime pine) providing year-round cover, while in the second, due to annual crops (wheat and pasture) exposing soils during winter. For land use scenarios, the same contrast occurred between catchments (humid, A1B: -18%; B1: -10%; dry, A1B: +257%; B1: +9%) due to the expansion of permanent cover vegetation in one case and annual crops in the other. Climate and land use changes had off-setting effects on sediment export at the humid catchment (A1B: -29%; B1: -22%), as a result of reduced precipitation and cultivation of more soil-protective crops. A different response was observed for the dry catchment (A1B: +222%; B1: +5%), as the increase in sediment export associated with the cultivation of highly erosion-prone crops was not aggravated by the higher rainfall amounts forecasted for winter months. The results of the present study highlight that indirect impacts of climate change, like land use changes, might be similar or more severe than direct impacts.

Forecasting extreme temperature health hazards in Europe

NASA Astrophysics Data System (ADS)

Di Napoli, Claudia; Pappenberger, Florian; Cloke, Hannah L.

2017-04-01

Extreme hot temperatures, such as those experienced during a heat wave, represent a dangerous meteorological hazard to human health. Heat disorders such as sunstroke are harmful to people of all ages and responsible for excess mortality in the affected areas. In 2003 more than 50,000 people died in western and southern Europe because of a severe and sustained episode of summer heat [1]. Furthermore, according to the Intergovernmental Panel on Climate Change heat waves are expected to get more frequent in the future thus posing an increasing threat to human lives. Developing appropriate tools for extreme hot temperatures prediction is therefore mandatory to increase public preparedness and mitigate heat-induced impacts. A recent study has shown that forecasts of the Universal Thermal Climate Index (UTCI) provide a valid overview of extreme temperature health hazards on a global scale [2]. UTCI is a parameter related to the temperature of the human body and its regulatory responses to the surrounding atmospheric environment. UTCI is calculated using an advanced thermo-physiological model that includes the human heat budget, physiology and clothing. To forecast UTCI the model uses meteorological inputs, such as 2m air temperature, 2m water vapour pressure and wind velocity at body height derived from 10m wind speed, from NWP models. Here we examine the potential of UTCI as an extreme hot temperature prediction tool for the European area. UTCI forecasts calculated using above-mentioned parameters from ECMWF models are presented. The skill in predicting UTCI for medium lead times is also analysed and discussed for implementation to international health-hazard warning systems. This research is supported by the ANYWHERE project (EnhANcing emergencY management and response to extreme WeatHER and climate Events) which is funded by the European Commission's HORIZON2020 programme. [1] Koppe C. et al., Heat waves: risks and responses. World Health Organization. Health and Global Environmental Change, Series No. 2, Copenhagen, Denmark, 2004. [2] Pappenberger F. et al., Global forecasting of thermal health hazards: the skill of probabilistic predictions of the Universal Thermal Climate Index (UTCI), International Journal of Biometeorology 59(3): 311-323, 2015.

Influence of climate change on the incidence and impact of arenavirus diseases: a speculative assessment.

PubMed

Clegg, J C

2009-06-01

The current worldwide incidence of viral haemorrhagic fevers caused by arenaviruses is briefly reviewed. The recently published Assessment Report of the Intergovernmental Panel on Climate Change has described the changes in global climate that are expected to occur over the course of the present century and beyond. Climate modelling and forecasting have not yet reached the stage where confident predictions of regional changes at the level of a virus endemic area can be made. However, in the regions where pathogenic arenaviruses now circulate, significant effects are likely to include increases in surface temperature, changes in the extent and distribution of rainfall, the occurrence of extreme weather events, glacier retreat, and coastal flooding as a result of sea level rise. The possible impact of these changes on the geographical location and the incidence of arenavirus diseases and its human impact are discussed.

Replacing climatological potential evapotranspiration estimates with dynamic satellite-based observations in operational hydrologic prediction models

NASA Astrophysics Data System (ADS)

Franz, K. J.; Bowman, A. L.; Hogue, T. S.; Kim, J.; Spies, R.

2011-12-01

In the face of a changing climate, growing populations, and increased human habitation in hydrologically risky locations, both short- and long-range planners increasingly require robust and reliable streamflow forecast information. Current operational forecasting utilizes watershed-scale, conceptual models driven by ground-based (commonly point-scale) observations of precipitation and temperature and climatological potential evapotranspiration (PET) estimates. The PET values are derived from historic pan evaporation observations and remain static from year-to-year. The need for regional dynamic PET values is vital for improved operational forecasting. With the advent of satellite remote sensing and the adoption of a more flexible operational forecast system by the National Weather Service, incorporation of advanced data products is now more feasible than in years past. In this study, we will test a previously developed satellite-derived PET product (UCLA MODIS-PET) in the National Weather Service forecast models and compare the model results to current methods. The UCLA MODIS-PET method is based on the Priestley-Taylor formulation, is driven with MODIS satellite products, and produces a daily, 250m PET estimate. The focus area is eight headwater basins in the upper Midwest U.S. There is a need to develop improved forecasting methods for this region that are able to account for climatic and landscape changes more readily and effectively than current methods. This region is highly flood prone yet sensitive to prolonged dry periods in late summer and early fall, and is characterized by a highly managed landscape, which has drastically altered the natural hydrologic cycle. Our goal is to improve model simulations, and thereby, the initial conditions prior to the start of a forecast through the use of PET values that better reflect actual watershed conditions. The forecast models are being tested in both distributed and lumped mode.

Influences of man-made emissions and climate changes on tropospheric ozone, methane, and sulfate at 2030 from a broad range of possible futures

NASA Astrophysics Data System (ADS)

Unger, Nadine; Shindell, Drew T.; Koch, Dorothy M.; Amann, Markus; Cofala, Janusz; Streets, David G.

2006-06-01

We apply the Goddard Institute for Space Studies composition-climate model to an assessment of tropospheric O3, CH4, and sulfate at 2030. We compare four different anthropogenic emissions forecasts: A1B and B1 from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios and Current Legislation (CLE) and Maximum Feasible Reduction (MFR) from the International Institute for Applied Systems Analysis. The projections encompass a wide range of possible man-made emissions changes. The A1B, B1, and CLE forecasts all suggest large increases in surface O3 and sulfate baseline pollution at tropical and subtropical latitudes, especially over the Indian subcontinent, where the pollution increases may be as large as 100%. The ranges of annual mean regional ground level O3 and sulfate changes across all scenarios are -10 to +30 ppbv and -1200 to +3000 pptv, respectively. Physical climate changes reduce future surface O3, but tend to increase ground level sulfate locally over North Africa because of an enhancement of aqueous-phase SO2 oxidation. For all examined future scenarios the combined sum of the CH4, O3, and sulfate radiative forcings is positive, even for the MFR scenario, because of the large reduction in sulfate. For A1B the forcings are as much as half of that of the preindustrial to present-day forcing for each species. For MFR the sign of the forcing for each species is reversed with respect to the other scenarios. At 2030, global changes in climate-sensitive natural emissions of CH4 from wetlands, NOx from lightning, and dimethyl sulfide from the ocean appear to be small (<5%).

Forecasting the Future: Exploring Evidence for Global Climate Change.

ERIC Educational Resources Information Center

California Univ., San Diego, La Jolla. Inst. of Marine Resources.

This curriculum and classroom activity guide considers evidence gathered in answer to questions concerning global environmental change. It describes methods that biologists, chemists, geologists, meteorologists, and physicists use to gather and interpret their findings. The activities and approaches in this guide were developed to meet the skill…

Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

NASA Astrophysics Data System (ADS)

Jha, Sanjeev K.; Shrestha, Durga L.; Stadnyk, Tricia A.; Coulibaly, Paulin

2018-03-01

Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS) Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS), from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.

The Impacts of Climate Variations on Military Operations in the Horn of Africa

DTIC Science & Technology

2006-03-01

variability in a region. Climate forecasts are predictions of the future state of the climate , much as we think of weather forecasts but at longer...arrive at accurate characterizations of the future state of the climate . Many of the civilian organizations that generate reanalysis data also

Climate Forecast System

Science.gov Websites

Weather Service NWS logo - Click to go to the NWS home page Climate Forecast System Home News Organization Web portal to all Federal, state and local government Web resources and services. The NCEP Climate when using the CFS Reanalysis (CFSR) data. Saha, Suranjana, and Coauthors, 2010: The NCEP Climate

Evaluation of Probable Maximum Precipitation and Flood under Climate Change in the 21st Century

NASA Astrophysics Data System (ADS)

Gangrade, S.; Kao, S. C.; Rastogi, D.; Ashfaq, M.; Naz, B. S.; Kabela, E.; Anantharaj, V. G.; Singh, N.; Preston, B. L.; Mei, R.

2016-12-01

Critical infrastructures are potentially vulnerable to extreme hydro-climatic events. Under a warming environment, the magnitude and frequency of extreme precipitation and flood are likely to increase enhancing the needs to more accurately quantify the risks due to climate change. In this study, we utilized an integrated modeling framework that includes the Weather Research Forecasting (WRF) model and a high resolution distributed hydrology soil vegetation model (DHSVM) to simulate probable maximum precipitation (PMP) and flood (PMF) events over Alabama-Coosa-Tallapoosa River Basin. A total of 120 storms were selected to simulate moisture maximized PMP under different meteorological forcings, including historical storms driven by Climate Forecast System Reanalysis (CFSR) and baseline (1981-2010), near term future (2021-2050) and long term future (2071-2100) storms driven by Community Climate System Model version 4 (CCSM4) under Representative Concentrations Pathway 8.5 emission scenario. We also analyzed the sensitivity of PMF to various antecedent hydrologic conditions such as initial soil moisture conditions and tested different compulsive approaches. Overall, a statistical significant increase is projected for future PMP and PMF, mainly attributed to the increase of background air temperature. The ensemble of simulated PMP and PMF along with their sensitivity allows us to better quantify the potential risks associated with hydro-climatic extreme events on critical energy-water infrastructures such as major hydropower dams and nuclear power plants.

Incorporating probabilistic seasonal climate forecasts into river management using a risk-based framework

USGS Publications Warehouse

Sojda, Richard S.; Towler, Erin; Roberts, Mike; Rajagopalan, Balaji

2013-01-01

[1] Despite the influence of hydroclimate on river ecosystems, most efforts to date have focused on using climate information to predict streamflow for water supply. However, as water demands intensify and river systems are increasingly stressed, research is needed to explicitly integrate climate into streamflow forecasts that are relevant to river ecosystem management. To this end, we present a five step risk-based framework: (1) define risk tolerance, (2) develop a streamflow forecast model, (3) generate climate forecast ensembles, (4) estimate streamflow ensembles and associated risk, and (5) manage for climate risk. The framework is successfully demonstrated for an unregulated watershed in southwest Montana, where the combination of recent drought and water withdrawals has made it challenging to maintain flows needed for healthy fisheries. We put forth a generalized linear modeling (GLM) approach to develop a suite of tools that skillfully model decision-relevant low flow characteristics in terms of climate predictors. Probabilistic precipitation forecasts are used in conjunction with the GLMs, resulting in season-ahead prediction ensembles that provide the full risk profile. These tools are embedded in an end-to-end risk management framework that directly supports proactive fish conservation efforts. Results show that the use of forecasts can be beneficial to planning, especially in wet years, but historical precipitation forecasts are quite conservative (i.e., not very “sharp”). Synthetic forecasts show that a modest “sharpening” can strongly impact risk and improve skill. We emphasize that use in management depends on defining relevant environmental flows and risk tolerance, requiring local stakeholder involvement.

An operational ensemble prediction system for catchment rainfall over eastern Africa spanning multiple temporal and spatial scales

NASA Astrophysics Data System (ADS)

Riddle, E. E.; Hopson, T. M.; Gebremichael, M.; Boehnert, J.; Broman, D.; Sampson, K. M.; Rostkier-Edelstein, D.; Collins, D. C.; Harshadeep, N. R.; Burke, E.; Havens, K.

2017-12-01

While it is not yet certain how precipitation patterns will change over Africa in the future, it is clear that effectively managing the available water resources is going to be crucial in order to mitigate the effects of water shortages and floods that are likely to occur in a changing climate. One component of effective water management is the availability of state-of-the-art and easy to use rainfall forecasts across multiple spatial and temporal scales. We present a web-based system for displaying and disseminating ensemble forecast and observed precipitation data over central and eastern Africa. The system provides multi-model rainfall forecasts integrated to relevant hydrological catchments for timescales ranging from one day to three months. A zoom-in features is available to access high resolution forecasts for small-scale catchments. Time series plots and data downloads with forecasts, recent rainfall observations and climatological data are available by clicking on individual catchments. The forecasts are calibrated using a quantile regression technique and an optimal multi-model forecast is provided at each timescale. The forecast skill at the various spatial and temporal scales will discussed, as will current applications of this tool for managing water resources in Sudan and optimizing hydropower operations in Ethiopia and Tanzania.

[Global climate change and carbon balance in forest ecosystems of boreal zones: imitating modeling as a forecast tool].

PubMed

Shanin, V N; Mikhaĭlov, A V; Bykhovets, S S; Komarov, A S

2010-01-01

The individually oriented system of the EFIMOD models simulating carbon and nitrogen flows in forest ecosystems has been used for forecasting the response of forest ecosystems to various forest exploitation regimes with climate change. As input data the forest management materials for the Manturovskii forestry of the Kostroma region were used. It has been shown that increase of mid-annual temperatures and rainfall influence the redistribution of carbon and nitrogen supply in organic form: supply increase of these elements in phytomass simultaneously with depletion of them in soil occurred. The most carbon and nitrogen accumulation in forest ecosystems occurs in the scenario without felling. In addition, in this scenario only the ecosystems of the modeling territory function as a carbon drain; in the other two scenarios (with selective and total felling) they function as a source of carbon. Climate changes greatly influence the decomposition rate of organic matter in soil, which leads to increased emission of carbonic acid. The second consequence of the increase in the destruction rate is nitrogen increase in the soil in a form available for plants that entails production increase of plantations.

Forecasting malaria cases using climatic factors in delhi, India: a time series analysis.

PubMed

Kumar, Varun; Mangal, Abha; Panesar, Sanjeet; Yadav, Geeta; Talwar, Richa; Raut, Deepak; Singh, Saudan

2014-01-01

Background. Malaria still remains a public health problem in developing countries and changing environmental and climatic factors pose the biggest challenge in fighting against the scourge of malaria. Therefore, the study was designed to forecast malaria cases using climatic factors as predictors in Delhi, India. Methods. The total number of monthly cases of malaria slide positives occurring from January 2006 to December 2013 was taken from the register maintained at the malaria clinic at Rural Health Training Centre (RHTC), Najafgarh, Delhi. Climatic data of monthly mean rainfall, relative humidity, and mean maximum temperature were taken from Regional Meteorological Centre, Delhi. Expert modeler of SPSS ver. 21 was used for analyzing the time series data. Results. Autoregressive integrated moving average, ARIMA (0,1,1) (0,1,0)(12), was the best fit model and it could explain 72.5% variability in the time series data. Rainfall (P value = 0.004) and relative humidity (P value = 0.001) were found to be significant predictors for malaria transmission in the study area. Seasonal adjusted factor (SAF) for malaria cases shows peak during the months of August and September. Conclusion. ARIMA models of time series analysis is a simple and reliable tool for producing reliable forecasts for malaria in Delhi, India.

An assessment of a North American Multi-Model Ensemble (NMME) based global drought early warning forecast system

NASA Astrophysics Data System (ADS)

Wood, E. F.; Yuan, X.; Sheffield, J.; Pan, M.; Roundy, J.

2013-12-01

One of the key recommendations of the WCRP Global Drought Information System (GDIS) workshop is to develop an experimental real-time global monitoring and prediction system. While great advances has been made in global drought monitoring based on satellite observations and model reanalysis data, global drought forecasting has been stranded in part due to the limited skill both in climate forecast models and global hydrologic predictions. Having been working on drought monitoring and forecasting over USA for more than a decade, the Princeton land surface hydrology group is now developing an experimental global drought early warning system that is based on multiple climate forecast models and a calibrated global hydrologic model. In this presentation, we will test its capability in seasonal forecasting of meteorological, agricultural and hydrologic droughts over global major river basins, using precipitation, soil moisture and streamflow forecasts respectively. Based on the joint probability distribution between observations using Princeton's global drought monitoring system and model hindcasts and real-time forecasts from North American Multi-Model Ensemble (NMME) project, we (i) bias correct the monthly precipitation and temperature forecasts from multiple climate forecast models, (ii) downscale them to a daily time scale, and (iii) use them to drive the calibrated VIC model to produce global drought forecasts at a 1-degree resolution. A parallel run using the ESP forecast method, which is based on resampling historical forcings, is also carried out for comparison. Analysis is being conducted over global major river basins, with multiple drought indices that have different time scales and characteristics. The meteorological drought forecast does not have uncertainty from hydrologic models and can be validated directly against observations - making the validation an 'apples-to-apples' comparison. Preliminary results for the evaluation of meteorological drought onset hindcasts indicate that climate models increase drought detectability over ESP by 31%-81%. However, less than 30% of the global drought onsets can be detected by climate models. The missed drought events are associated with weak ENSO signals and lower potential predictability. Due to the high false alarms from climate models, the reliability is more important than sharpness for a skillful probabilistic drought onset forecast. Validations and skill assessments for agricultural and hydrologic drought forecasts are carried out using soil moisture and streamflow output from the VIC land surface model (LSM) forced by a global forcing data set. Given our previous drought forecasting experiences over USA and Africa, validating the hydrologic drought forecasting is a significant challenge for a global drought early warning system.

Artist's Concept of the Orbiting Carbon Observatory

NASA Technical Reports Server (NTRS)

2008-01-01

Artist's concept of the Orbiting Carbon Observatory. The mission, scheduled to launch in early 2009, will be the first spacecraft dedicated to studying atmospheric carbon dioxide, the principal human-produced driver of climate change. It will provide the first global picture of the human and natural sources of carbon dioxide and the places where this important greenhouse gas is stored. Such information will improve global carbon cycle models as well as forecasts of atmospheric carbon dioxide levels and of how our climate may change in the future.

Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R. W.

2008-12-01

The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Can regional climate models provide additional useful information from global seasonal forecasts? MRED will use a suite of regional climate models to downscale seasonal forecasts produced by the new National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus will be on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the potential usefulness of higher resolution, especially for near-surface fields influenced by high resolution orography. Each regional model will cover the conterminous US (CONUS) at approximately 32 km resolution, and will perform an ensemble of 15 runs for each year 1982-2003 for the forecast period 1 December - 30 April. MRED will compare individual regional and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs), as well as wind, humidity, radiation, turbulent heat fluxes, which are important for more advanced coupled macro-scale hydrologic models. Metrics of ensemble spread will also be evaluated. Extensive analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will eventually define a strategy for more skillful and useful regional seasonal climate forecasts.

Modeling responses of large-river fish populations to global climate change through downscaling and incorporation of predictive uncertainty

USGS Publications Warehouse

Wildhaber, Mark L.; Wikle, Christopher K.; Anderson, Christopher J.; Franz, Kristie J.; Moran, Edward H.; Dey, Rima; Mader, Helmut; Kraml, Julia

2012-01-01

Climate change operates over a broad range of spatial and temporal scales. Understanding its effects on ecosystems requires multi-scale models. For understanding effects on fish populations of riverine ecosystems, climate predicted by coarse-resolution Global Climate Models must be downscaled to Regional Climate Models to watersheds to river hydrology to population response. An additional challenge is quantifying sources of uncertainty given the highly nonlinear nature of interactions between climate variables and community level processes. We present a modeling approach for understanding and accomodating uncertainty by applying multi-scale climate models and a hierarchical Bayesian modeling framework to Midwest fish population dynamics and by linking models for system components together by formal rules of probability. The proposed hierarchical modeling approach will account for sources of uncertainty in forecasts of community or population response. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. This understanding will aid evaluation of management options for coping with global climate change. In our initial analyses, we found that predicted pallid sturgeon population responses were dependent on the climate scenario considered.

Assessing the Value of Post-processed State-of-the-art Long-term Weather Forecast Ensembles within An Integrated Agronomic Modelling Framework

NASA Astrophysics Data System (ADS)

LI, Y.; Castelletti, A.; Giuliani, M.

2014-12-01

Over recent years, long-term climate forecast from global circulation models (GCMs) has been demonstrated to show increasing skills over the climatology, thanks to the advances in the modelling of coupled ocean-atmosphere dynamics. Improved information from long-term forecast is supposed to be a valuable support to farmers in optimizing farming operations (e.g. crop choice, cropping time) and for more effectively coping with the adverse impacts of climate variability. Yet, evaluating how valuable this information can be is not straightforward and farmers' response must be taken into consideration. Indeed, while long-range forecast are traditionally evaluated in terms of accuracy by comparison of hindcast and observed values, in the context of agricultural systems, potentially useful forecast information should alter the stakeholders' expectation, modify their decisions and ultimately have an impact on their annual benefit. Therefore, it is more desirable to assess the value of those long-term forecasts via decision-making models so as to extract direct indication of probable decision outcomes from farmers, i.e. from an end-to-end perspective. In this work, we evaluate the operational value of thirteen state-of-the-art long-range forecast ensembles against climatology forecast and subjective prediction (i.e. past year climate and historical average) within an integrated agronomic modeling framework embedding an implicit model of farmers' behavior. Collected ensemble datasets are bias-corrected and downscaled using a stochastic weather generator, in order to address the mismatch of the spatio-temporal scale between forecast data from GCMs and distributed crop simulation model. The agronomic model is first simulated using the forecast information (ex-ante), followed by a second run with actual climate (ex-post). Multi-year simulations are performed to account for climate variability and the value of the different climate forecast is evaluated against the perfect foresight scenario based on the expected crop productivity as well as the land-use decisions. Our results show that not all the products generate beneficial effects to farmers and that the forecast errors might be amplified by the farmers decisions.

The International Arctic Buoy Programme (IABP)

NASA Astrophysics Data System (ADS)

Rigor, I. G.; Ortmeyer, M.

2003-12-01

The Arctic has undergone dramatic changes in weather, climate and environment. It should be noted that many of these changes were first observed and studied using data from the International Arctic Buoy Programme (IABP). For example, IABP data were fundamental to Walsh et al. (1996) showing that atmospheric pressure has decreased, Rigor et al. (2000) showing that air temperatures have increased, and to Proshutinsky and Johnson (1997); Steele and Boyd, (1998); Kwok, (2000); and Rigor et al. (2002) showing that the clockwise circulation of sea ice and the ocean has weakened. All these results relied heavily on data from the IABP. In addition to supporting these studies of climate change, the IABP observations are also used to forecast weather and ice conditions, validate satellite retrievals of environmental variables, to force, validate and initialize numerical models. Over 350 papers have been written using data from the IABP. The observations and datasets of the IABP data are one of the cornerstones for environmental forecasting and research in the Arctic.

Understanding and Seasonal Forecasting of multiscale droughts in China

NASA Astrophysics Data System (ADS)

Yuan, X.; Wang, L.; Wang, S.; Zhang, M.

2016-12-01

Droughts were climate anomalies that occurred naturally. But they have been altered by climate change and human interventions, and have covered a variety of spatiotemporal scales from seasonal/decadal droughts at regional to continental scales that are associated with large-scale climate anomalies and certain atmospheric circulation patterns, to flash droughts at local scales that are usually concurrent with heat extremes. Droughts have quite different implications across a number of sectors, with the considerations augmented from meteorological droughts to agricultural and hydrological droughts, where the latter could be affected by human activities directly. This raises a grand challenge to understand and predict droughts across scales in a changing environment. This presentation will be started by diagnosing an El Niño-induced meteorological drought that occurred over northern China (NC) last year, where the oceanic and atmospheric background are investigated, and the real-time prediction from Climate Forecast System version 2 (CFSv2) are diagnosed. The comparison between 2015 NC drought and other historical droughts are discussed, and a dynamical-statistical forecasting approach is being developed. Secondly, a rapidly developing agricultural drought event that termed as "flash droughts" accompanied by extreme heat, low soil moisture and high evapotranspiration (ET), occurred frequently around the world, and caused devastating impacts on crop yields and water supply. The increasing trend of flash droughts over China was tripled after the big El Niño event in 1997/98, but the warming hiatus does exist over many regions of China. The changes in flash droughts over China are being attributed by using multiple reanalysis data and the CMIP5 simulations. Lastly, the effects of human interventions on the drought propagation will be investigated over Yellow River basin in northern China. A comparison between SPI and standardized streamflow index indicates that the response of hydrological droughts to meteorological droughts becomes longer, and the duration and severity of hydrological droughts could be doubled or tripled with human interventions. The impact of human intervention on the hydrological drought predictability is being explored within the NMME/VIC forecasting framework.

International Collaboration in the field of GNSS-Meteorology and Climate Monitoring

NASA Astrophysics Data System (ADS)

Jones, J.; Guerova, G.; Dousa, J.; Bock, O.; Elgered, G.; Vedel, H.; Pottiaux, E.; de Haan, S.; Pacione, R.; Dick, G.; Wang, J.; Gutman, S. I.; Wickert, J.; Rannat, K.; Liu, G.; Braun, J. J.; Shoji, Y.

2012-12-01

International collaboration in the field of GNSS-meteorology and climate monitoring is essential, as severe weather and climate change have no respect for national boundaries. The use of Global Navigation Satellite Systems (GNSS) for meteorological purposes is an established atmospheric observing technique, which can accurately sense water vapour, the most abundant greenhouse gas, accounting for 60-70% of atmospheric warming. Severe weather forecasting is challenging, in part due to the high temporal and spatial variation of atmospheric water vapour. Water vapour is currently under-sampled and obtaining and exploiting more high-quality humidity observations is essential to severe weather forecasting and climate monitoring. A proposed EU COST Action (http://www.cost.eu) will address new and improved capabilities from concurrent developments in both GNSS and atmospheric communities to improve (short-range) weather forecasts and climate projections. For the first time, the synergy of the three GNSS systems, GPS, GLONASS and Galileo, will be used to develop new, advanced tropospheric products, stimulating the full potential exploitation of multi-GNSS water vapour estimates on a wide range of temporal and spatial scales, from real-time severe weather monitoring and forecasting to climate research. The Action will work in close collaboration with the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN), GNSS Precipitable Water Task Team (TT). GRUAN is a global reference observing network, designed to meet climate requirements and to fill a major void in the current global observing system. GRUAN observations will provide long-term, high-quality data to determine climatic trends and to constrain and validate data from space-based remote sensors. Ground-based GNSS PW was identified as a Priority 1 measurement for GRUAN, and the GNSS-PW TT's goal is to develop explicit guidance on hardware, software and data management practices to obtain GNSS PW measurements of consistent quality at all GRUAN sites. The GRUAN GNSS-PW TT and the proposed COST Action will look to expand the international framework already in place with the European E-GVAP programme to facilitate global collaboration to facilitate knowledge and data exchange.

Climate Web sites

NASA Astrophysics Data System (ADS)

Showstack, Randy

With the growing interest in extreme climate and weather events, the National Oceanic and Atmospheric Administration (NOAA) has set up a one-stop Web site. It includes data on tornadoes, hurricanes, and heavy rainfall, temperature extremes, global climate change, satellite images, and El Niño and La Niña. The Web address is http://www.ncdc.noaa.gov.Another good climate Web site is the La Niña Home Page. Set up by the Environmental and Societal Impacts Group of the National Center for Atmospheric Research, the site includes forecasts, data sources, impacts, and Internet links.

Modeling the influence of climate change on the mass balance of polychlorinated biphenyls in the Adriatic Sea.

PubMed

Lamon, Lara; MacLeod, Matthew; Marcomini, Antonio; Hungerbühler, Konrad

2012-05-01

Climate forcing is forecasted to influence the Adriatic Sea region in a variety of ways, including increasing temperature, and affecting wind speeds, marine currents, precipitation and water salinity. The Adriatic Sea is intensively developed with agriculture, industry, and port activities that introduce pollutants to the environment. Here, we developed and applied a Level III fugacity model for the Adriatic Sea to estimate the current mass balance of polychlorinated biphenyls in the Sea, and to examine the effects of a climate change scenario on the distribution of these pollutants. The model's performance was evaluated for three PCB congeners against measured concentrations in the region using environmental parameters estimated from the 20th century climate scenario described in the Special Report on Emission Scenarios (SRES) by the IPCC, and using Monte Carlo uncertainty analysis. We find that modeled fugacities of PCBs in air, water and sediment of the Adriatic are in good agreement with observations. The model indicates that PCBs in the Adriatic Sea are closely coupled with the atmosphere, which acts as a net source to the water column. We used model experiments to assess the influence of changes in temperature, wind speed, precipitation, marine currents, particulate organic carbon and air inflow concentrations forecast in the IPCC A1B climate change scenario on the mass balance of PCBs in the Sea. Assuming an identical PCBs' emission profile (e.g. use pattern, treatment/disposal of stockpiles, mode of entry), modeled fugacities of PCBs in the Adriatic Sea under the A1B climate scenario are higher because higher temperatures reduce the fugacity capacity of air, water and sediments, and because diffusive sources to the air are stronger. Copyright © 2012 Elsevier Ltd. All rights reserved.

Prospects for seasonal forecasting of summer drought and low river flow anomalies in England and Wales

NASA Astrophysics Data System (ADS)

Wedgbrow, C. S.; Wilby, R. L.; Fox, H. R.; O'Hare, G.

2002-02-01

Future climate change scenarios suggest enhanced temporal and spatial gradients in water resources across the UK. Provision of seasonal forecast statistics for surface climate variables could alleviate some negative effects of climate change on water resource infrastructure. This paper presents a preliminary investigation of spatial and temporal relationships between large-scale North Atlantic climatic indices, drought severity and river flow anomalies in England and Wales. Potentially useful predictive relationships are explored between winter indices of the Polar-Eurasian (POL) teleconnection pattern, the North Atlantic oscillation (NAO), North Atlantic sea surface temperature anomalies (SSTAs), and the summer Palmer drought severity index (PDSI) and reconstructed river flows in England and Wales. Correlation analyses, coherence testing and an index of forecast potential, demonstrate that preceding winter values of the POL index, SSTA (and to a lesser extent the NAO), provide indications of summer and early autumn drought severity and river flow anomalies in parts of northwest, southwest and southeast England. Correlation analyses demonstrate that positive winter anomalies of T1, POL index and NAO index are associated with negative PDSI (i.e. drought) across eastern parts of the British Isles in summer (r < 0.51). Coherence tests show that a positive winter SSTA (1871-1995) and POL index (1950-95) have preceded below-average summer river flows in the northwest and southwest of England and Wales in 70 to 100% of summers. The same rivers have also experienced below-average flows during autumn following negative winter phases of the NAO index in 64 to 93% of summers (1865-1995). Possible explanations for the predictor-predictand relationships are considered, including the memory of groundwater, and ocean-atmosphere coupling, and regional manifestations of synoptic rainfall processes. However, further research is necessary to increase the number of years and predictor variables from which it is possible to derive rules that may be useful for forecasting.

Tolerance adaptation and precipitation changes complicate latitudinal patterns of climate change impacts.

PubMed

Bonebrake, Timothy C; Mastrandrea, Michael D

2010-07-13

Global patterns of biodiversity and comparisons between tropical and temperate ecosystems have pervaded ecology from its inception. However, the urgency in understanding these global patterns has been accentuated by the threat of rapid climate change. We apply an adaptive model of environmental tolerance evolution to global climate data and climate change model projections to examine the relative impacts of climate change on different regions of the globe. Our results project more adverse impacts of warming on tropical populations due to environmental tolerance adaptation to conditions of low interannual variability in temperature. When applied to present variability and future forecasts of precipitation data, the tolerance adaptation model found large reductions in fitness predicted for populations in high-latitude northern hemisphere regions, although some tropical regions had comparable reductions in fitness. We formulated an evolutionary regional climate change index (ERCCI) to additionally incorporate the predicted changes in the interannual variability of temperature and precipitation. Based on this index, we suggest that the magnitude of climate change impacts could be much more heterogeneous across latitude than previously thought. Specifically, tropical regions are likely to be just as affected as temperate regions and, in some regions under some circumstances, possibly more so.

Assessing the Effects of Climate Variability on Orange Yield in Florida to Reduce Production Forecast Errors

NASA Astrophysics Data System (ADS)

Concha Larrauri, P.

2015-12-01

Orange production in Florida has experienced a decline over the past decade. Hurricanes in 2004 and 2005 greatly affected production, almost to the same degree as strong freezes that occurred in the 1980's. The spread of the citrus greening disease after the hurricanes has also contributed to a reduction in orange production in Florida. The occurrence of hurricanes and diseases cannot easily be predicted but the additional effects of climate on orange yield can be studied and incorporated into existing production forecasts that are based on physical surveys, such as the October Citrus forecast issued every year by the USDA. Specific climate variables ocurring before and after the October forecast is issued can have impacts on flowering, orange drop rates, growth, and maturation, and can contribute to the forecast error. Here we present a methodology to incorporate local climate variables to predict the USDA's orange production forecast error, and we study the local effects of climate on yield in different counties in Florida. This information can aid farmers to gain an insight on what is to be expected during the orange production cycle, and can help supply chain managers to better plan their strategy.

Plastic and evolutionary responses to climate change in fish

PubMed Central

Crozier, Lisa G; Hutchings, Jeffrey A

2014-01-01

The physical and ecological ‘fingerprints’ of anthropogenic climate change over the past century are now well documented in many environments and taxa. We reviewed the evidence for phenotypic responses to recent climate change in fish. Changes in the timing of migration and reproduction, age at maturity, age at juvenile migration, growth, survival and fecundity were associated primarily with changes in temperature. Although these traits can evolve rapidly, only two studies attributed phenotypic changes formally to evolutionary mechanisms. The correlation-based methods most frequently employed point largely to ‘fine-grained’ population responses to environmental variability (i.e. rapid phenotypic changes relative to generation time), consistent with plastic mechanisms. Ultimately, many species will likely adapt to long-term warming trends overlaid on natural climate oscillations. Considering the strong plasticity in all traits studied, we recommend development and expanded use of methods capable of detecting evolutionary change, such as the long term study of selection coefficients and temporal shifts in reaction norms, and increased attention to forecasting adaptive change in response to the synergistic interactions of the multiple selection pressures likely to be associated with climate change. PMID:24454549

Plastic and evolutionary responses to climate change in fish.

PubMed

Crozier, Lisa G; Hutchings, Jeffrey A

2014-01-01

The physical and ecological 'fingerprints' of anthropogenic climate change over the past century are now well documented in many environments and taxa. We reviewed the evidence for phenotypic responses to recent climate change in fish. Changes in the timing of migration and reproduction, age at maturity, age at juvenile migration, growth, survival and fecundity were associated primarily with changes in temperature. Although these traits can evolve rapidly, only two studies attributed phenotypic changes formally to evolutionary mechanisms. The correlation-based methods most frequently employed point largely to 'fine-grained' population responses to environmental variability (i.e. rapid phenotypic changes relative to generation time), consistent with plastic mechanisms. Ultimately, many species will likely adapt to long-term warming trends overlaid on natural climate oscillations. Considering the strong plasticity in all traits studied, we recommend development and expanded use of methods capable of detecting evolutionary change, such as the long term study of selection coefficients and temporal shifts in reaction norms, and increased attention to forecasting adaptive change in response to the synergistic interactions of the multiple selection pressures likely to be associated with climate change.

Estimating daily climatologies for climate indices derived from climate model data and observations

PubMed Central

Mahlstein, Irina; Spirig, Christoph; Liniger, Mark A; Appenzeller, Christof

2015-01-01

Climate indices help to describe the past, present, and the future climate. They are usually closer related to possible impacts and are therefore more illustrative to users than simple climate means. Indices are often based on daily data series and thresholds. It is shown that the percentile-based thresholds are sensitive to the method of computation, and so are the climatological daily mean and the daily standard deviation, which are used for bias corrections of daily climate model data. Sample size issues of either the observed reference period or the model data lead to uncertainties in these estimations. A large number of past ensemble seasonal forecasts, called hindcasts, is used to explore these sampling uncertainties and to compare two different approaches. Based on a perfect model approach it is shown that a fitting approach can improve substantially the estimates of daily climatologies of percentile-based thresholds over land areas, as well as the mean and the variability. These improvements are relevant for bias removal in long-range forecasts or predictions of climate indices based on percentile thresholds. But also for climate change studies, the method shows potential for use. Key Points More robust estimates of daily climate characteristics Statistical fitting approach Based on a perfect model approach PMID:26042192

The effects of global climate change on Southeast Asia: A survey of likely impacts and problems of adaptation

NASA Technical Reports Server (NTRS)

Njoto, Sukrisno; Howe, Charles W.

1991-01-01

Study results indicate the likelihood of significant net damages from climate change, in particular damages from sea-level rise and higher temperatures that seem unlikely to be offset by favorable shifts in precipitation and carbon dioxide. Also indicated was the importance of better climate models, in particular models that can calculate climate change on a regional scale appropriate to policy-making. In spite of this potential for damage, there seems to be a low level of awareness and concern, probably caused by the higher priority given to economic growth and reinforced by the great uncertainty in the forecasts. The common property nature of global environment systems also leads to a feeling of helplessness on the part of country governments.

Observed Changes at the Surface of the Arctic Ocean

NASA Astrophysics Data System (ADS)

Ortmeyer, M.; Rigor, I.

2004-12-01

The Arctic has long been considered a harbinger of global climate change since simulations with global climate models predict that if the concentration of CO2 in the atmosphere doubles, the Arctic would warm by more than 5°C, compared to a warming of 2°C for subpolar regions (Manabe et al., 1991). And indeed, studies of the observational records show polar amplification of the warming trends (e.g. Serreze and Francis, 2004). These temperature trends are accompanied by myriad concurrent changes in Arctic climate. One of the first indicators of Arctic climate change was found by Walsh et al. (1996) using sea level pressure (SLP) data from the International Arctic Buoy Programme (IABP, http://iabp.apl.washington.edu). In this study, they showed that SLP over the Arctic Ocean decreased by over 4 hPa from 1979 - 1994. The decreases in SLP (winds) over the Arctic Ocean, forced changes in the circulation of sea ice and the surface ocean currents such that the Beaufort Gyre is reduced in size and speed (e.g. Rigor et al., 2002). Data from the IABP has also been assimilated into the global surface air temperature (SAT) climatologies (e.g. Jones et al. 1999), and the IABP SAT analysis shows that the temperature trends noted over land extend out over the Arctic Ocean. Specifically, Rigor et al. (2000) found warming trends in SAT over the Arctic Ocean during win¬ter and spring, with values as high as 2°C/decade in the eastern Arctic during spring. It should be noted that many of the changes in Arctic climate were first observed or explained using data from the IABP. The observations from IABP have been one of the cornerstones for environmental forecasting and studies of climate and climate change. These changes have a profound impact on wildlife and people. Many species and cultures depend on the sea ice for habitat and subsistence. Thus, monitoring the Arctic Ocean is crucial not only for our ability to detect climate change, but also to improve our understanding of the Arctic and global climate system, and for forecasting weather and sea ice conditions. The IABP provides the longest continuing record of observations for the Arctic Ocean.

Projected climate-induced habitat loss for salmonids in the John Day River network, Oregon, U.S.A.

USGS Publications Warehouse

Ruesch, Aaron S.; Torgersen, Christian E.; Lawler, Joshua J.; Olden, Julian D.; Peterson, Erin E.; Volk, Carol J.; Lawrence, David J.

2012-01-01

Climate change will likely have profound effects on cold-water species of freshwater fishes. As temperatures rise, cold-water fish distributions may shift and contract in response. Predicting the effects of projected stream warming in stream networks is complicated by the generally poor correlation between water temperature and air temperature. Spatial dependencies in stream networks are complex because the geography of stream processes is governed by dimensions of flow direction and network structure. Therefore, forecasting climate-driven range shifts of stream biota has lagged behind similar terrestrial modeling efforts. We predicted climate-induced changes in summer thermal habitat for 3 cold-water fish species—juvenile Chinook salmon, rainbow trout, and bull trout (Oncorhynchus tshawytscha, O. mykiss, and Salvelinus confluentus, respectively)—in the John Day River basin, northwestern United States. We used a spatially explicit statistical model designed to predict water temperature in stream networks on the basis of flow and spatial connectivity. The spatial distribution of stream temperature extremes during summers from 1993 through 2009 was largely governed by solar radiation and interannual extremes of air temperature. For a moderate climate change scenario, estimated declines by 2100 in the volume of habitat for Chinook salmon, rainbow trout, and bull trout were 69–95%, 51–87%, and 86–100%, respectively. Although some restoration strategies may be able to offset these projected effects, such forecasts point to how and where restoration and management efforts might focus.

Regional Climate Change across North America in 2030 Projected from RCP6.0

NASA Astrophysics Data System (ADS)

Otte, T.; Nolte, C. G.; Faluvegi, G.; Shindell, D. T.

2012-12-01

Projecting climate change scenarios to local scales is important for understanding and mitigating the effects of climate change on society and the environment. Many of the general circulation models (GCMs) that are participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture local changes in temperature and precipitation extremes. We seek to project the GCM's large-scale climate change signal to the local scale using a regional climate model (RCM) by applying dynamical downscaling techniques. The RCM will be used to better understand the local changes of temperature and precipitation extremes that may result from a changing climate. In this research, downscaling techniques that we developed with historical data are now applied to GCM fields. Results from downscaling NASA/GISS ModelE2 simulations of the IPCC AR5 Representative Concentration Pathway (RCP) scenario 6.0 will be shown. The Weather Research and Forecasting (WRF) model has been used as the RCM to downscale decadal time slices for ca. 2000 and ca. 2030 over North America and illustrate potential changes in regional climate that are projected by ModelE2 and WRF under RCP6.0. The analysis focuses on regional climate fields that most strongly influence the interactions between climate change and air quality. In particular, an analysis of extreme temperature and precipitation events will be presented.

The Pacific Northwest's Climate Impacts Group: Climate Science in the Public Interest

NASA Astrophysics Data System (ADS)

Mantua, N.; Snover, A.

2006-12-01

Since its inception in 1995, the University of Washington's Climate Impacts Group (CIG) (funded under NOAA's Regional Integrated Science and Assessments (RISA) Program) has become the leader in exploring the impacts of climate variability and climate change on natural and human systems in the U.S. Pacific Northwest (PNW), specifically climate impacts on water, forest, fish and coastal resource systems. The CIG's research provides PNW planners, decision makers, resource managers, local media, and the general public with valuable knowledge of ways in which the region's key natural resources are vulnerable to changes in climate, and how this vulnerability can be reduced. The CIG engages in climate science in the public interest, conducting original research on the causes and consequences of climate variability and change for the PNW and developing forecasts and decision support tools to support the use of this information in federal, state, local, tribal, and private sector resource management decisions. The CIG's focus on the intersection of climate science and public policy has placed the CIG nationally at the forefront of regional climate impacts assessment and integrated analysis.

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

NASA Technical Reports Server (NTRS)

Mazrooei, Amirhossein; Sinah, Tusshar; Sankarasubramanian, A.; Kumar, Sujay V.; Peters-Lidard, Christa D.

2015-01-01

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

Enabling Philippine Farmers to Adapt to Climate Variability Using Seasonal Climate and Weather Forecast with a Crop Simulation Model in an SMS-based Farmer Decision Support System

NASA Astrophysics Data System (ADS)

Ebardaloza, J. B. R.; Trogo, R.; Sabido, D. J.; Tongson, E.; Bagtasa, G.; Balderama, O. F.

2015-12-01

Corn farms in the Philippines are rainfed farms, hence, it is of utmost importance to choose the start of planting date so that the critical growth stages that are in need of water will fall on dates when there is rain. Most farmers in the Philippines use superstitions and traditions as basis for farming decisions such as when to start planting [1]. Before climate change, superstitions like planting after a feast day of a saint has worked for them but with the recent progression of climate change, farmers now recognize that there is a need for technological intervention [1]. The application discussed in this paper presents a solution that makes use of meteorological station sensors, localized seasonal climate forecast, localized weather forecast and a crop simulation model to provide recommendations to farmers based on the crop cultivar, soil type and fertilizer type used by farmers. It is critical that the recommendations given to farmers are not generic as each farmer would have different needs based on their cultivar, soil, fertilizer, planting schedule and even location [2]. This application allows the farmer to inquire about whether it will rain in the next seven days, the best date to start planting based on the potential yield upon harvest, when to apply fertilizer and by how much, when to water and by how much. Short messaging service (SMS) is the medium chosen for this application because while mobile penetration in the Philippines is as high as 101%, the smart phone penetration is only at 15% [3]. SMS has been selected as it has been identified as the most effective way of reaching farmers with timely agricultural information and knowledge [4,5]. The recommendations while derived from making use of Automated Weather Station (AWS) sensor data, Weather Research Forecasting (WRF) models and DSSAT 4.5 [9], are translated into the local language of the farmers and in a format that is easily understood as recommended in [6,7,8]. A pilot study has been started in May 2015 and the harvest of this pilot season will be September 2015.

Simulating seasonal tropical cyclone intensities at landfall along the South China coast

NASA Astrophysics Data System (ADS)

Lok, Charlie C. F.; Chan, Johnny C. L.

2018-04-01

A numerical method is developed using a regional climate model (RegCM3) and the Weather Forecast and Research (WRF) model to predict seasonal tropical cyclone (TC) intensities at landfall for the South China region. In designing the model system, three sensitivity tests have been performed to identify the optimal choice of the RegCM3 model domain, WRF horizontal resolution and WRF physics packages. Driven from the National Centers for Environmental Prediction Climate Forecast System Reanalysis dataset, the model system can produce a reasonable distribution of TC intensities at landfall on a seasonal scale. Analyses of the model output suggest that the strength and extent of the subtropical ridge in the East China Sea are crucial to simulating TC landfalls in the Guangdong and Hainan provinces. This study demonstrates the potential for predicting TC intensities at landfall on a seasonal basis as well as projecting future climate changes using numerical models.

An Evaluation of the NOAA Climate Forecast System Subseasonal Forecasts

NASA Astrophysics Data System (ADS)

Mass, C.; Weber, N.

2016-12-01

This talk will describe a multi-year evaluation of the 1-5 week forecasts of the NOAA Climate Forecasting System (CFS) over the globe, North America, and the western U.S. Forecasts are evaluated for both specific times and for a variety of time-averaging periods. Initial results show a loss of predictability at approximately three weeks, with sea surface temperature retaining predictability longer than atmospheric variables. It is shown that a major CFS problem is an inability to realistically simulate propagating convection in the tropics, with substantial implications for midlatitude teleconnections and subseasonal predictability. The inability of CFS to deal with tropical convection will be discussed in connection with the prediction of extreme climatic events over the midlatitudes.

An intercomparison of approaches for improving operational seasonal streamflow forecasts

NASA Astrophysics Data System (ADS)

Mendoza, Pablo A.; Wood, Andrew W.; Clark, Elizabeth; Rothwell, Eric; Clark, Martyn P.; Nijssen, Bart; Brekke, Levi D.; Arnold, Jeffrey R.

2017-07-01

For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches - statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) - and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction - HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions. Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions.

Integrating SPOT-VEGETATION 13-yr time series and land-surface modelling to forecast the terrestrial carbon dynamics in a changing climate - The VEGECLIM project: achievements and lessons learned

NASA Astrophysics Data System (ADS)

Defourny, Pierre; Verbeeck, Hans; Moreau, Inès; De Weirdt, Marjolein; Verhegghen, Astrid; Kibambe-Lubamba, Jean-Paul; Jungers, Quentin; Maignan, Fabienne; Najdovski, Nicolas; Poulter, Benjamin; MacBean, Natasha; Peylin, Philippe

2014-05-01

Vegetation is a major carbon sink and is as such a key component of the international response to climate change caused by the build-up of greenhouse gases in the atmosphere. However, anthropogenic disturbances like deforestation are the primary mechanism that changes ecosystems from carbon sinks to sources, and are hardly included in the current carbon modelling approaches. Moreover, in tropical regions, the seasonal/interannual variability of carbon fluxes is still uncertain and a weak or even no seasonality is taken into account in global vegetation models. In the context of climate change and mitigation policies like "Reducing Emissions from Deforestation and Forest Degradation in Developing Countries" (REDD), it is particularly important to be able to quantify and forecast the vegetation dynamics and carbon fluxes in these regions. The overall objective of the VEGECLIM project is to increase our knowledge on the terrestrial carbon cycle in tropical regions and to improve the forecast of the vegetation dynamics and carbon stocks and fluxes under different climate-change and deforestation scenarios. Such an approach aims to determine whether the African terrestrial carbon balance will remain a net sink or could become a carbon source by the end of the century, according to different climate-change and deforestation scenarios. The research strategy is to integrate the information of the land surface characterizations obtained from 13 years of consistent SPOT-VEGETATION time series (land cover, vegetation phenology through vegetation indices such as the Enhanced Vegetation Index (EVI)) as well as in-situ carbon flux data into the process based ORCHIDEE global vegetation model, capable of simulating vegetation dynamics and carbon balance. Key challenge of this project was to bridge the gap between the land cover and the land surface model teams. Several improvements of the ORCHIDEE model have been realized such as a new seasonal leaf dynamics for tropical evergreen forests, the introduction of spatial soil phosphorus to improve the spatial distribution of simulated woody biomass and an assimilation of smoothed seasonal pattern of satellite-based EVI used as a proxy to vegetation productivity. The outputs of the ORCHIDEE simulations over both Amazon and Congo Basins are discussed with regards to the observed phenology by remote sensing.

Managing the Nation's water in a changing climate

USGS Publications Warehouse

Lins, H.F.; Stakhiv, E.Z.

1998-01-01

Among the many concerns associated with global climate change, the potential effects on water resources are frequently cited as the most worrisome. In contrast, those who manage water resources do not rate climatic change among their top planning and operational concerns. The difference in these views can be associated with how water managers operate their systems and the types of stresses, and the operative time horizons, that affect the Nation's water resources infrastructure. Climate, or more precisely weather, is an important variable in the management of water resources at daily to monthly time scales because water resources systems generally are operated on a daily basis. At decadal to centennial time scales, though, climate is much less important because (1) forecasts, particularly of regional precipitation, are extremely uncertain over such time periods, and (2) the magnitude of effects due to changes in climate on water resources is small relative to changes in other variables such as population, technology, economics, and environmental regulation. Thus, water management agencies find it difficult to justify changing design features or operating rules on the basis of simulated climatic change at the present time, especially given that reservoir-design criteria incorporate considerable buffering capacity for extreme meteorological and hydrological events.

"Going the Extra Mile in Downscaling: Why Downscaling is not ...

EPA Pesticide Factsheets

This presentation provides an example of doing additional work for preprocessing global climate model data for use in regional climate modeling simulations with the Weather Research and Forecasting (WRF) model. In this presentation, results from 15 months of downscaling the Community Earth System Model (CESM) were shown, both using the out-of-the-box downscaling of CESM and also with a modification to setting the inland lake temperatures. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.

Seasonal Forecast Skill And Teleconnections Over East Africa

NASA Astrophysics Data System (ADS)

MacLeod, D.; Palmer, T.

2017-12-01

Many people living in East Africa are significantly exposed to risks arising from climate variability. The region experiences two rainy seasons and poor performance of either or both of these (such as seen recently in 2016/17) reduces agricultural productivity and threatens food security. In combination with other factors this can lead to famine. By utilizing seasonal climate forecasts, preparatory actions can be taken in order to mitigate the risks arising from such climate variability. As part of the project ForPAc: "Towards forecast-based preparedness action", we are working with humanitarian agencies in Kenya to build such early warning systems on subseasonal-to-seasonal timescales. Here, the seasonal predictability and forecast skill of the two East African rainy seasons will be presented. Results from the new ECMWF operational forecasting system SEAS5 will be shown and compared to the previous System 4. Analysis of a new 110 year long atmosphere-only simulation will also be discussed, demonstrating impacts of atmosphere-ocean coupling as well as putting operational forecast skill in a long-term context. Particular focus will be given to the model representation of teleconnections of seasonal climate with global sea surface temperatures; highlighting sources of forecast error and informing future model development.

Towards seasonal Arctic shipping route predictions

NASA Astrophysics Data System (ADS)

Melia, N.; Haines, K.; Hawkins, E.; Day, J. J.

2017-08-01

The continuing decline in Arctic sea-ice will likely lead to increased human activity and opportunities for shipping in the region, suggesting that seasonal predictions of route openings will become ever more important. Here we present results from a set of ‘perfect model’ experiments to assess the predictability characteristics of the opening of Arctic sea routes. We find skilful predictions of the upcoming summer shipping season can be made from as early as January, although typically forecasts show lower skill before a May ‘predictability barrier’. We demonstrate that in forecasts started from January, predictions of route opening date are twice as uncertain as predicting the closing date and that the Arctic shipping season is becoming longer due to climate change, with later closing dates mostly responsible. We find that predictive skill is state dependent with predictions for high or low ice years exhibiting greater skill than medium ice years. Forecasting the fastest open water route through the Arctic is accurate to within 200 km when predicted from July, a six-fold increase in accuracy compared to forecasts initialised from the previous November, which are typically no better than climatology. Finally we find that initialisation of accurate summer sea-ice thickness information is crucial to obtain skilful forecasts, further motivating investment into sea-ice thickness observations, climate models, and assimilation systems.

Can Regional Climate Models Improve Warm Season Forecasts in the North American Monsoon Region?

NASA Astrophysics Data System (ADS)

Dominguez, F.; Castro, C. L.

2009-12-01

The goal of this work is to improve warm season forecasts in the North American Monsoon Region. To do this, we are dynamically downscaling warm season CFS (Climate Forecast System) reforecasts from 1982-2005 for the contiguous U.S. using the Weather Research and Forecasting (WRF) regional climate model. CFS is the global coupled ocean-atmosphere model used by the Climate Prediction Center (CPC), a branch of the National Center for Environmental Prediction (NCEP), to provide official U.S. seasonal climate forecasts. Recently, NCEP has produced a comprehensive long-term retrospective ensemble CFS reforecasts for the years 1980-2005. These reforecasts show that CFS model 1) has an ability to forecast tropical Pacific SSTs and large-scale teleconnection patterns, at least as evaluated for the winter season; 2) has greater skill in forecasting winter than summer climate; and 3) demonstrates an increase in skill when a greater number of ensembles members are used. The decrease in CFS skill during the warm season is due to the fact that the physical mechanisms of rainfall at this time are more related to mesoscale processes, such as the diurnal cycle of convection, low-level moisture transport, propagation and organization of convection, and surface moisture recycling. In general, these are poorly represented in global atmospheric models. Preliminary simulations for years with extreme summer climate conditions in the western and central U.S. (specifically 1988 and 1993) show that CFS-WRF simulations can provide a more realistic representation of convective rainfall processes. Thus a RCM can potentially add significant value in climate forecasting of the warm season provided the downscaling methodology incorporates the following: 1) spectral nudging to preserve the variability in the large scale circulation while still permitting the development of smaller-scale variability in the RCM; and 2) use of realistic soil moisture initial condition, in this case provided by the North American Regional Reanalysis. With these conditions, downscaled CFS-WRF reforecast simulations can produce realistic continental-scale patterns of warm season precipitation. This includes a reasonable representation of the North American monsoon in the southwest U.S. and northwest Mexico, which is notoriously difficult to represent in a global atmospheric model. We anticipate that this research will help lead the way toward substantially improved real time operational forecasts of North American summer climate with a RCM.

Forecasting consequences of changing sea ice availability for Pacific walruses

USGS Publications Warehouse

Udevitz, Mark S.; Jay, Chadwick V.; Taylor, Rebecca; Fischbach, Anthony S.; Beatty, William S.; Noren, Shawn R.

2017-01-01

The accelerating rate of anthropogenic alteration and disturbance of environments has increased the need for forecasting effects of environmental change on fish and wildlife populations. Models linking projections of environmental change with behavioral responses and bioenergetic effects can provide a basis for these forecasts. There is particular interest in forecasting effects of projected reductions in sea ice availability on Pacific walruses (Odobenus rosmarus divergens). Declining extent of summer sea ice in the Chukchi Sea has caused Pacific walruses to increase use of coastal haulouts and decrease use of more productive offshore feeding areas. Such climate-induced changes in distribution and behavior could ultimately affect the status of the population. We developed behavioral models to relate changes in sea ice availability to adult female walrus movements and activity levels, and adapted previously developed bioenergetics models to relate those activity levels to energy requirements and the ability to meet those requirements. We then linked these models to general circulation model projections of future ice availability to forecast autumn body condition for female walruses during mid- and late-century time periods. Our results suggest that as sea ice becomes less available in the Chukchi Sea, female walruses will spend more time in the southwestern region of that sea, less time resting, and less time foraging. Median forecasted autumn body masses were 7–12% lower in future scenarios than during recent times, but posterior distributions broadly overlapped and median forecasted seasonal mass losses (15–34%) were comparable to seasonal mass losses routinely experienced by other pinnipeds. These seasonal reductions in body condition would be unlikely to result in demographic effects, but if walruses were unable to rebuild endogenous reserves while wintering in the Bering Sea, cumulative effects could have implications for reproduction and survival, ultimately affecting the status of the Pacific walrus population. Our approach provides a general framework for forecasting consequences of the broad range of environmental changes and anthropogenic disturbances that may affect bioenergetics through behavioral responses or changes in prey availability.

Climate change and children's health.

PubMed

Bernstein, Aaron S; Myers, Samuel S

2011-04-01

To present the latest data that demonstrate how climate change affects children's health and to identify the principal ways in which climate change puts children's health at risk. Data continue to emerge that further implicate climate change as contributing to health burdens in children. Climate models have become even more sophisticated and consistently forecast that greenhouse gas emissions will lead to higher mean temperatures that promote more intense storms and droughts, both of which have profound implications for child health. Recent climate models shed light upon the spread of vector-borne disease, including Lyme disease in North America and malaria in Africa. Modeling studies have found that conditions conducive to forest fires, which generate harmful air pollutants and damage agriculture, are likely to become more prevalent in this century due to the effects of greenhouse gases added to earth's atmosphere. Through many pathways, and in particular via placing additional stress upon the availability of food, clean air, and clean water and by potentially expanding the burden of disease from certain vector-borne diseases, climate change represents a major threat to child health. Pediatricians have already seen and will increasingly see the adverse health effects of climate change in their practices. Because of this, and many other reasons, pediatricians have a unique capacity to help resolve the climate change problem.

The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change.

PubMed

Valladares, Fernando; Matesanz, Silvia; Guilhaumon, François; Araújo, Miguel B; Balaguer, Luis; Benito-Garzón, Marta; Cornwell, Will; Gianoli, Ernesto; van Kleunen, Mark; Naya, Daniel E; Nicotra, Adrienne B; Poorter, Hendrik; Zavala, Miguel A

2014-11-01

Species are the unit of analysis in many global change and conservation biology studies; however, species are not uniform entities but are composed of different, sometimes locally adapted, populations differing in plasticity. We examined how intraspecific variation in thermal niches and phenotypic plasticity will affect species distributions in a warming climate. We first developed a conceptual model linking plasticity and niche breadth, providing five alternative intraspecific scenarios that are consistent with existing literature. Secondly, we used ecological niche-modeling techniques to quantify the impact of each intraspecific scenario on the distribution of a virtual species across a geographically realistic setting. Finally, we performed an analogous modeling exercise using real data on the climatic niches of different tree provenances. We show that when population differentiation is accounted for and dispersal is restricted, forecasts of species range shifts under climate change are even more pessimistic than those using the conventional assumption of homogeneously high plasticity across a species' range. Suitable population-level data are not available for most species so identifying general patterns of population differentiation could fill this gap. However, the literature review revealed contrasting patterns among species, urging greater levels of integration among empirical, modeling and theoretical research on intraspecific phenotypic variation. © 2014 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.

Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast.

PubMed

Ceglar, Andrej; Toreti, Andrea; Prodhomme, Chloe; Zampieri, Matteo; Turco, Marco; Doblas-Reyes, Francisco J

2018-01-22

Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.

ESTIMATING THE DISTRIBUTION OF HARVESTED ESTUARINE BIVALVES WITH HABITAT SUITABILITY MODELS AND UNDERSTANDING THE IMPLICATIONS UNDER A CHANGING CLIMATE

EPA Science Inventory

Habitat suitability models are useful to forecast how environmental change may affect the abundance or distribution of species of interest. In the case of harvested bivalves, those models may be used to estimate the vulnerability of this valued ecosystem good to stressors. Usin...

The effects of temperature on nest predation by mammals, birds, and snakes

Treesearch

W. Andrew Cox; F.R. Thompson III; J.L. Reidy

2013-01-01

Understanding how weather influences survival and reproduction is an important component of forecasting how climate change will influence wildlife population viability. Nest predation is the primary source of reproductive failure for passerine birds and can change in response to temperature. However, it is unclear which predator species are responsible for such...

Predictability Analysis of PM10 Concentrations in Budapest

NASA Astrophysics Data System (ADS)

Ferenczi, Zita

2013-04-01

Climate, weather and air quality may have harmful effects on human health and environment. Over the past few hundred years we had to face the changes in climate in parallel with the changes in air quality. These observed changes in climate, weather and air quality continuously interact with each other: pollutants are changing the climate, thus changing the weather, but climate also has impacts on air quality. The increasing number of extreme weather situations may be a result of climate change, which could create favourable conditions for rising of pollutant concentrations. Air quality in Budapest is determined by domestic and traffic emissions combined with the meteorological conditions. In some cases, the effect of long-range transport could also be essential. While the time variability of the industrial and traffic emissions is not significant, the domestic emissions increase in winter season. In recent years, PM10 episodes have caused the most critical air quality problems in Budapest, especially in winter. In Budapest, an air quality network of 11 stations detects the concentration values of different pollutants hourly. The Hungarian Meteorological Service has developed an air quality prediction model system for the area of Budapest. The system forecasts the concentration of air pollutants (PM10, NO2, SO2 and O3) for two days in advance. In this work we used meteorological parameters and PM10 data detected by the stations of the air quality network, as well as the forecasted PM10 values of the air quality prediction model system. In this work we present the evaluation of PM10 predictions in the last two years and the most important meteorological parameters affecting PM10 concentration. The results of this analysis determine the effect of the meteorological parameters and the emission of aerosol particles on the PM10 concentration values as well as the limits of this prediction system.

Different types of drifts in two seasonal forecast systems and their dependence on ENSO

NASA Astrophysics Data System (ADS)

Hermanson, L.; Ren, H.-L.; Vellinga, M.; Dunstone, N. D.; Hyder, P.; Ineson, S.; Scaife, A. A.; Smith, D. M.; Thompson, V.; Tian, B.; Williams, K. D.

2017-11-01

Seasonal forecasts using coupled ocean-atmosphere climate models are increasingly employed to provide regional climate predictions. For the quality of forecasts to improve, regional biases in climate models must be diagnosed and reduced. The evolution of biases as initialized forecasts drift away from the observations is poorly understood, making it difficult to diagnose the causes of climate model biases. This study uses two seasonal forecast systems to examine drifts in sea surface temperature (SST) and precipitation, and compares them to the long-term bias in the free-running version of each model. Drifts are considered from daily to multi-annual time scales. We define three types of drift according to their relation with the long-term bias in the free-running model: asymptoting, overshooting and inverse drift. We find that precipitation almost always has an asymptoting drift. SST drifts on the other hand, vary between forecasting systems, where one often overshoots and the other often has an inverse drift. We find that some drifts evolve too slowly to have an impact on seasonal forecasts, even though they are important for climate projections. The bias found over the first few days can be very different from that in the free-running model, so although daily weather predictions can sometimes provide useful information on the causes of climate biases, this is not always the case. We also find that the magnitude of equatorial SST drifts, both in the Pacific and other ocean basins, depends on the El Niño Southern Oscillation (ENSO) phase. Averaging over all hindcast years can therefore hide the details of ENSO state dependent drifts and obscure the underlying physical causes. Our results highlight the need to consider biases across a range of timescales in order to understand their causes and develop improved climate models.

Evaluating the extreme precipitation events using a mesoscale atmopshere model

NASA Astrophysics Data System (ADS)

Yucel, I.; Onen, A.

2012-04-01

Evidence is showing that global warming or climate change has a direct influence on changes in precipitation and the hydrological cycle. Extreme weather events such as heavy rainfall and flooding are projected to become much more frequent as climate warms. Mesoscale atmospheric models coupled with land surface models provide efficient forecasts for meteorological events in high lead time and therefore they should be used for flood forecasting and warning issues as they provide more continuous monitoring of precipitation over large areas. This study examines the performance of the Weather Research and Forecasting (WRF) model in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in West Black Sea Region of Turkey. Extreme precipitation events usually resulted in flood conditions as an associated hydrologic response of the basin. The performance of the WRF system is further investigated by using the three dimensional variational (3D-VAR) data assimilation scheme within WRF. WRF performance with and without data assimilation at high spatial resolution (4 km) is evaluated by making comparison with gauge precipitation and satellite-estimated rainfall data from Multi Precipitation Estimates (MPE). WRF-derived precipitation showed capabilities in capturing the timing of the precipitation extremes and in some extent spatial distribution and magnitude of the heavy rainfall events. These precipitation characteristics are enhanced with the use of 3D-VAR scheme in WRF system. Data assimilation improved area-averaged precipitation forecasts by 9 percent and at some points there exists quantitative match in precipitation events, which are critical for hydrologic forecast application.

Reconstructing Common Era Climate Fields Using Data Assimilation, Proxies, and a Linear Climate Model

NASA Astrophysics Data System (ADS)

Perkins, W. A.; Hakim, G. J.

2016-12-01

In this work, we examine the skill of a new approach to performing climate field reconstructions (CFRs) using a form of online paleoclimate data assimilation (PDA). Many previous studies have foregone climate model forecasts during assimilation due to the computational expense of running coupled global climate models (CGCMs), and the relatively low skill of these forecasts on longer timescales. Here we greatly diminish the computational costs by employing an empirical forecast model (known as a linear inverse model; LIM), which has been shown to have comparable skill to CGCMs. CFRs of annually averaged 2m air temperature anomalies are compared between the Last Millennium Reanalysis framework (no forecasting or "offline"), a persistence forecast, and four LIM forecasting experiments over the instrumental period (1850 - 2000). We test LIM calibrations for observational (Berkeley Earth), reanalysis (20th Century Reanalysis), and CMIP5 climate model (CCSM4 and MPI) data. Generally, we find that the usage of LIM forecasts for online PDA increases reconstruction agreement with the instrumental record for both spatial and global mean temperature (GMT). The detrended GMT skill metrics show the most dramatic increases in skill with coefficient of efficiency (CE) improvements over the no-forecasting benchmark averaging 57%. LIM experiments display a common pattern of spatial field increases in CE skill over northern hemisphere land areas and in the high-latitude North Atlantic - Barents Sea corridor (Figure 1). However, the non-GCM-calibrated LIMs introduce other deficiencies into the spatial skill of these reconstructions, likely due to aspects of the LIM calibration process. Overall, the CMIP5 LIMs have the best performance when considering both spatial fields and GMT. A comparison with the persistence forecast experiment suggests that improvements are associated with the usage of the LIM forecasts, and not simple persistence of temperature anomalies over time. These results show that the use of LIM forecasting can help add further dynamical constraint to CFRs. As we move forward, this will be an important factor in fully utilizing dynamically consistent information from the proxy record while reconstructing the past millennium.

Advances of NOAA Training Program in Climate Services

NASA Astrophysics Data System (ADS)

Timofeyeva, M. M.

2012-12-01

Since 2002, NOAA's National Weather Service (NWS) Climate Services Division (CSD) has offered numerous training opportunities to NWS staff. After eight-years of development, the training program offers three instructor-led courses and roughly 25 online (distance learning) modules covering various climate topics, such as: climate data and observations, climate variability and change, and NWS national / local climate products (tools, skill, and interpretation). Leveraging climate information and expertise available at all NOAA line offices and partners allows for the delivery of the most advanced knowledge and is a very critical aspect of the training program. The emerging NOAA Climate Service (NCS) requires a well-trained, climate-literate workforce at the local level capable of delivering NOAA's climate products and services as well as providing climate-sensitive decision support. NWS Weather Forecast Offices and River Forecast Centers presently serve as local outlets for the NCS climate services. Trained NWS climate service personnel use proactive and reactive approaches and professional education methods in communicating climate variability and change information to local users. Both scientifically-sound messages and amiable communication techniques are important in developing an engaged dialog between the climate service providers and users. Several pilot projects have been conducted by the NWS CSD this past year that apply the program's training lessons and expertise to specialized external user group training. The technical user groups included natural resources managers, engineers, hydrologists, and planners for transportation infrastructure. Training of professional user groups required tailoring instructions to the potential applications for each group of users. Training technical users identified the following critical issues: (1) knowledge of target audience expectations, initial knowledge status, and potential use of climate information; (2) leveraging partnership with climate services providers; and, (3) applying 3H training approach, where the first H stands for Head (trusted science), the second H stands for Heart (make it easy), and the third H for Hand (support with applications).

Case studies of seasonal rainfall forecasts for Hong Kong and its vicinity using a regional climate model

Treesearch

David Hui; Karen Shum; Ji Chen; Shyh-Chin Chen; Jack Ritchie; John Roads

2007-01-01

Seasonal climate forecasts are one of the most promising tools for providing early warnings for natural hazards such as floods and droughts. Using two case studies, this paper documents the skill of a regional climate model in the seasonal forecasting of below normal rainfall in southern China during the rainy seasons of July–August–September 2003 and April–...

Predicting phenology by integrating ecology, evolution and climate science

USGS Publications Warehouse

Pau, Stephanie; Wolkovich, Elizabeth M.; Cook, Benjamin I.; Davies, T. Jonathan; Kraft, Nathan J.B.; Bolmgren, Kjell; Betancourt, Julio L.; Cleland, Elsa E.

2011-01-01

Forecasting how species and ecosystems will respond to climate change has been a major aim of ecology in recent years. Much of this research has focused on phenology — the timing of life-history events. Phenology has well-demonstrated links to climate, from genetic to landscape scales; yet our ability to explain and predict variation in phenology across species, habitats and time remains poor. Here, we outline how merging approaches from ecology, climate science and evolutionary biology can advance research on phenological responses to climate variability. Using insight into seasonal and interannual climate variability combined with niche theory and community phylogenetics, we develop a predictive approach for species' reponses to changing climate. Our approach predicts that species occupying higher latitudes or the early growing season should be most sensitive to climate and have the most phylogenetically conserved phenologies. We further predict that temperate species will respond to climate change by shifting in time, while tropical species will respond by shifting space, or by evolving. Although we focus here on plant phenology, our approach is broadly applicable to ecological research of plant responses to climate variability.

Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models.

PubMed

Jenouvrier, Stéphanie; Holland, Marika; Stroeve, Julienne; Barbraud, Christophe; Weimerskirch, Henri; Serreze, Mark; Caswell, Hal

2012-09-01

Sea ice conditions in the Antarctic affect the life cycle of the emperor penguin (Aptenodytes forsteri). We present a population projection for the emperor penguin population of Terre Adélie, Antarctica, by linking demographic models (stage-structured, seasonal, nonlinear, two-sex matrix population models) to sea ice forecasts from an ensemble of IPCC climate models. Based on maximum likelihood capture-mark-recapture analysis, we find that seasonal sea ice concentration anomalies (SICa ) affect adult survival and breeding success. Demographic models show that both deterministic and stochastic population growth rates are maximized at intermediate values of annual SICa , because neither the complete absence of sea ice, nor heavy and persistent sea ice, would provide satisfactory conditions for the emperor penguin. We show that under some conditions the stochastic growth rate is positively affected by the variance in SICa . We identify an ensemble of five general circulation climate models whose output closely matches the historical record of sea ice concentration in Terre Adélie. The output of this ensemble is used to produce stochastic forecasts of SICa , which in turn drive the population model. Uncertainty is included by incorporating multiple climate models and by a parametric bootstrap procedure that includes parameter uncertainty due to both model selection and estimation error. The median of these simulations predicts a decline of the Terre Adélie emperor penguin population of 81% by the year 2100. We find a 43% chance of an even greater decline, of 90% or more. The uncertainty in population projections reflects large differences among climate models in their forecasts of future sea ice conditions. One such model predicts population increases over much of the century, but overall, the ensemble of models predicts that population declines are far more likely than population increases. We conclude that climate change is a significant risk for the emperor penguin. Our analytical approach, in which demographic models are linked to IPCC climate models, is powerful and generally applicable to other species and systems. © 2012 Blackwell Publishing Ltd.

Climate change in the Brazilian northeast

NASA Astrophysics Data System (ADS)

Rodrigues, Regina R.; Haarsma, Reindert J.; Hoelzemann, Judith J.

2012-10-01

Climate Change, Impacts and Vulnerabilities in Brazil: Preparing the Brazilian Northeast for the Future; Natal, Brazil, 27 May to 01 June 2012 The variability of the semiarid climate of the Brazilian northeast has enormous environmental and social implications. Because most of the population in this area depends on subsistence agriculture, periods of severe drought in the past have caused extreme poverty and subsequent migration to urban centers. From the ecological point of view, frequent and prolonged droughts can lead to the desertification of large areas. Understanding the causes of rainfall variability, in particular periods of severe drought, is crucial for accurate forecasting, mitigation, and adaptation in this important region of Brazil.

The epistemological status of general circulation models

NASA Astrophysics Data System (ADS)

Loehle, Craig

2018-03-01

Forecasts of both likely anthropogenic effects on climate and consequent effects on nature and society are based on large, complex software tools called general circulation models (GCMs). Forecasts generated by GCMs have been used extensively in policy decisions related to climate change. However, the relation between underlying physical theories and results produced by GCMs is unclear. In the case of GCMs, many discretizations and approximations are made, and simulating Earth system processes is far from simple and currently leads to some results with unknown energy balance implications. Statistical testing of GCM forecasts for degree of agreement with data would facilitate assessment of fitness for use. If model results need to be put on an anomaly basis due to model bias, then both visual and quantitative measures of model fit depend strongly on the reference period used for normalization, making testing problematic. Epistemology is here applied to problems of statistical inference during testing, the relationship between the underlying physics and the models, the epistemic meaning of ensemble statistics, problems of spatial and temporal scale, the existence or not of an unforced null for climate fluctuations, the meaning of existing uncertainty estimates, and other issues. Rigorous reasoning entails carefully quantifying levels of uncertainty.

Adapted conservation measures are required to save the Iberian lynx in a changing climate

NASA Astrophysics Data System (ADS)

Fordham, D. A.; Akçakaya, H. R.; Brook, B. W.; Rodríguez, A.; Alves, P. C.; Civantos, E.; Triviño, M.; Watts, M. J.; Araújo, M. B.

2013-10-01

The Iberian lynx (Lynx pardinus) has suffered severe population declines in the twentieth century and is now on the brink of extinction. Climate change could further threaten the survival of the species, but its forecast effects are being neglected in recovery plans. Quantitative estimates of extinction risk under climate change have so far mostly relied on inferences from correlative projections of species' habitat shifts. Here we use ecological niche models coupled to metapopulation simulations with source-sink dynamics to directly investigate the combined effects of climate change, prey availability and management intervention on the persistence of the Iberian lynx. Our approach is unique in that it explicitly models dynamic bi-trophic species interactions in a climate change setting. We show that anticipated climate change will rapidly and severely decrease lynx abundance and probably lead to its extinction in the wild within 50 years, even with strong global efforts to mitigate greenhouse gas emissions. In stark contrast, we also show that a carefully planned reintroduction programme, accounting for the effects of climate change, prey abundance and habitat connectivity, could avert extinction of the lynx this century. Our results demonstrate, for the first time, why considering prey availability, climate change and their interaction in models is important when designing policies to prevent future biodiversity loss.

Climate change influences on marine infectious diseases: implications for management and society

USGS Publications Warehouse

Burge, Colleen A.; Eakin, C. Mark; Friedman, Carolyn S.; Froelich, Brett; Hershberger, Paul K.; Hofmann, Eileen E.; Petes, Laura E.; Prager, Katherine C.; Weil, Ernesto; Willis, Bette L.; Ford, Susan E.; Harvell, C. Drew

2014-01-01

Infectious diseases are common in marine environments, but the effects of a changing climate on marine pathogens are not well understood. Here, we focus on reviewing current knowledge about how the climate drives hostpathogen interactions and infectious disease outbreaks. Climate-related impacts on marine diseases are being documented in corals, shellfish, finfish, and humans; these impacts are less clearly linked to other organisms. Oceans and people are inextricably linked, and marine diseases can both directly and indirectly affect human health, livelihoods, and well-being. We recommend an adaptive management approach to better increase the resilience of ocean systems vulnerable to marine diseases in a changing climate. Land-based management methods of quarantining, culling, and vaccinating are not successful in the ocean; therefore, forecasting conditions that lead to outbreaks and designing tools/approaches to influence these conditions may be the best way to manage marine disease.

Bahamians and Climate Change: An Analysis of Risk Perception and Climate Change Literacy

NASA Astrophysics Data System (ADS)

Neely, R.; Owens, M. A.

2011-12-01

The Commonwealth of the Bahamas is forecasted to be adversely impacted by the effects of climate change. This presentation will present the results of an assessment of the risk perception toward climate change and climate change literacy among Bahamians. 499 Bahamians from the health care and hospitality industries participated in surveys and/or focus groups and three (3) areas of climate change literacy (attitude, behavior and knowledge) were analyzed as well as risk perception. In general, 1) Bahamians demonstrated an elementary understanding of the underlying causes of climate change, 2) possessed positive attitudes toward adopting new climate change policies, and 3) are already adjusting their behaviors in light of the current predictions. This research also resulted in the development of a model of the relationships between the climate literacy subscales (attitude, behavior and knowledge) and risk perception. This study also examined information sources and their impacts on climate change literacy. As the source of information is important in assessing the quality of the information, participants also identified the source(s) of most of their climate change information. The TV news was cited as the most common source for climate change information among Bahamians. As there is limited active research generating specific climate change information in the Bahamas, all the information Bahamians receive as it pertains to climate change is generated abroad. As a result, Bahamians must decipher through to make sense of it on an individual level. From the focus groups, many of the participants have been able to view possible changes through a cultural lens and are willing to make adjustments to maintain the uniqueness and viability of the Bahamas and to preserve it for generations. Continued study of Bahamians' climate change literacy will inform adaption and mitigation policy as well as individual action.

Forecasting the magnitude and onset of El Niño based on climate network

NASA Astrophysics Data System (ADS)

Meng, Jun; Fan, Jingfang; Ashkenazy, Yosef; Bunde, Armin; Havlin, Shlomo

2018-04-01

El Niño is probably the most influential climate phenomenon on inter-annual time scales. It affects the global climate system and is associated with natural disasters; it has serious consequences in many aspects of human life. However, the forecasting of the onset and in particular the magnitude of El Niño are still not accurate enough, at least more than half a year ahead. Here, we introduce a new forecasting index based on climate network links representing the similarity of low frequency temporal temperature anomaly variations between different sites in the Niño 3.4 region. We find that significant upward trends in our index forecast the onset of El Niño approximately 1 year ahead, and the highest peak since the end of last El Niño in our index forecasts the magnitude of the following event. We study the forecasting capability of the proposed index on several datasets, including, ERA-Interim, NCEP Reanalysis I, PCMDI-AMIP 1.1.3 and ERSST.v5.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

USGS Publications Warehouse

Dietze, Michael C.; Fox, Andrew; Beck-Johnson, Lindsay; Betancourt, Julio L.; Hooten, Mevin B.; Jarnevich, Catherine S.; Keitt, Timothy H.; Kenney, Melissa A.; Laney, Christine M.; Larsen, Laurel G.; Loescher, Henry W.; Lunch, Claire K.; Pijanowski, Bryan; Randerson, James T.; Read, Emily; Tredennick, Andrew T.; Vargas, Rodrigo; Weathers, Kathleen C.; White, Ethan P.

2018-01-01

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges.

PubMed

Dietze, Michael C; Fox, Andrew; Beck-Johnson, Lindsay M; Betancourt, Julio L; Hooten, Mevin B; Jarnevich, Catherine S; Keitt, Timothy H; Kenney, Melissa A; Laney, Christine M; Larsen, Laurel G; Loescher, Henry W; Lunch, Claire K; Pijanowski, Bryan C; Randerson, James T; Read, Emily K; Tredennick, Andrew T; Vargas, Rodrigo; Weathers, Kathleen C; White, Ethan P

2018-02-13

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Using simple chaotic models to interpret climate under climate change: Implications for probabilistic climate prediction

NASA Astrophysics Data System (ADS)

Daron, Joseph

2010-05-01

Exploring the reliability of model based projections is an important pre-cursor to evaluating their societal relevance. In order to better inform decisions concerning adaptation (and mitigation) to climate change, we must investigate whether or not our models are capable of replicating the dynamic nature of the climate system. Whilst uncertainty is inherent within climate prediction, establishing and communicating what is plausible as opposed to what is likely is the first step to ensuring that climate sensitive systems are robust to climate change. Climate prediction centers are moving towards probabilistic projections of climate change at regional and local scales (Murphy et al., 2009). It is therefore important to understand what a probabilistic forecast means for a chaotic nonlinear dynamic system that is subject to changing forcings. It is in this context that we present the results of experiments using simple models that can be considered analogous to the more complex climate system, namely the Lorenz 1963 and Lorenz 1984 models (Lorenz, 1963; Lorenz, 1984). Whilst the search for a low-dimensional climate attractor remains illusive (Fraedrich, 1986; Sahay and Sreenivasan, 1996) the characterization of the climate system in such terms can be useful for conceptual and computational simplicity. Recognising that a change in climate is manifest in a change in the distribution of a particular climate variable (Stainforth et al., 2007), we first establish the equilibrium distributions of the Lorenz systems for certain parameter settings. Allowing the parameters to vary in time, we investigate the dependency of such distributions to initial conditions and discuss the implications for climate prediction. We argue that the role of chaos and nonlinear dynamic behaviour ought to have more prominence in the discussion of the forecasting capabilities in climate prediction. References: Fraedrich, K. Estimating the dimensions of weather and climate attractors. J. Atmos. Sci, 43, 419-432, 1986. Lorenz, E. N. Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130-141, 1963. Lorenz, E. N. Irregularity: a fundamental property of the atmosphere. Tellus, 36A, 98-110, 1984. Murphy, J. M., D. M. H. Sexton, G. J. Jenkins, B. B. B. Booth, C. C. Brown, R. T. Clark, M. Collins, G. R. Harris, E. J. Kendon, R. A. Betts, S. J. Brown, P. Boorman, T. P. Howard, K. A. Humphrey, M. P. McCarthy, R. E. McDonald, A. Stephens, C. Wallace, R. Warren, R. Wilby, and R. A. Wood. Uk climate projections science report: Climate change projections. 2009. Sahay, A. and K. R. Sreenivasan. The search for a low-dimensional characterization of a local climate system. Phil. Trans. R. Soc. A., 354, 1715-1750, 1996. Stainforth, D. A., M. R. Allen, E. R. Tredger, and L. A. Smith. Confidence, uncertainty and decision-support relevance in climate predictions. Phil. Trans. R. Soc. A, 365, 2145-2161, 2007.

NEON: the first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure

Treesearch

Thomas U. Kampe; Brian R. Johnson; Michele Kuester; Michael Keller

2010-01-01

The National Ecological Observatory Network (NEON) is an ecological observation platform for discovering, understanding and forecasting the impacts of climate change, land use change, and invasive species on continental-scale ecology. NEON will operate for 30 years and gather long-term data on ecological response changes and on feedbacks with the geosphere, hydrosphere...

Predicting the Impacts of Climate Change on Central American Agriculture

NASA Astrophysics Data System (ADS)

Winter, J. M.; Ruane, A. C.; Rosenzweig, C.

2011-12-01

Agriculture is a vital component of Central America's economy. Poor crop yields and harvest reliability can produce food insecurity, malnutrition, and conflict. Regional climate models (RCMs) and agricultural models have the potential to greatly enhance the efficiency of Central American agriculture and water resources management under both current and future climates. A series of numerical experiments was conducted using Regional Climate Model Version 3 (RegCM3) and the Weather Research and Forecasting Model (WRF) to evaluate the ability of RCMs to reproduce the current climate of Central America and assess changes in temperature and precipitation under multiple future climate scenarios. Control simulations were thoroughly compared to a variety of observational datasets, including local weather station data, gridded meteorological data, and high-resolution satellite-based precipitation products. Future climate simulations were analyzed for both mean shifts in climate and changes in climate variability, including extreme events (droughts, heat waves, floods). To explore the impacts of changing climate on maize, bean, and rice yields in Central America, RCM output was used to force the Decision Support System for Agrotechnology Transfer Model (DSSAT). These results were synthesized to create climate change impacts predictions for Central American agriculture that explicitly account for evolving distributions of precipitation and temperature extremes.

Assessment of Folsom Lake Watershed response to historical and potential future climate scenarios

USGS Publications Warehouse

Carpenter, Theresa M.; Georgakakos, Konstantine P.

2000-01-01

An integrated forecast-control system was designed to allow the profitable use of ensemble forecasts for the operational management of multi-purpose reservoirs. The system ingests large-scale climate model monthly precipitation through the adjustment of the marginal distribution of reservoir-catchment precipitation to reflect occurrence of monthly climate precipitation amounts in the extreme terciles of their distribution. Generation of ensemble reservoir inflow forecasts is then accomplished with due account for atmospheric- forcing and hydrologic- model uncertainties. These ensemble forecasts are ingested by the decision component of the integrated system, which generates non- inferior trade-off surfaces and, given management preferences, estimates of reservoir- management benefits over given periods. In collaboration with the Bureau of Reclamation and the California Nevada River Forecast Center, the integrated system is applied to Folsom Lake in California to evaluate the benefits for flood control, hydroelectric energy production, and low flow augmentation. In addition to retrospective studies involving the historical period 1964-1993, system simulations were performed for the future period 2001-2030, under a control (constant future greenhouse-gas concentrations assumed at the present levels) and a greenhouse-gas- increase (1-% per annum increase assumed) scenario. The present paper presents and validates ensemble 30-day reservoir- inflow forecasts under a variety of situations. Corresponding reservoir management results are presented in Yao and Georgakakos, A., this issue. Principle conclusions of this paper are that the integrated system provides reliable ensemble inflow volume forecasts at the 5-% confidence level for the majority of the deciles of forecast frequency, and that the use of climate model simulations is beneficial mainly during high flow periods. It is also found that, for future periods with potential sharp climatic increases of precipitation amount and to maintain good reliability levels, operational ensemble inflow forecasting should involve atmospheric forcing from appropriate climatic periods.

Drought forecasting in Luanhe River basin involving climatic indices

NASA Astrophysics Data System (ADS)

Ren, Weinan; Wang, Yixuan; Li, Jianzhu; Feng, Ping; Smith, Ronald J.

2017-11-01

Drought is regarded as one of the most severe natural disasters globally. This is especially the case in Tianjin City, Northern China, where drought can affect economic development and people's livelihoods. Drought forecasting, the basis of drought management, is an important mitigation strategy. In this paper, we evolve a probabilistic forecasting model, which forecasts transition probabilities from a current Standardized Precipitation Index (SPI) value to a future SPI class, based on conditional distribution of multivariate normal distribution to involve two large-scale climatic indices at the same time, and apply the forecasting model to 26 rain gauges in the Luanhe River basin in North China. The establishment of the model and the derivation of the SPI are based on the hypothesis of aggregated monthly precipitation that is normally distributed. Pearson correlation and Shapiro-Wilk normality tests are used to select appropriate SPI time scale and large-scale climatic indices. Findings indicated that longer-term aggregated monthly precipitation, in general, was more likely to be considered normally distributed and forecasting models should be applied to each gauge, respectively, rather than to the whole basin. Taking Liying Gauge as an example, we illustrate the impact of the SPI time scale and lead time on transition probabilities. Then, the controlled climatic indices of every gauge are selected by Pearson correlation test and the multivariate normality of SPI, corresponding climatic indices for current month and SPI 1, 2, and 3 months later are demonstrated using Shapiro-Wilk normality test. Subsequently, we illustrate the impact of large-scale oceanic-atmospheric circulation patterns on transition probabilities. Finally, we use a score method to evaluate and compare the performance of the three forecasting models and compare them with two traditional models which forecast transition probabilities from a current to a future SPI class. The results show that the three proposed models outperform the two traditional models and involving large-scale climatic indices can improve the forecasting accuracy.

A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments

NASA Astrophysics Data System (ADS)

Schepen, Andrew; Zhao, Tongtiegang; Wang, Quan J.; Robertson, David E.

2018-03-01

Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S), which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S post-processed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications.

Is "the perfect model" really needed? - Analysis of the quality level of climate information necessary for supporting adaptation in agriculture and forestry

NASA Astrophysics Data System (ADS)

Gálos, Borbála; Ostler, Wolf-Uwe; Csáki, Péter; Bidló, András; Panferov, Oleg

2016-04-01

Recent results of climate science (e.g. IPCC AR5, 2013) and statements of climate policy (e.g. Paris Agreement) confirm that climate change is an ongoing issue. The consequences will be noticeable for a long time even if the 2 Degree goal is reached. Therefore, action plans are necessary for adaptation and mitigation on national and international level. Forestry and agriculture are especially threatened by the probable increase of the frequency and/or intensity of climate extremes. Severe impacts of recurrent droughts/heat waves that were observed in the last decades in the sensitive and vulnerable ecosystems and regions are very likely to occur with increasing probability throughout the 21st century. For the adequate climate impact assessments, for adaptation strategies as well as for supporting decisions in the above mentioned sectors the reliable information on the long-term climate tendencies and on ecosystem responses are required. Here are the two major problems: on the one hand the information on current climate and future climate developments are highly uncertain. On the other hand, due to limited knowledge on ecosystem responses, it is difficult to define how certain or accurate the provided climate data should be for the plausible application in agricultural/forestry research and practice. Considering agriculture and forestry, our research is focusing on the following questions: • What is the climate information demand of practice and impact research in the two sectors? • What quality level of climate information is necessary for adaptation support? • How does the accuracy of climate input affect the results of the climate impact assessments? The agriculture and forestry operate at two very different time scales and have a different reaction times and adaptation capacities. Agriculture requires short-term information on current conditions and short-/medium-term weather forecast. To assess the degree of information accuracy required by practical agriculture a questionnaire has been carried out among 180 farms of different sizes and specializations (mostly arable farming and viniculture) in Reinland-Palatine, Germany. The results show that almost all farmers use the weather information daily and are in need of weather forecast. More than a half requires also the forecast on extreme events. However the farmers require more qualitative (e.g. temperature coarser than 1°C) than high-precision quantitative information in short and medium-term forecasts. Forestry requires long-term (30-100 years) climate projections. For the assessment of climate change impacts on forest distribution, production and tree species selection, monthly temperature means and precipitation sums are sufficient. Based on the results of regional climate models it will be shown how the bias, the spread and spatial resolution of the simulation results are affecting the accuracy of impact assessments. Our analyses can help to fill the gap between climate services and the needs of impact researchers and end users in agriculture and forestry. User-relevant climate information can contribute to appropriate adaptation support services and management options in the two sectors. Keywords: regional climate projections, climate impact assessment, agriculture, forestry, adaptation support, accuracy of climate information Funding: The research is supported by the "Agroclimate-2" (VKSZ_12-1-2013-0034) joint EU-national research project.

Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R.

2009-04-01

Regional climate models (RCMs) have long been used to downscale global climate simulations. In contrast the ability of RCMs to downscale seasonal climate forecasts has received little attention. The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Does dynamical downscaling using RCMs provide additional useful information for seasonal forecasts made by global models? MRED is using a suite of RCMs to downscale seasonal forecasts produced by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus is on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the usefulness of higher resolution for near-surface fields influenced by high resolution orography. Each RCM covers the conterminous U.S. at approximately 32 km resolution, comparable to the scale of the North American Regional Reanalysis (NARR) which will be used to evaluate the models. The forecast ensemble for each RCM is comprised of 15 members over a period of 22+ years (from 1982 to 2003+) for the forecast period 1 December - 30 April. Each RCM will create a 15-member lagged ensemble by starting on different dates in the preceding November. This results in a 120-member ensemble for each projection (8 RCMs by 15 members per RCM). The RCMs will be continually updated at their lateral boundaries using 6-hourly output from CFS or GEOS5. Hydrometeorological output will be produced in a standard netCDF-based format for a common analysis grid, which simplifies both model intercomparison and the generation of ensembles. MRED will compare individual RCM and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs). Metrics of ensemble spread will also be evaluated. Extensive process-oriented analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will define a strategy for more skillful and useful regional seasonal climate forecasts.

Climate changes impact the surface albedo of a forest ecosystem based on MODIS satellite data

NASA Astrophysics Data System (ADS)

Zoran, M. A.; Nemuc, A. V.

2007-10-01

Surface albedo is one of the most important biophysical parameter responsible for energy balance control and the surface temperature and boundary-layer structure of the atmosphere. Forest land surface albedo is also highly variable temporally showing both diurnal as well as seasonal variations. In forest systems, albedo controls the microclimate conditions which affects ecosystem physical, physiological, and biogeochemical processes such as energy balance, evapotranspiration, photosynthesis. Due to anthropogenic and natural factors, land cover and land use changes result is the land surfaces albedo change. The main aim of this paper is to investigate the albedo patterns due to the impact of atmospheric pollution and climate variations of a forest ecosystem Branesti-Cernica, placed to the North-East of Bucharest city, Romania based on satellite Landsat ETM+, IKONOS and MODIS data and climate station observations. Our study focuses on 3 years of data (2003-2005), each of which had a different climatic regime. As the physical climate system is very sensitive to surface albedo, forest ecosystems could significantly feedback to the projected climate change modeling scenarios through albedo changes. The results of this research have a number of applications in weather forecasting, climate change, and forest ecosystem studies.

Update on the NASA GEOS-5 Aerosol Forecasting and Data Assimilation System

NASA Technical Reports Server (NTRS)

Colarco, Peter; da Silva, Arlindo; Aquila, Valentina; Bian, Huisheng; Buchard, Virginie; Castellanos, Patricia; Darmenov, Anton; Follette-Cook, Melanie; Govindaraju, Ravi; Keller, Christoph;

Climate change and the ecology and evolution of Arctic vertebrates.

PubMed

Gilg, Olivier; Kovacs, Kit M; Aars, Jon; Fort, Jérôme; Gauthier, Gilles; Grémillet, David; Ims, Rolf A; Meltofte, Hans; Moreau, Jérôme; Post, Eric; Schmidt, Niels Martin; Yannic, Glenn; Bollache, Loïc

2012-02-01

Climate change is taking place more rapidly and severely in the Arctic than anywhere on the globe, exposing Arctic vertebrates to a host of impacts. Changes in the cryosphere dominate the physical changes that already affect these animals, but increasing air temperatures, changes in precipitation, and ocean acidification will also affect Arctic ecosystems in the future. Adaptation via natural selection is problematic in such a rapidly changing environment. Adjustment via phenotypic plasticity is therefore likely to dominate Arctic vertebrate responses in the short term, and many such adjustments have already been documented. Changes in phenology and range will occur for most species but will only partly mitigate climate change impacts, which are particularly difficult to forecast due to the many interactions within and between trophic levels. Even though Arctic species richness is increasing via immigration from the South, many Arctic vertebrates are expected to become increasingly threatened during this century. © 2012 New York Academy of Sciences.

Experimental droughts with rainout shelters: A methodological review

USDA-ARS?s Scientific Manuscript database

Forecast increases in the frequency, intensity and duration of droughts with climate change may have extreme and extensive ecological consequences. There are currently hundreds of published, ongoing and new drought experiments worldwide aimed to assess ecosystem sensitivities to drought and identify...

USING ECO-EVOLUTIONARY INDIVIDUAL-BASED MODELS TO INVESTIGATE SPATIALLY-DEPENDENT PROCESSES IN CONSERVATION GENETICS

EPA Science Inventory

Eco-evolutionary population simulation models are powerful new forecasting tools for exploring management strategies for climate change and other dynamic disturbance regimes. Additionally, eco-evo individual-based models (IBMs) are useful for investigating theoretical feedbacks ...

An integrated modeling framework for real-time irrigation scheduling: the benefit of spectroscopy and weather forecasts

NASA Astrophysics Data System (ADS)

Brook, Anna; Polinova, Maria; Housh, Mashor

2016-04-01

Agriculture and agricultural landscapes are increasingly under pressure to meet the demands of a constantly increasing human population and globally changing food patterns. At the same time, there is rising concern that climate change and food security will harm agriculture in many regions of the world (Nelson et al., 2009). Facing those treats, majority of Mediterranean countries had chosen irrigated agriculture. For crop plants water is one of the most important inputs, as it is responsible for crop growth, production and it ensures the efficiency of other inputs (e.g. seeds, fertilizers and pesticide) but its use is in competition with other local sectors (e.g. industry, urban human use). Thus, well-timed availability of water is vital to agriculture for ensured yields. The increasing demand for irrigation has necessitated the need for optimal irrigation scheduling techniques that coordinate the timing and amount of irrigation to optimally manage the water use in agriculture systems. The irrigation scheduling problem can be challenging as farmers try to deal with different conflicting objectives of maximizing their yield while minimizing irrigation water use. Another challenge in the irrigation scheduling problem is attributed to the uncertain factors involved in the plant growth process during the growing season. Most notable, the climatic factors such as evapotranspiration and rainfall, these uncertain factors add a third objective to the farmer perspective, namely, minimizing the risk associated with these uncertain factors. Nevertheless, advancements in weather forecasting reduced the uncertainty level associated with future climatic data. Thus, climatic forecasts can be reliably employed to guide optimal irrigation schedule scheme when coupled with stochastic optimization models (Housh et al., 2012). Many studies have concluded that optimal irrigation decisions can provide substantial economic value over conventional irrigation decisions (Wang and Cai 2009). These studies have only incorporated short-term (weekly) forecasts, missing the potential benefit of the mid-term (seasonal) climate forecasts The latest progress in new data acquisition technologies (mainly in the field of Earth observation by remote sensing and imaging spectroscopy systems) as well as the state-of-the-art achievements in the fields of geographical information systems (GIS), computer science and climate and climate impact modelling enable to develop both integrated modelling and realistic spatial simulations. The present method is the use of field spectroscopy technology to keep constant monitoring of the field. The majority of previously developed decision support systems use satellite remote sensing data that provide very limited capabilities (conventional and basic parameters). The alternative is to use a more progressive technology of hyperspectral airborne or ground-based imagery data that provide an exhaustive description of the field. Nevertheless, this alternative is known to be very costly and complex. As such, we will present a low-cost imaging spectroscopy technology supported by detailed and fine-resolution field spectroscopy as a cost effective option for near field real-time monitoring tool. In order to solve the soil water balance and to predict the water irrigation volume a pedological survey is realized in the evaluation study areas.The remote sensing and field spectroscopy were applied to integrate continuous feedbacks from the field (e.g. soil moisture, organic/inorganic carbon, nitrogen, salinity, fertilizers, sulphur acid, texture; crop water-stress, plant stage, LAI , chlorophyll, biomass, yield prediction applying PROSPECT+SILT ; Fraction of Absorbed Photosynthetically Active Radiation FAPAR) estimated based on remote sensing information to minimize the errors associated with crop simulation process. A stochastic optimization model will be formulated that take into account both mid-term seasonal probabilistic climate prediction and short-term weekly forecasts. In order to optimize the water resource use, the irrigation scheduling will be defined by use a simulation model of soil-plant and atmosphere system (e.g. SWAP model, Van Dam et al., 2008). The use of this tool is necessary to: i) take into account the soil spatial variability; ii) to predict the system behaviour under the forecasted climate; iii) define the optimized irrigation water volumes. Given this knowledge in the three domains of optimization under uncertainty, spectroscopy/remote sensing and climate forecasting, we will be presented as an integrated framework for deriving optimal irrigation decisions. References Nelson, Gerald C., et al. Climate change: Impact on agriculture and costs of adaptation. Vol. 21. Intl Food Policy Res Inst, 2009. Housh, Mashor, Avi Ostfeld, and Uri Shamir. "Seasonal multi-year optimal management of quantities and salinities in regional water supply systems." Environmental Modelling & Software 37 (2012): 55-67. Wang, Dingbao, and Ximing Cai. "Irrigation scheduling - Role of weather forecasting and farmers' behavior." Journal of Water Resources Planning and Management 135.5 (2009): 364-372. Van Dam, J. C., et al. SWAP version 3.2: Theory description and user manual. No. 1649. Wageningen, The Netherlands: Alterra, 2008.

The predictability of Iowa's hydroclimate through analog forecasts

NASA Astrophysics Data System (ADS)

Rowe, Scott Thomas

Iowa has long been affected by periods characterized by extreme drought and flood. In 2008, Cedar Rapids, Iowa was devastated by a record flood with damages around 3 billion. Several years later, Iowa was affected by severe drought in 2012, causing upwards of 30 billion in damages and losses across the United States. These climatic regimes can quickly transition from one regime to another, as was observed in the June 2013 major floods to the late summer 2013 severe drought across eastern Iowa. Though it is not possible to prevent a natural disaster from occurring, we explore how predictable these events are by using forecast models and analogs. Iowa's climate records are analyzed from 1950 to 2012 to determine if there are specific surface and upper-air pressure patterns linked to climate regimes (i.e., cold/hot and dry/wet conditions for a given month). We found that opposing climate regimes in Iowa have reversed anomalies in certain geographical regions of the northern hemisphere. These defined patterns and waves suggested to us that it could be possible to forecast extreme temperature and precipitation periods over Iowa if given a skillful forecast system. We examined the CMC, COLA, and GFDL models within the National Multi-Model Ensemble suite to create analog forecasts based on either surface or upper-air pressure forecasts. The verification results show that some analogs have predictability skill at the 0.5-month lead time exceeding random chance, but our overall confidence in the analog forecasts is not high enough to allow us to issue statewide categorical temperature and precipitation climate forecasts.

Probability for Weather and Climate

NASA Astrophysics Data System (ADS)

Smith, L. A.

2013-12-01

Over the last 60 years, the availability of large-scale electronic computers has stimulated rapid and significant advances both in meteorology and in our understanding of the Earth System as a whole. The speed of these advances was due, in large part, to the sudden ability to explore nonlinear systems of equations. The computer allows the meteorologist to carry a physical argument to its conclusion; the time scales of weather phenomena then allow the refinement of physical theory, numerical approximation or both in light of new observations. Prior to this extension, as Charney noted, the practicing meteorologist could ignore the results of theory with good conscience. Today, neither the practicing meteorologist nor the practicing climatologist can do so, but to what extent, and in what contexts, should they place the insights of theory above quantitative simulation? And in what circumstances can one confidently estimate the probability of events in the world from model-based simulations? Despite solid advances of theory and insight made possible by the computer, the fidelity of our models of climate differs in kind from the fidelity of models of weather. While all prediction is extrapolation in time, weather resembles interpolation in state space, while climate change is fundamentally an extrapolation. The trichotomy of simulation, observation and theory which has proven essential in meteorology will remain incomplete in climate science. Operationally, the roles of probability, indeed the kinds of probability one has access too, are different in operational weather forecasting and climate services. Significant barriers to forming probability forecasts (which can be used rationally as probabilities) are identified. Monte Carlo ensembles can explore sensitivity, diversity, and (sometimes) the likely impact of measurement uncertainty and structural model error. The aims of different ensemble strategies, and fundamental differences in ensemble design to support of decision making versus advance science, are noted. It is argued that, just as no point forecast is complete without an estimate of its accuracy, no model-based probability forecast is complete without an estimate of its own irrelevance. The same nonlinearities that made the electronic computer so valuable links the selection and assimilation of observations, the formation of ensembles, the evolution of models, the casting of model simulations back into observables, and the presentation of this information to those who use it to take action or to advance science. Timescales of interest exceed the lifetime of a climate model and the career of a climate scientist, disarming the trichotomy that lead to swift advances in weather forecasting. Providing credible, informative climate services is a more difficult task. In this context, the value of comparing the forecasts of simulation models not only with each other but also with the performance of simple empirical models, whenever possible, is stressed. The credibility of meteorology is based on its ability to forecast and explain the weather. The credibility of climatology will always be based on flimsier stuff. Solid insights of climate science may be obscured if the severe limits on our ability to see the details of the future even probabilistically are not communicated clearly.

Assessing Variability and Errors in Historical Runoff Forecasting with Physical Models and Alternative Data Sources

NASA Astrophysics Data System (ADS)

Penn, C. A.; Clow, D. W.; Sexstone, G. A.

2017-12-01

Water supply forecasts are an important tool for water resource managers in areas where surface water is relied on for irrigating agricultural lands and for municipal water supplies. Forecast errors, which correspond to inaccurate predictions of total surface water volume, can lead to mis-allocated water and productivity loss, thus costing stakeholders millions of dollars. The objective of this investigation is to provide water resource managers with an improved understanding of factors contributing to forecast error, and to help increase the accuracy of future forecasts. In many watersheds of the western United States, snowmelt contributes 50-75% of annual surface water flow and controls both the timing and volume of peak flow. Water supply forecasts from the Natural Resources Conservation Service (NRCS), National Weather Service, and similar cooperators use precipitation and snowpack measurements to provide water resource managers with an estimate of seasonal runoff volume. The accuracy of these forecasts can be limited by available snowpack and meteorological data. In the headwaters of the Rio Grande, NRCS produces January through June monthly Water Supply Outlook Reports. This study evaluates the accuracy of these forecasts since 1990, and examines what factors may contribute to forecast error. The Rio Grande headwaters has experienced recent changes in land cover from bark beetle infestation and a large wildfire, which can affect hydrological processes within the watershed. To investigate trends and possible contributing factors in forecast error, a semi-distributed hydrological model was calibrated and run to simulate daily streamflow for the period 1990-2015. Annual and seasonal watershed and sub-watershed water balance properties were compared with seasonal water supply forecasts. Gridded meteorological datasets were used to assess changes in the timing and volume of spring precipitation events that may contribute to forecast error. Additionally, a spatially-distributed physics-based snow model was used to assess possible effects of land cover change on snowpack properties. Trends in forecasted error are variable while baseline model results show a consistent under-prediction in the recent decade, highlighting possible compounding effects of climate and land cover changes.

Short-term improvements in public health from global-climate policies on fossil-fuel combustion: an interim report. Working Group on Public Health and Fossil-Fuel Combustion.

PubMed

1997-11-08

Most public-health assessments of climate-control policies have focused on long-term impacts of global change. Our interdisciplinary working group assesses likely short-term impacts on public health. We combined models of energy consumption, carbon emissions, and associated atmospheric particulate-matter (PM) concentration under two different forecasts: business-as-usual (BAU); and a hypothetical climate-policy scenario, where developed and developing countries undertake significant reductions in carbon emissions. We predict that by 2020, 700,000 avoidable deaths (90% CI 385,000-1,034,000) will occur annually as a result of additional PM exposure under the BAU forecasts when compared with the climate-policy scenario. From 2000 to 2020, the cumulative impact on public health related to the difference in PM exposure could total 8 million deaths globally (90% CI 4.4-11.9 million). In the USA alone, the avoidable number of annual deaths from PM exposure in 2020 (without climate-change-control policy) would equal in magnitude deaths associated with human immunodeficiency diseases or all liver diseases in 1995. The mortality estimates are indicative of the magnitude of the likely health benefits of the climate-policy scenario examined and are not precise predictions of avoidable deaths. While characterized by considerable uncertainty, the short-term public-health impacts of reduced PM exposures associated with greenhouse-gas reductions are likely to be substantial even under the most conservative set of assumptions.

Global assessment of experimental climate warming on tundra vegetation: heterogeneity over space and time.

PubMed

Elmendorf, Sarah C; Henry, Gregory H R; Hollister, Robert D; Björk, Robert G; Bjorkman, Anne D; Callaghan, Terry V; Collier, Laura Siegwart; Cooper, Elisabeth J; Cornelissen, Johannes H C; Day, Thomas A; Fosaa, Anna Maria; Gould, William A; Grétarsdóttir, Járngerður; Harte, John; Hermanutz, Luise; Hik, David S; Hofgaard, Annika; Jarrad, Frith; Jónsdóttir, Ingibjörg Svala; Keuper, Frida; Klanderud, Kari; Klein, Julia A; Koh, Saewan; Kudo, Gaku; Lang, Simone I; Loewen, Val; May, Jeremy L; Mercado, Joel; Michelsen, Anders; Molau, Ulf; Myers-Smith, Isla H; Oberbauer, Steven F; Pieper, Sara; Post, Eric; Rixen, Christian; Robinson, Clare H; Schmidt, Niels Martin; Shaver, Gaius R; Stenström, Anna; Tolvanen, Anne; Totland, Orjan; Troxler, Tiffany; Wahren, Carl-Henrik; Webber, Patrick J; Welker, Jeffery M; Wookey, Philip A

2012-02-01

Understanding the sensitivity of tundra vegetation to climate warming is critical to forecasting future biodiversity and vegetation feedbacks to climate. In situ warming experiments accelerate climate change on a small scale to forecast responses of local plant communities. Limitations of this approach include the apparent site-specificity of results and uncertainty about the power of short-term studies to anticipate longer term change. We address these issues with a synthesis of 61 experimental warming studies, of up to 20 years duration, in tundra sites worldwide. The response of plant groups to warming often differed with ambient summer temperature, soil moisture and experimental duration. Shrubs increased with warming only where ambient temperature was high, whereas graminoids increased primarily in the coldest study sites. Linear increases in effect size over time were frequently observed. There was little indication of saturating or accelerating effects, as would be predicted if negative or positive vegetation feedbacks were common. These results indicate that tundra vegetation exhibits strong regional variation in response to warming, and that in vulnerable regions, cumulative effects of long-term warming on tundra vegetation - and associated ecosystem consequences - have the potential to be much greater than we have observed to date. © 2011 Blackwell Publishing Ltd/CNRS.

Constraints of Predicting Land Cover Changes from Bioclimatic Models - with Special Regard to Forest Cover

NASA Astrophysics Data System (ADS)

Matyas, C.; Rasztovits, E.

2009-04-01

The determination of "climatic envelopes" of biota and especially of forests has attained a sudden actuality in the context of expected climatic changes, as zonal vegetation types serve as convenient climate indicators. Studies on bioclimatic modelling and on climate change-triggered vegetation shifts are abundant and have been considered also in the fourth report of IPCC. Present and predicted distribution of forest biota provide an illustrative impression of shift of potential land cover changes. There are, however, certain assumptions which remain often unmentioned, and which - if left unconsidered - may compromise the outcome. The bioclimatic models of actual biome or species distributions may be biased, because: (1) Present "natural" vegetation cover types are in most part of the world under strong human influence. In Europe, even the few remaining close to natural landscapes are the results of long lasting human interference of the past which continue also in the present. (2) It is a well known ecological rule that actual ranges of species and biota are regulated by complex, often hidden interactions which may modify distributions. Physiologically (more accurately: genetically) set potential limits may be per definitionem wider than the realized, actual ones. To include extrazonal outliers in bioclimatic models may cause errors. (3) The longevity and persistence of forest trees may be deceptive for climatic modelling at the retreating, xeric limits. The climatic zones move usually faster than the land (forest) cover indicating those zones. (4) Climate envelopes use standard (mean) climate parameters. It is however the effect of the sequence of consecutive extreme weather events and linked biotic damages which will concretely decide over survival or mortality. Therefore the use of climate means should be regarded only as surrogates for weather extremes. (5) The change of climatic environment may alter the phenologic behaviour which cannot be tested in advance. This affects also consuming and pathogenic organisms. Forecasts are unreliable, especially because up to date negligible or unknown pests and diseases may become virulent. Environmental shifts may also lead to changing interactions between hosts and consumers. The described and other factors may lead to overestimate progress at the front, and to possibly too pessimistic forecasts at the retreating (xeric) end of distributions.

Uncertainty in forecasts of long-run economic growth.

PubMed

Christensen, P; Gillingham, K; Nordhaus, W

2018-05-22

Forecasts of long-run economic growth are critical inputs into policy decisions being made today on the economy and the environment. Despite its importance, there is a sparse literature on long-run forecasts of economic growth and the uncertainty in such forecasts. This study presents comprehensive probabilistic long-run projections of global and regional per-capita economic growth rates, comparing estimates from an expert survey and a low-frequency econometric approach. Our primary results suggest a median 2010-2100 global growth rate in per-capita gross domestic product of 2.1% per year, with a standard deviation (SD) of 1.1 percentage points, indicating substantially higher uncertainty than is implied in existing forecasts. The larger range of growth rates implies a greater likelihood of extreme climate change outcomes than is currently assumed and has important implications for social insurance programs in the United States.

Constraints and Suggestions in Adopting Seasonal Climate Forecasts by Farmers in South India

ERIC Educational Resources Information Center

Shankar, K. Ravi; Nagasree, K.; Venkateswarlu, B.; Maraty, Pochaiah

2011-01-01

The main objective of this study was to determine constraints and suggestions of farmers towards adopting seasonal climate forecasts. It addresses the question: Which forms of providing forecasts will be helpful to farmers in agricultural decision making? For the study, farmers were selected from Andhra Pradesh state of South India. One hundred…

Climate change can alter predator-prey dynamics and population viability of prey.

PubMed

Bastille-Rousseau, Guillaume; Schaefer, James A; Peers, Michael J L; Ellington, E Hance; Mumma, Matthew A; Rayl, Nathaniel D; Mahoney, Shane P; Murray, Dennis L

2018-01-01

For many organisms, climate change can directly drive population declines, but it is less clear how such variation may influence populations indirectly through modified biotic interactions. For instance, how will climate change alter complex, multi-species relationships that are modulated by climatic variation and that underlie ecosystem-level processes? Caribou (Rangifer tarandus), a keystone species in Newfoundland, Canada, provides a useful model for unravelling potential and complex long-term implications of climate change on biotic interactions and population change. We measured cause-specific caribou calf predation (1990-2013) in Newfoundland relative to seasonal weather patterns. We show that black bear (Ursus americanus) predation is facilitated by time-lagged higher summer growing degree days, whereas coyote (Canis latrans) predation increases with current precipitation and winter temperature. Based on future climate forecasts for the region, we illustrate that, through time, coyote predation on caribou calves could become increasingly important, whereas the influence of black bear would remain unchanged. From these predictions, demographic projections for caribou suggest long-term population limitation specifically through indirect effects of climate change on calf predation rates by coyotes. While our work assumes limited impact of climate change on other processes, it illustrates the range of impact that climate change can have on predator-prey interactions. We conclude that future efforts to predict potential effects of climate change on populations and ecosystems should include assessment of both direct and indirect effects, including climate-predator interactions.

Global Climate Teleconnections to Forecast Increased Risk of Vector-Borne Animal and Human Disease Transmission

USDA-ARS?s Scientific Manuscript database

We willexamine how climate teleconnect ions and variability impact vector biology and vector borne disease ecology, and demonstrate that global climate monitoring can be used to anticipate and forecast epidemics and epizootics. In this context we willexamine significant worldwide weather anomalies t...

Slow Response or No Response? Distinguishing Non-Climatic Range Limits from Demographic Inertia

NASA Astrophysics Data System (ADS)

Hillerislambers, J.; Anderegg, L. D. L.; Breckheimer, I.; Ford, K.; Kroiss, S.

2016-12-01

One of the greatest challenges ecologists face is forecasting how species distributions will respond to climate change. In general, species distributions have moved polewards and upslope with recent climate change (i.e. range shifts), supporting the assumption that range limits are climatically determined. However, studies also document a surprising number of species whose distributions have remained unchanged in the last 50-100 years, despite significant warming during that time period. This apparent lack of response to warming can arise for species whose range limits are determined by factors other than climate (e.g. species interactions) OR for long-lived, slow-growing, and/or dispersal-limited species whose range shifts are unable to keep pace with rapid climate change. Unfortunately, while these two possibilities are often difficult to distinguish, they have very different implications for the long-term viability of the species in question. Here, we use extensive demographic data for long-lived and slow-growing conifers collected across a large climatic gradient at Mount Rainier (WA, USA) to explore A) evidence for climatically determined range limits and B) the rate at which altitudinal distributions could shift in response to climate change in the region. In doing so, we highlight some of the complications we face in identifying whether species will be sensitive or resilient to climate change.

Adaptation to Impacts of Climate Change on Aeroallergens and Allergic Respiratory Diseases

PubMed Central

Beggs, Paul J.

2010-01-01

Climate change has the potential to have many significant impacts on aeroallergens such as pollen and mould spores, and therefore related diseases such as asthma and allergic rhinitis. This paper critically reviews this topic, with a focus on the potential adaptation measures that have been identified to date. These are aeroallergen monitoring; aeroallergen forecasting; allergenic plant management; planting practices and policies; urban/settlement planning; building design and heating, ventilating, and air-conditioning (HVAC); access to health care and medications; education; and research. PMID:20948943

Climate and Salmon Restoration in the Columbia River Basin: The Role and Usability of Seasonal Forecasts.

NASA Astrophysics Data System (ADS)

Pulwarty, Roger S.; Redmond, Kelly T.

1997-03-01

The Pacific Northwest is dependent on the vast and complex Columbia River system for power production, irrigation, navigation, flood control, recreation, municipal and industrial water supplies, and fish and wildlife habitat. In recent years Pacific salmon populations in this region, a highly valued cultural and economic resource, have declined precipitously. Since 1980, regional entities have embarked on the largest effort at ecosystem management undertaken to date in the United States, primarily aimed at balancing hydropower demands with salmon restoration activities. It has become increasingly clear that climatically driven fluctuations in the freshwater and marine environments occupied by these fish are an important influence on population variability. It is also clear that there are significant prospects of climate predictability that may prove advantageous in managing the water resources shared by the long cast of regional interests. The main thrusts of this study are 1) to describe the climate and management environments of the Columbia River basin, 2) to assess the present degree of use and benefits of available climate information, 3) to identify new roles and applications made possible by recent advances in climate forecasting, and 4) to understand, from the point of view of present and potential users in specific contexts of salmon management, what information might be needed, for what uses, and when, where, and how it should be provided. Interviews were carried out with 32 individuals in 19 organizations involved in salmon management decisions. Primary needs were in forecasting runoff volume and timing, river transit times, and stream temperatures, as much as a year or more in advance. Most respondents desired an accuracy of 75% for a seasonal forecast. Despite the significant influence of precipitation and its subsequent hydrologic impacts on the regional economy, no specific use of the present climate forecasts was uncovered. Understanding the limitations to information use forms a major component of this study. The complexity of the management environment, the lack of well-defined linkages among potential users and forecasters, and the lack of supplementary background information relating to the forecasts pose substantial barriers to future use of forecasts. Recommendations to address these problems are offered. The use of climate information and forecasts to reduce the uncertainty inherent in managing large systems for diverse needs bears significant promise.

Climate-mediated dance of the plankton

NASA Astrophysics Data System (ADS)

Behrenfeld, Michael J.

2014-10-01

Climate change will unquestionably influence global ocean plankton because it directly impacts both the availability of growth-limiting resources and the ecological processes governing biomass distributions and annual cycles. Forecasting this change demands recognition of the vital, yet counterintuitive, attributes of the plankton world. The biomass of photosynthetic phytoplankton, for example, is not proportional to their division rate. Perhaps more surprising, physical processes (such as deep vertical mixing) can actually trigger an accumulation in phytoplankton while simultaneously decreasing their division rates. These behaviours emerge because changes in phytoplankton division rates are paralleled by proportional changes in grazing, viral attack and other loss rates. Here I discuss this trophic dance between predators and prey, how it dictates when phytoplankton biomass remains constant or achieves massive blooms, and how it can determine even the sign of change in ocean ecosystems under a warming climate.

Farmers' perceptions of and adaptation strategies to climate change and their determinants; the case of Punjab province, Pakistan

NASA Astrophysics Data System (ADS)

Abid, M.; Scheffran, J.; Schneider, U. A.; Ashfaq, M.

2014-10-01

Climate change is a global environmental threat to all economic sectors, particularly the agricultural sector. Pakistan is one of the negatively affected countries from climate change due to its high exposure to extreme events and low adaptive capacity. In Pakistan, farmers are the primary stakeholders in agriculture and are more at risk due to climate vulnerability. Based on farm household data of 450 households collected from three districts in three agro-ecological zones in Punjab province of Pakistan, this study examined how farmers perceive climate change and how they adapt their farming in response to perceived changes in climate. The results demonstrate that awareness to climate change persists in the area, and farm households make adjustments to adapt their agriculture in response to climatic change. Overall 58% of the farm households adapted their farming to climate change. Changing crop varieties, changing planting dates, plantation of trees and changing fertilizer were the main adaptation methods implemented by farm households in the study area. Results from the binary logistic model revealed that education, farm experience, household size, land area, tenancy status, ownership of tube-well, access to market information, information on weather forecasting and extension all influence the farmers' choice of adaptation measures. Results also indicate that adaptation to climate change is constrained by several factors such as lack of information; lack of money; resource constraint and shortage of irrigation water in the study area. Findings of the study suggest the need of greater investment in farmer education and improved institutional setup for climate change adaptation to improve farmers' wellbeing.

Calibration and combination of monthly near-surface temperature and precipitation predictions over Europe

NASA Astrophysics Data System (ADS)

Rodrigues, Luis R. L.; Doblas-Reyes, Francisco J.; Coelho, Caio A. S.

2018-02-01

A Bayesian method known as the Forecast Assimilation (FA) was used to calibrate and combine monthly near-surface temperature and precipitation outputs from seasonal dynamical forecast systems. The simple multimodel (SMM), a method that combines predictions with equal weights, was used as a benchmark. This research focuses on Europe and adjacent regions for predictions initialized in May and November, covering the boreal summer and winter months. The forecast quality of the FA and SMM as well as the single seasonal dynamical forecast systems was assessed using deterministic and probabilistic measures. A non-parametric bootstrap method was used to account for the sampling uncertainty of the forecast quality measures. We show that the FA performs as well as or better than the SMM in regions where the dynamical forecast systems were able to represent the main modes of climate covariability. An illustration with the near-surface temperature over North Atlantic, the Mediterranean Sea and Middle-East in summer months associated with the well predicted first mode of climate covariability is offered. However, the main modes of climate covariability are not well represented in most situations discussed in this study as the seasonal dynamical forecast systems have limited skill when predicting the European climate. In these situations, the SMM performs better more often.

Toward Seasonal Forecasting of Global Droughts: Evaluation over USA and Africa

NASA Astrophysics Data System (ADS)

Wood, Eric; Yuan, Xing; Roundy, Joshua; Sheffield, Justin; Pan, Ming

2013-04-01

Extreme hydrologic events in the form of droughts are significant sources of social and economic damage. In the United States according to the National Climatic Data Center, the losses from drought exceed US210 billion during 1980-2011, and account for about 24% of all losses from major weather disasters. Internationally, especially for the developing world, drought has had devastating impacts on local populations through food insecurity and famine. Providing reliable drought forecasts with sufficient early warning will help the governments to move from the management of drought crises to the management of drought risk. After working on drought monitoring and forecasting over the USA for over 10 years, the Princeton land surface hydrology group is now developing a global drought monitoring and forecasting system using a dynamical seasonal climate-hydrologic LSM-model (CHM) approach. Currently there is an active debate on the merits of the CHM-based seasonal hydrologic forecasts as compared to Ensemble Streamflow Prediction (ESP). We use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2) and its previous version CFSv1, to investigate the value of seasonal climate model forecasts by conducting a set of 27-year seasonal hydrologic hindcasts over the USA. Through Bayesian downscaling, climate models have higher squared correlation (R2) and smaller error than ESP for monthly precipitation averaged over major river basins across the USA, and the forecasts conditional on ENSO show further improvements (out to four months) over river basins in the southern USA. All three approaches have plausible predictions of soil moisture drought frequency over central USA out to six months because of strong soil moisture memory, and seasonal climate models provide better results over central and eastern USA. The R2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur in different seasons for different basins. The R2 of drought severity accumulated over USA is higher during winter, and climate models present added value especially at long leads. For countries with sparse networks and weak reporting systems, remote sensing observations can provide the realtime data for the monitoring of drought. More importantly, these datasets are now available for at least a decade, which allows for estimating a climatology against which current conditions can be compared. Based on our established experimental African Drought Monitor (ADM) (see http://hydrology.princeton.edu/~nchaney/ADM_ML), we use the downscaled CFSv2 climate forcings to drive the re-calibrated VIC model and produce 6-month, 20-member ensemble hydrologic forecasts over Africa starting on the 1st of each calendar month during 1982-2007. Our CHM-based seasonal hydrologic forecasts are now being analyzed for its skill in predicting short-term soil moisture droughts over Africa. Besides relying on a single seasonal climate model or a single drought index, preliminary forecast results will be presented using multiple seasonal climate models based on the NOAA-supported National Multi-Model Ensemble (NMME) project, and with multiple drought indices. Results will be presented for the USA NIDIS test beds such as Southeast US and Colorado NIDIS (National Integrated Drought Information System) test beds, and potentially for other regions of the globe.

Climate Change Adaptation Activities at the NASA John F. Kennedy Space Center, FL., USA

NASA Technical Reports Server (NTRS)

Hall, Carlton; Phillips, Lynne

2016-01-01

In 2010, the Office of Strategic Infrastructure and Earth Sciences established the Climate Adaptation Science Investigators (CASI) program to integrate climate change forecasts and knowledge into sustainable management of infrastructure and operations needed for the NASA mission. NASA operates 10 field centers valued at $32 billion dollars, occupies 191,000 acres and employs 58,000 people. CASI climate change and sea-level rise forecasts focus on the 2050 and 2080 time periods. At the 140,000 acre Kennedy Space Center (KSC) data are used to simulate impacts on infrastructure, operations, and unique natural resources. KSC launch and processing facilities represent a valued national asset located in an area with high biodiversity including 33 species of special management concern. Numerical and advanced Bayesian and Monte Carlo statistical modeling is being conducted using LiDAR digital elevation models coupled with relevant GIS layers to assess potential future conditions. Results are provided to the Environmental Management Branch, Master Planning, Construction of Facilities, Engineering Construction Innovation Committee and our regional partners to support Spaceport development, management, and adaptation planning and design. Potential impacts to natural resources include conversion of 50% of the Center to open water, elevation of the surficial aquifer, alterations of rainfall and evapotranspiration patterns, conversion of salt marsh to mangrove forest, reductions in distribution and extent of upland habitats, overwash of the barrier island dune system, increases in heat stress days, and releases of chemicals from legacy contamination sites. CASI has proven successful in bringing climate change planning to KSC including recognition of the need to increase resiliency and development of a green managed shoreline retreat approach to maintain coastal ecosystem services while maximizing life expectancy of Center launch and payload processing resources.

Climate Change Adaptation Activities at the NASA John F. Kennedy Space Center, Fl., USA

NASA Astrophysics Data System (ADS)

Hall, C. R.; Phillips, L. V.; Foster, T.; Stolen, E.; Duncan, B.; Hunt, D.; Schaub, R.

2016-12-01

In 2010, the Office of Strategic Infrastructure and Earth Sciences established the Climate Adaptation Science Investigators (CASI) program to integrate climate change forecasts and knowledge into sustainable management of infrastructure and operations needed for the NASA mission. NASA operates 10 field centers valued at $32 billion dollars, occupies 191,000 acres and employs 58,000 people. CASI climate change and sea-level rise forecasts focus on the 2050 and 2080 time periods. At the 140,000 acre Kennedy Space Center (KSC) data are used to simulate impacts on infrastructure, operations, and unique natural resources. KSC launch and processing facilities represent a valued national asset located in an area with high biodiversity including 33 species of special management concern. Numerical and advanced Bayesian and Monte Carlo statistical modeling is being conducted using LiDAR digital elevation models coupled with relevant GIS layers to assess potential future conditions. Results are provided to the Environmental Management Branch, Master Planning, Construction of Facilities, Engineering Construction Innovation Committee and our regional partners to support Spaceport development, management, and adaptation planning and design. Potential impacts to natural resources include conversion of 50% of the Center to open water, elevation of the surficial aquifer, alterations of rainfall and evapotranspiration patterns, conversion of salt marsh to mangrove forest, reductions in distribution and extent of upland habitats, overwash of the barrier island dune system, increases in heat stress days, and releases of chemicals from legacy contamination sites. CASI has proven successful in bringing climate change planning to KSC including recognition of the need to increase resiliency and development of a green managed shoreline retreat approach to maintain coastal ecosystem services while maximizing life expectancy of Center launch and payload processing resources.

Forecasting the Development of the Tourism Industry in the Regions of Russia in Light of Global Climate Change and Environmental Situation

NASA Astrophysics Data System (ADS)

Evreinov, O. B.; Maksimova, E. M.; Bakanova, A. A.; Yakovleva, M. P.

2014-12-01

Forecasting the development of the tourism industry is a strategic planning for periods ranging from 20 to 50 years. Basis for the development of tourism in the region is the presence of the necessary infrastructure - roads, communications, accommodation facilities and hospitality. Thus, all investments in the tourism industry are very long-term. Current approaches to long-term planning in tourism based on the most efficient use of the region's resources - natural, cultural, etc. But what will happen to these resources in 20-30 years? Global warming and climate change, a change in environmental conditions - all this gives the real impact today. Summer 2010 in Moscow and in the whole of Europe, warm snowless winters in St. Petersburg, monthly temperature records, permafrost thawing in Siberia - all this can affect the characteristics of the tourist regions in the future. In the presentation, the authors have tried to reflect the basic principles of strategic planning with regard to global and regional changes and to show the possible impact of such changes on Tourism industry in specific regions of Russia for the next 30-50 years.

Short-term load forecasting of power system

NASA Astrophysics Data System (ADS)

Xu, Xiaobin

2017-05-01

In order to ensure the scientific nature of optimization about power system, it is necessary to improve the load forecasting accuracy. Power system load forecasting is based on accurate statistical data and survey data, starting from the history and current situation of electricity consumption, with a scientific method to predict the future development trend of power load and change the law of science. Short-term load forecasting is the basis of power system operation and analysis, which is of great significance to unit combination, economic dispatch and safety check. Therefore, the load forecasting of the power system is explained in detail in this paper. First, we use the data from 2012 to 2014 to establish the partial least squares model to regression analysis the relationship between daily maximum load, daily minimum load, daily average load and each meteorological factor, and select the highest peak by observing the regression coefficient histogram Day maximum temperature, daily minimum temperature and daily average temperature as the meteorological factors to improve the accuracy of load forecasting indicators. Secondly, in the case of uncertain climate impact, we use the time series model to predict the load data for 2015, respectively, the 2009-2014 load data were sorted out, through the previous six years of the data to forecast the data for this time in 2015. The criterion for the accuracy of the prediction is the average of the standard deviations for the prediction results and average load for the previous six years. Finally, considering the climate effect, we use the BP neural network model to predict the data in 2015, and optimize the forecast results on the basis of the time series model.

Climate change, water rights, and water supply: The case of irrigated agriculture in Idaho

NASA Astrophysics Data System (ADS)

Xu, Wenchao; Lowe, Scott E.; Adams, Richard M.

2014-12-01

We conduct a hedonic analysis to estimate the response of agricultural land use to water supply information under the Prior Appropriation Doctrine by using Idaho as a case study. Our analysis includes long-term climate (weather) trends and water supply conditions as well as seasonal water supply forecasts. A farm-level panel data set, which accounts for the priority effects of water rights and controls for diversified crop mixes and rotation practices, is used. Our results indicate that farmers respond to the long-term surface and ground water conditions as well as to the seasonal water supply variations. Climate change-induced variations in climate and water supply conditions could lead to substantial damages to irrigated agriculture. We project substantial losses (up to 32%) of the average crop revenue for major agricultural areas under future climate scenarios in Idaho. Finally, farmers demonstrate significantly varied responses given their water rights priorities, which imply that the distributional impact of climate change is sensitive to institutions such as the Prior Appropriation Doctrine.

Long-term landscape changes in a subalpine spruce-fir forest in central Utah, USA

Treesearch

Jesse L. Morris; R. Justin DeRose; Andrea R. Brunelle

2015-01-01

In Western North America, increasing wildfire and outbreaks of native bark beetles have been mediated by warming climate conditions. Bioclimatic models forecast the loss of key high elevation species throughout the region. This study uses retrospective vegetation and fire history data to reconstruct the drivers of past disturbance and environmental change....

What might we learn from climate forecasts?

PubMed Central

Smith, Leonard A.

2002-01-01

Most climate models are large dynamical systems involving a million (or more) variables on big computers. Given that they are nonlinear and not perfect, what can we expect to learn from them about the earth's climate? How can we determine which aspects of their output might be useful and which are noise? And how should we distribute resources between making them “better,” estimating variables of true social and economic interest, and quantifying how good they are at the moment? Just as “chaos” prevents accurate weather forecasts, so model error precludes accurate forecasts of the distributions that define climate, yielding uncertainty of the second kind. Can we estimate the uncertainty in our uncertainty estimates? These questions are discussed. Ultimately, all uncertainty is quantified within a given modeling paradigm; our forecasts need never reflect the uncertainty in a physical system. PMID:11875200

Prediction of Seasonal Climate-induced Variations in Global Food Production

NASA Technical Reports Server (NTRS)

Iizumi, Toshichika; Sakuma, Hirofumi; Yokozawa, Masayuki; Luo, Jing-Jia; Challinor, Andrew J.; Brown, Molly E.; Sakurai, Gen; Yamagata, Toshio

2013-01-01

Consumers, including the poor in many countries, are increasingly dependent on food imports and are therefore exposed to variations in yields, production, and export prices in the major food-producing regions of the world. National governments and commercial entities are paying increased attention to the cropping forecasts of major food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We assessed the reliability of hindcasts (i.e., retrospective forecasts for the past) of crop yield loss relative to the previous year for two lead times. Pre-season yield predictions employ climatic forecasts and have lead times of approximately 3 to 5 months for providing information regarding variations in yields for the coming cropping season. Within-season yield predictions use climatic forecasts with lead times of 1 to 3 months. Pre-season predictions can be of value to national governments and commercial concerns, complemented by subsequent updates from within-season predictions. The latter incorporate information on the most recent climatic data for the upcoming period of reproductive growth. In addition to such predictions, hindcasts using observations from satellites were performed to demonstrate the upper limit of the reliability of crop forecasting.

National Centers for Environmental Prediction

Science.gov Websites

SYSTEM CFS CLIMATE FORECAST SYSTEM NAQFC NAQFC MODEL GEFS GLOBAL ENSEMBLE FORECAST SYSTEM HWRF HURRICANE WEATHER RESEARCH and FORECASTING HMON HMON - OPERATIONAL HURRICANE FORECASTING WAVEWATCH III WAVEWATCH III

Forecast Mekong 2012: Building scientific capacity

USGS Publications Warehouse

Stefanov, James E.

2012-01-01

In 2009, U.S. Secretary of State Hillary R. Clinton joined the Foreign Ministers of Cambodia, Laos, Thailand, and Vietnam in launching the Lower Mekong Initiative to enhance U.S. engagement with the countries of the Lower Mekong River Basin in the areas of environment, health, education, and infrastructure. The U.S. Geological Survey Forecast Mekong supports the Lower Mekong Initiative through a variety of activities. The principal objectives of Forecast Mekong include the following: * Build scientific capacity in the Lower Mekong Basin and promote cooperation and collaboration among scientists working in the region. * Provide data, information, and scientific models to help resource managers there make informed decisions. * Produce forecasting and visualization tools to support basin planning, including climate change adaptation. The focus of this product is Forecast Mekong accomplishments and current activities related to the development of scientific capacity at organizations and institutions in the region. Building on accomplishments in 2010 and 2011, Forecast Mekong continues to enhance scientific capacity in the Lower Mekong Basin with a suite of activities in 2012.

Experimental Forecasts of Wildfire Pollution at the Canadian Meteorological Centre

NASA Astrophysics Data System (ADS)

Pavlovic, Radenko; Beaulieu, Paul-Andre; Chen, Jack; Landry, Hugo; Cousineau, Sophie; Moran, Michael

2016-04-01

Environment and Climate Change Canada's Canadian Meteorological Centre Operations division (CMCO) has been running an experimental North American air quality forecast system with near-real-time wildfire emissions since 2014. This system, named FireWork, also takes anthropogenic and other natural emission sources into account. FireWork 48-hour forecasts are provided to CMCO forecasters and external partners in Canada and the U.S. twice daily during the wildfire season. This system has proven to be very useful in capturing short- and long-range smoke transport from wildfires over North America. Several upgrades to the FireWork system have been made since 2014 to accommodate the needs of operational AQ forecasters and to improve system performance. In this talk we will present performance statistics and some case studies for the 2014 and 2015 wildfire seasons. We will also describe current limitations of the FireWork system and ongoing and future work planned for this air quality forecast system.

Humidity-regulated dormancy onset in the Fabaceae: a conceptual model and its ecological implications for the Australian wattle Acacia saligna.

PubMed

Tozer, Mark G; Ooi, Mark K J

2014-09-01

Seed dormancy enhances fitness by preventing seeds from germinating when the probability of seedling survival and recruitment is low. The onset of physical dormancy is sensitive to humidity during ripening; however, the implications of this mechanism for seed bank dynamics have not been quantified. This study proposes a model that describes how humidity-regulated dormancy onset may control the accumulation of a dormant seed bank, and seed experiments are conducted to calibrate the model for an Australian Fabaceae, Acacia saligna. The model is used to investigate the impact of climate on seed dormancy and to forecast the ecological implications of human-induced climate change. The relationship between relative humidity and dormancy onset was quantified under laboratory conditions by exposing freshly matured non-dormant seeds to constant humidity levels for fixed durations. The model was field-calibrated by measuring the response of seeds exposed to naturally fluctuating humidity. The model was applied to 3-hourly records of humidity spanning the period 1972-2007 in order to estimate both temporal variability in dormancy and spatial variability attributable to climatic differences among populations. Climate change models were used to project future changes in dormancy onset. A sigmoidal relationship exists between dormancy and humidity under both laboratory and field conditions. Seeds ripened under field conditions became dormant following very short exposure to low humidity (<20 %). Prolonged exposure at higher humidity did not increase dormancy significantly. It is predicted that populations growing in a temperate climate produce 33-55 % fewer dormant seeds than those in a Mediterranean climate; however, dormancy in temperate populations is predicted to increase as a result of climate change. Humidity-regulated dormancy onset may explain observed variation in physical dormancy. The model offers a systematic approach to modelling this variation in population studies. Forecast changes in climate have the potential to alter the seed bank dynamics of species with physical dormancy regulated by this mechanism, with implications for their capacity to delay germination and exploit windows for recruitment. © The Author 2014. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Humidity-regulated dormancy onset in the Fabaceae: a conceptual model and its ecological implications for the Australian wattle Acacia saligna

PubMed Central

Tozer, Mark G.; Ooi, Mark K. J.

2014-01-01

Background and aims Seed dormancy enhances fitness by preventing seeds from germinating when the probability of seedling survival and recruitment is low. The onset of physical dormancy is sensitive to humidity during ripening; however, the implications of this mechanism for seed bank dynamics have not been quantified. This study proposes a model that describes how humidity-regulated dormancy onset may control the accumulation of a dormant seed bank, and seed experiments are conducted to calibrate the model for an Australian Fabaceae, Acacia saligna. The model is used to investigate the impact of climate on seed dormancy and to forecast the ecological implications of human-induced climate change. Methods The relationship between relative humidity and dormancy onset was quantified under laboratory conditions by exposing freshly matured non-dormant seeds to constant humidity levels for fixed durations. The model was field-calibrated by measuring the response of seeds exposed to naturally fluctuating humidity. The model was applied to 3-hourly records of humidity spanning the period 1972–2007 in order to estimate both temporal variability in dormancy and spatial variability attributable to climatic differences among populations. Climate change models were used to project future changes in dormancy onset. Key Results A sigmoidal relationship exists between dormancy and humidity under both laboratory and field conditions. Seeds ripened under field conditions became dormant following very short exposure to low humidity (<20 %). Prolonged exposure at higher humidity did not increase dormancy significantly. It is predicted that populations growing in a temperate climate produce 33–55 % fewer dormant seeds than those in a Mediterranean climate; however, dormancy in temperate populations is predicted to increase as a result of climate change. Conclusions Humidity-regulated dormancy onset may explain observed variation in physical dormancy. The model offers a systematic approach to modelling this variation in population studies. Forecast changes in climate have the potential to alter the seed bank dynamics of species with physical dormancy regulated by this mechanism, with implications for their capacity to delay germination and exploit windows for recruitment. PMID:25015069

Climate change summary

Treesearch

Steve McNulty; Jennifer Moore Myers; Peter Caldwell; Ge Sun

2013-01-01

Key FindingsSince 1960, all but two southern capital cities (Montgomery, AL, and Oklahoma City, OK) have experienced a statistically significant increase in average annual temperature (approximately 0.016° C), but none has experienced significant trends in precipitation.The South is forecasted to experience warmer temperatures...

Skilful rainfall forecasts from artificial neural networks with long duration series and single-month optimization

NASA Astrophysics Data System (ADS)

Abbot, John; Marohasy, Jennifer

2017-11-01

General circulation models, which forecast by first modelling actual conditions in the atmosphere and ocean, are used extensively for monthly rainfall forecasting. We show how more skilful monthly and seasonal rainfall forecasts can be achieved through the mining of historical climate data using artificial neural networks (ANNs). This technique is demonstrated for two agricultural regions of Australia: the wheat belt of Western Australia and the sugar growing region of coastal Queensland. The most skilful monthly rainfall forecasts measured in terms of Ideal Point Error (IPE), and a score relative to climatology, are consistently achieved through the use of ANNs optimized for each month individually, and also by choosing to input longer historical series of climate indices. Using the longer series restricts the number of climate indices that can be used.

Probabilistic empirical prediction of seasonal climate: evaluation and potential applications

NASA Astrophysics Data System (ADS)

Dieppois, B.; Eden, J.; van Oldenborgh, G. J.

2017-12-01

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a new evaluation of an established empirical system used to predict seasonal climate across the globe. Forecasts for surface air temperature, precipitation and sea level pressure are produced by the KNMI Probabilistic Empirical Prediction (K-PREP) system every month and disseminated via the KNMI Climate Explorer (climexp.knmi.nl). K-PREP is based on multiple linear regression and built on physical principles to the fullest extent with predictive information taken from the global CO2-equivalent concentration, large-scale modes of variability in the climate system and regional-scale information. K-PREP seasonal forecasts for the period 1981-2016 will be compared with corresponding dynamically generated forecasts produced by operational forecast systems. While there are many regions of the world where empirical forecast skill is extremely limited, several areas are identified where K-PREP offers comparable skill to dynamical systems. We discuss two key points in the future development and application of the K-PREP system: (a) the potential for K-PREP to provide a more useful basis for reference forecasts than those based on persistence or climatology, and (b) the added value of including K-PREP forecast information in multi-model forecast products, at least for known regions of good skill. We also discuss the potential development of stakeholder-driven applications of the K-PREP system, including empirical forecasts for circumboreal fire activity.

Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate

NASA Astrophysics Data System (ADS)

Bazile, Rachel; Boucher, Marie-Amélie; Perreault, Luc; Leconte, Robert

2017-11-01

Hydropower production requires optimal dam and reservoir management to prevent flooding damage and avoid operation losses. In a northern climate, where spring freshet constitutes the main inflow volume, seasonal forecasts can help to establish a yearly strategy. Long-term hydrological forecasts often rely on past observations of streamflow or meteorological data. Another alternative is to use ensemble meteorological forecasts produced by climate models. In this paper, those produced by the ECMWF (European Centre for Medium-Range Forecast) System 4 are examined and bias is characterized. Bias correction, through the linear scaling method, improves the performance of the raw ensemble meteorological forecasts in terms of continuous ranked probability score (CRPS). Then, three seasonal ensemble hydrological forecasting systems are compared: (1) the climatology of simulated streamflow, (2) the ensemble hydrological forecasts based on climatology (ESP) and (3) the hydrological forecasts based on bias-corrected ensemble meteorological forecasts from System 4 (corr-DSP). Simulated streamflow computed using observed meteorological data is used as benchmark. Accounting for initial conditions is valuable even for long-term forecasts. ESP and corr-DSP both outperform the climatology of simulated streamflow for lead times from 1 to 5 months depending on the season and watershed. Integrating information about future meteorological conditions also improves monthly volume forecasts. For the 1-month lead time, a gain exists for almost all watersheds during winter, summer and fall. However, volume forecasts performance for spring varies from one watershed to another. For most of them, the performance is close to the performance of ESP. For longer lead times, the CRPS skill score is mostly in favour of ESP, even if for many watersheds, ESP and corr-DSP have comparable skill. Corr-DSP appears quite reliable but, in some cases, under-dispersion or bias is observed. A more complex bias-correction method should be further investigated to remedy this weakness and take more advantage of the ensemble forecasts produced by the climate model. Overall, in this study, bias-corrected ensemble meteorological forecasts appear to be an interesting source of information for hydrological forecasting for lead times up to 1 month. They could also complement ESP for longer lead times.

Climate variables as predictors for seasonal forecast of dengue occurrence in Chennai, Tamil Nadu

NASA Astrophysics Data System (ADS)

Subash Kumar, D. D.; Andimuthu, R.

2013-12-01

Background Dengue is a recently emerging vector borne diseases in Chennai. As per the WHO report in 2011 dengue is one of eight climate sensitive disease of this century. Objective Therefore an attempt has been made to explore the influence of climate parameters on dengue occurrence and use for forecasting. Methodology Time series analysis has been applied to predict the number of dengue cases in Chennai, a metropolitan city which is the capital of Tamil Nadu, India. Cross correlation of the climate variables with dengue cases revealed that the most influential parameters were monthly relative humidity, minimum temperature at 4 months lag and rainfall at one month lag (Table 1). However due to intercorrelation of relative humidity and rainfall was high and therefore for predictive purpose the rainfall at one month lag was used for the model development. Autoregressive Integrated Moving Average (ARIMA) models have been applied to forecast the occurrence of dengue. Results and Discussion The best fit model was ARIMA (1,0,1). It was seen that the monthly minimum temperature at four months lag (β= 3.612, p = 0.02) and rainfall at one month lag (β= 0.032, p = 0.017) were associated with dengue occurrence and they had a very significant effect. Mean Relative Humidity had a directly significant positive correlation at 99% confidence level, but the lagged effect was not prominent. The model predicted dengue cases showed significantly high correlation of 0.814(Figure 1) with the observed cases. The RMSE of the model was 18.564 and MAE was 12.114. The model is limited by the scarcity of the dataset. Inclusion of socioeconomic conditions and population offset are further needed to be incorporated for effective results. Conclusion Thus it could be claimed that the change in climatic parameters is definitely influential in increasing the number of dengue occurrence in Chennai. The climate variables therefore can be used for seasonal forecasting of dengue with rise in minimum temperature and rainfall at a city level. Table 1. Cross correlation of climate variables with dengue cases in Chennai ** p<0.01,*p<0.05

Towards a Local-Scale Climate Service for Colombian Agriculture: Findings and Future Perspectives

NASA Astrophysics Data System (ADS)

Ramirez-Villegas, J.; Prager, S.; Llanos, L.; Agudelo, D.; Esquivel, A.; Sotelo, S.; Guevara, E.; Howland, F. C.; Munoz, A.; Rodriguez, J.; Ordonez, L.; Fernandes, K.

2017-12-01

Globally, interannual climate variability explains roughly a third of the yield variation for major crops. In Colombia, interannual climate variations and specially those driven by ENSO can disrupt production, lower farmers' incomes and increase market prices for both urban and rural consumers alike. Farmers in Colombia, however, often plan for the cropping season based on the immediately prior year's experience, which is unlikely to result in successful crops under high climate variability events. Critical decisions for avoiding total investment loss or to ensure successful harvests, including issues related to planting date, what variety to plant, or whether to plant, are made, at best, intuitively. Here, we demonstrate that the combination of better data, skillful seasonal climate forecasts, calibrated crop models, and a web-based climate services platform tailored to users' needs can prove successful in establishing a sustained climate service for agriculture. Rainfall predictability analyses indicate that statistical seasonal climate forecasts are skillful enough for issuing forecasts reliably in virtually all areas, with dry periods generally showing greater predictability than wet periods. Importantly, we find that a better specification of predictor regions significantly enhances seasonal forecast skill. Rice and maize crop models capture well the growth and development of rice and maize crops in experimental settings, and ably simulate historical (1980-2014) variations in productivity. With skillful climate and crop models, we developed a climate services platform that produces seasonal climate forecasts, and connects these with crop models. A usability study of the forecast platform revealed that, from a population of ca. 200 farmers and professionals, roughly two thirds correctly interpreted information and felt both confident and encouraged to use the platform. Nevertheless, capacity strengthening on key agro-climatology concepts was highlighted by farmers as a crucial need. Challenges also arose in certain zones due to limited access to electricity, computers or Internet. Based on our results, we conclude that for a climate service to be truly sustainable, well-calibrated and skillful models are as critical as the co-creation of the service itself with the stakeholder community.

Assessing the Dynamic Effects of Climate on Individual Tree Growth Across Time and Space

NASA Astrophysics Data System (ADS)

Itter, M.; Finley, A. O.; D'Amato, A. W.; Foster, J. R.; Bradford, J. B.

2015-12-01

The relationship between climate variability and an ecosystem process, such as forest growth, is frequently not fixed over time, but changes due to complex interactions between unobserved ecological factors and the process of interest. Climate data and forecasts are frequently spatially and temporally misaligned with ecological observations making inference regarding the effects of climate on ecosystem processes particularly challenging. Here we develop a Bayesian dynamic hierarchical model for annual tree growth increment that allows the effects of climate to evolve over time, applies climate data at a spatial-temporal scale consistent with observations, and controls for individual-level variability commonly encountered in ecological datasets. The model is applied to individual tree data from northern Minnesota using a modified Thornthwaite-type water balance model to transform PRISM temperature and precipitation estimates to physiologically relevant values of actual and potential evapotranspiration (AET, PET), and climatic water deficit. Model results indicate that mean tree growth is most sensitive to AET during the growing season and PET and minimum temperature in the spring prior to growth. The effects of these variables on tree growth, however, are not stationary with significant effects observed in only a subset of years during the 111-year study period. Importantly, significant effects of climate do not result from anomalous climate observations, but follow from large growth deviations unexplained by tree age and size, and time since forest disturbance. Results differ markedly from alternative models that assume the effects of climate are stationary over time or apply climate estimates at the individual scale. Forecasts of future tree growth as a function of climate follow directly from the dynamic hierarchical model allowing for assessment of forest change. Current work is focused on extending the model framework to include regional climate and ecosystem effects for application to a larger tree growth dataset spanning a latitudinal gradient within the US from Maine to Florida.

Predicting the spatial and temporal distributions of marine fish species utilizing earth system data in a MaxEnt model framework

NASA Astrophysics Data System (ADS)

Wang, L.; Kerr, L. A.; Bridger, E.

2016-02-01

Changes in species distributions have been widely associated with climate change. Understanding how ocean temperatures influence species distributions is critical for elucidating the role of climate in ecosystem change as well as for forecasting how species may be distributed in the future. As such, species distribution modeling (SDM) is increasingly useful in marine ecosystems research, as it can enable estimation of the likelihood of encountering marine fish in space or time as a function of a set of environmental and ecosystem conditions. Many traditional SDM approaches are applied to species data collected through standardized methods that include both presence and absence records, but are incapable of using presence-only data, such as those collected from fisheries or through citizen science programs. Maximum entropy (MaxEnt) models provide promising tools as they can predict species distributions from incomplete information (presence-only data). We developed a MaxEnt framework to relate the occurrence records of several marine fish species (e.g. Atlantic herring, Atlantic mackerel, and butterfish) to environmental conditions. Environmental variables derived from remote sensing, such as monthly average sea surface temperature (SST), are matched with fish species data, and model results indicate the relative occurrence rate of the species as a function of the environmental variables. The results can be used to provide hindcasts of where species might have been in the past in relation to historical environmental conditions, nowcasts in relation to current conditions, and forecasts of future species distributions. In this presentation, we will assess the relative influence of several environmental factors on marine fish species distributions, and evaluate the effects of data coverage on these presence-only models. We will also discuss how the information from species distribution forecasts can support climate adaptation planning in marine fisheries.

A changing climate: impacts on human exposures to O3 using ...

EPA Pesticide Factsheets

Predicting the impacts of changing climate on human exposure to air pollution requires future scenarios that account for changes in ambient pollutant concentrations, population sizes and distributions, and housing stocks. An integrated methodology to model changes in human exposures due to these impacts was developed by linking climate, air quality, land-use, and human exposure models. This methodology was then applied to characterize changes in predicted human exposures to O3 under multiple future scenarios. Regional climate projections for the U.S. were developed by downscaling global circulation model (GCM) scenarios for three of the Intergovernmental Panel on Climate Change’s (IPCC’s) Representative Concentration Pathways (RCPs) using the Weather Research and Forecasting (WRF) model. The regional climate results were in turn used to generate air quality (concentration) projections using the Community Multiscale Air Quality (CMAQ) model. For each of the climate change scenarios, future U.S. census-tract level population distributions from the Integrated Climate and Land Use Scenarios (ICLUS) model for four future scenarios based on the IPCC’s Special Report on Emissions Scenarios (SRES) storylines were used. These climate, air quality, and population projections were used as inputs to EPA’s Air Pollutants Exposure (APEX) model for 12 U.S. cities. Probability density functions show changes in the population distribution of 8 h maximum daily O3 exposur

Added value of dynamical downscaling of winter seasonal forecasts over North America

NASA Astrophysics Data System (ADS)

Tefera Diro, Gulilat; Sushama, Laxmi

2017-04-01

Skillful seasonal forecasts have enormous potential benefits for socio-economic sectors that are sensitive to weather and climate conditions, as the early warning routines could reduce the vulnerability of such sectors. In this study, individual ensemble members of the ECMWF global ensemble seasonal forecasts are dynamically downscaled to produce ensemble of regional seasonal forecasts over North America using the fifth generation Canadian Regional Climate Model (CRCM5). CRCM5 forecasts are initialized on November 1st of each year and are integrated for four months for the 1991-2001 period at 0.22 degree resolution to produce a one-month lead-time forecast. The initial conditions for atmospheric variables are obtained from ERA-Interim reanalysis, whereas the initial conditions for land surface are obtained from a separate ERA-interim driven CRCM5 simulation with spectral nudging applied to the interior domain. The global and regional ensemble forecasts were then verified to investigate the skill and economic benefits of dynamical downscaling. Results indicate that both the global and regional climate models produce skillful precipitation forecast over the southern Great Plains and eastern coasts of the U.S and skillful temperature forecasts over the northern U.S. and most of Canada. In comparison to ECMWF forecasts, CRCM5 forecasts improved the temperature forecast skill over most part of the domain, but the improvements for precipitation is limited to regions with complex topography, where it improves the frequency of intense daily precipitation. CRCM5 forecast also yields a better economic value compared to ECMWF precipitation forecasts, for users whose cost to loss ratio is smaller than 0.5.

Forecasted Impact of Climate Change on Infectious Disease and Health Security in Hawaii by 2050.

PubMed

Canyon, Deon V; Speare, Rick; Burkle, Frederick M

2016-12-01

Climate change is expected to cause extensive shifts in the epidemiology of infectious and vector-borne diseases. Scenarios on the effects of climate change typically attribute altered distribution of communicable diseases to a rise in average temperature and altered incidence of infectious diseases to weather extremes. Recent evaluations of the effects of climate change on Hawaii have not explored this link. It may be expected that Hawaii's natural geography and robust water, sanitation, and health care infrastructure renders residents less vulnerable to many threats that are the focus on smaller, lesser developed, and more vulnerable Pacific islands. In addition, Hawaii's communicable disease surveillance and response system can act rapidly to counter increases in any disease above baseline and to redirect resources to deal with changes, particularly outbreaks due to exotic pathogens. The evidence base examined in this article consistently revealed very low climate sensitivity with respect to infectious and mosquito-borne diseases. A community resilience model is recommended to increase adaptive capacity for all possible climate change impacts rather an approach that focuses specifically on communicable diseases. (Disaster Med Public Health Preparedness. 2016;10:797-804).

The evolutionary time machine: forecasting how populations can adapt to changing environments using dormant propagules

PubMed Central

Orsini, Luisa; Schwenk, Klaus; De Meester, Luc; Colbourne, John K.; Pfrender, Michael E.; Weider, Lawrence J.

2013-01-01

Evolutionary changes are determined by a complex assortment of ecological, demographic and adaptive histories. Predicting how evolution will shape the genetic structures of populations coping with current (and future) environmental challenges has principally relied on investigations through space, in lieu of time, because long-term phenotypic and molecular data are scarce. Yet, dormant propagules in sediments, soils and permafrost are convenient natural archives of population-histories from which to trace adaptive trajectories along extended time periods. DNA sequence data obtained from these natural archives, combined with pioneering methods for analyzing both ecological and population genomic time-series data, are likely to provide predictive models to forecast evolutionary responses of natural populations to environmental changes resulting from natural and anthropogenic stressors, including climate change. PMID:23395434

GCM Hindcasts for SST Forced Climate Variability over Agriculturally Intensive Regions

NASA Technical Reports Server (NTRS)

Druyan, Leonard M.; Shah, Kathryn P.; Chandler, Mark A.; Rind, David

1998-01-01

The ability to forecast seasonal climate is of great practical interest. One of the most obvious benefits would be agriculture, for which various preparations (planting, machinery, irrigation, manpower) would be enabled. The expectation of being able to make such forecasts far enough in advance (on the order of 9 months) hinges on components of the system with the longest persistence or predictability. The mixed results of El Nino forecasts has raised the hope that tropical Pacific sea surface temperatures (SST) fall into this category. For agriculturally-relevant forecasts to be made, and utilized, requires several conditions. The SST in the regions that affect agricultural areas must be forecast successfully, many months in advance. The climate response to such sea surface temperatures must then be ascertained, either through the use of historical empirical studies or models (e.g., GCMS). For practical applications, the agricultural production must be strongly influenced by climate, and farmers on either the local level or through commercial concerns must be able to adjust to using such forecasts. In a continuing series of papers, we will explore each of these components. This article concerns the question of utilizing SST to forecast the climate in several regions of agricultural production. We optimize the possibility of doing so successfully by using observed SST in a hindcast mode (i.e., a perfect forecast), and we also use the globally observed values (rather than just those from the tropical Pacific, for which predictability has been shown). This then is the ideal situation; in subsequent papers we will explore degrading the results by using only tropical Pacific SSTs, and then using only

Hurricane Sandy: Caught in the eye of the storm and a city's adaptation response

NASA Astrophysics Data System (ADS)

Orton, P. M.; Horton, R. M.; Blumberg, A. F.; Rosenzweig, C.; Solecki, W.; Bader, D.

2015-12-01

The NOAA RISA program has funded the seven-institution Consortium for Climate Risk in the Urban Northeast (CCRUN) for the past five years to serve stakeholder needs in assessing and managing risks from climate variability and change. When Hurricane Sandy struck, we were in an ideal position, making flood forecasts and communicating NOAA forecasts to the public with dozens of media placements, translating the poorly understood flood forecasts into human dimensions. In 2013 and 2015, by request of New York City (NYC), we worked through the NYC Panel on Climate Change to deliver updated climate risk assessment reports, to be used in the post-Sandy rebuilding and resiliency efforts. These utilized innovative methodologies for probabilistic local and regional sea level change projections, and contrasted methods of dynamic versus (the more common) static flood mapping. We participated in a federal-academic partnership that developed a Sea Level Tool for Sandy Recovery that integrates CCRUN sea level rise projections with policy-relevant FEMA flood maps, and now several updated flood maps and coastal flood mapping tools (NOAA, FEMA, and USACE) incorporate our projections. For the adaptation response, we helped develop NYC's $20 billion flood adaptation plan, and we were on a winning team under the Housing and Urban Development Rebuild By Design (RBD) competition, a few of the many opportunities that arose with negligible additional funding and which CCRUN funds supported. Our work at times disrupted standard lines of thinking, but NYC showed an openness to altering course. In one case we showed that an NYC plan of wetland restoration in Jamaica Bay would provide no reduction in flooding unless deep-dredged channels circumventing them were shallowed or narrowed. In another, the lead author's RBD team challenged the notion at one location that levees were the solution to accelerating sea level rise, developing a plan to use ecological breakwaters and layered components of physical and social resilience. CCRUN has succeeded in winning another five years of RISA funding, and this will enable us to continue our climate risk and adaptation work for the entire Urban Northeast.

Forecasting the evolution in the mixing regime of a deep subalpine lake under climate change scenarios through numerical modelling (Lake Maggiore, Northern Italy/Southern Switzerland)

NASA Astrophysics Data System (ADS)

Fenocchi, Andrea; Rogora, Michela; Sibilla, Stefano; Ciampittiello, Marzia; Dresti, Claudia

2018-01-01

The impact of air temperature rise is eminent for the large deep lakes in the Italian subalpine district, climate change being caused there by both natural phenomena and anthropogenic greenhouse-gases (GHG) emissions. These oligomictic lakes are experiencing a decrease in the frequency of winter full turnover and an intensification of stability. As a result, hypolimnetic oxygen concentrations are decreasing and nutrients are accumulating in bottom water, with effects on the whole ecosystem functioning. Forecasting the future evolution of the mixing pattern is relevant to assess if a reduction in GHG releases would be able to revert such processes. The study focuses on Lake Maggiore, for which the thermal structure evolution under climate change in the 2016-2085 period was assessed through numerical simulations, performed with the General Lake Model (GLM). Different prospects of regional air temperature rise were considered, given by the Swiss Climate Change Scenarios CH2011. Multiple realisations were performed for each scenario to obtain robust statistical predictions, adopting random series of meteorological data produced with the Vector-Autoregressive Weather Generator (VG). Results show that a reversion in the increasing thermal stability would be possible only if global GHG emissions started to be reduced by 2020, allowing an equilibrium mixing regime to be restored by the end of the twenty-first century. Otherwise, persistent lack of complete-mixing, severe water warming and extensive effects on water quality are to be expected for the centuries to come. These projections can be extended to the other lakes in the subalpine district.

Ecological forecasting in the presence of abrupt regime shifts

NASA Astrophysics Data System (ADS)

Dippner, Joachim W.; Kröncke, Ingrid

2015-10-01

Regime shifts may cause an intrinsic decrease in the potential predictability of marine ecosystems. In such cases, forecasts of biological variables fail. To improve prediction of long-term variability in environmental variables, we constructed a multivariate climate index and applied it to forecast ecological time series. The concept is demonstrated herein using climate and macrozoobenthos data from the southern North Sea. Special emphasis is given to the influence of selection of length of fitting period to the quality of forecast skill especially in the presence of regime shifts. Our results indicate that the performance of multivariate predictors in biological forecasts is much better than that of single large-scale climate indices, especially in the presence of regime shifts. The approach used to develop the index is generally applicable to all geographical regions in the world and to all areas of marine biology, from the species level up to biodiversity. Such forecasts are of vital interest for practical aspects of the sustainable management of marine ecosystems and the conservation of ecosystem goods and services.

Life history and spatial traits predict extinction risk due to climate change

NASA Astrophysics Data System (ADS)

Pearson, Richard G.; Stanton, Jessica C.; Shoemaker, Kevin T.; Aiello-Lammens, Matthew E.; Ersts, Peter J.; Horning, Ned; Fordham, Damien A.; Raxworthy, Christopher J.; Ryu, Hae Yeong; McNees, Jason; Akçakaya, H. Reşit

2014-03-01

There is an urgent need to develop effective vulnerability assessments for evaluating the conservation status of species in a changing climate. Several new assessment approaches have been proposed for evaluating the vulnerability of species to climate change based on the expectation that established assessments such as the IUCN Red List need revising or superseding in light of the threat that climate change brings. However, although previous studies have identified ecological and life history attributes that characterize declining species or those listed as threatened, no study so far has undertaken a quantitative analysis of the attributes that cause species to be at high risk of extinction specifically due to climate change. We developed a simulation approach based on generic life history types to show here that extinction risk due to climate change can be predicted using a mixture of spatial and demographic variables that can be measured in the present day without the need for complex forecasting models. Most of the variables we found to be important for predicting extinction risk, including occupied area and population size, are already used in species conservation assessments, indicating that present systems may be better able to identify species vulnerable to climate change than previously thought. Therefore, although climate change brings many new conservation challenges, we find that it may not be fundamentally different from other threats in terms of assessing extinction risks.

Weather and seasonal climate prediction for South America using a multi-model superensemble

NASA Astrophysics Data System (ADS)

Chaves, Rosane R.; Ross, Robert S.; Krishnamurti, T. N.

2005-11-01

This work examines the feasibility of weather and seasonal climate predictions for South America using the multi-model synthetic superensemble approach for climate, and the multi-model conventional superensemble approach for numerical weather prediction, both developed at Florida State University (FSU). The effect on seasonal climate forecasts of the number of models used in the synthetic superensemble is investigated. It is shown that the synthetic superensemble approach for climate and the conventional superensemble approach for numerical weather prediction can reduce the errors over South America in seasonal climate prediction and numerical weather prediction.For climate prediction, a suite of 13 models is used. The forecast lead-time is 1 month for the climate forecasts, which consist of precipitation and surface temperature forecasts. The multi-model ensemble is comprised of four versions of the FSU-Coupled Ocean-Atmosphere Model, seven models from the Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction (DEMETER), a version of the Community Climate Model (CCM3), and a version of the predictive Ocean Atmosphere Model for Australia (POAMA). The results show that conditions over South America are appropriately simulated by the Florida State University Synthetic Superensemble (FSUSSE) in comparison to observations and that the skill of this approach increases with the use of additional models in the ensemble. When compared to observations, the forecasts are generally better than those from both a single climate model and the multi-model ensemble mean, for the variables tested in this study.For numerical weather prediction, the conventional Florida State University Superensemble (FSUSE) is used to predict the mass and motion fields over South America. Predictions of mean sea level pressure, 500 hPa geopotential height, and 850 hPa wind are made with a multi-model superensemble comprised of six global models for the period January, February, and December of 2000. The six global models are from the following forecast centers: FSU, Bureau of Meteorology Research Center (BMRC), Japan Meteorological Agency (JMA), National Centers for Environmental Prediction (NCEP), Naval Research Laboratory (NRL), and Recherche en Prevision Numerique (RPN). Predictions of precipitation are made for the period January, February, and December of 2001 with a multi-analysis-multi-model superensemble where, in addition to the six forecast models just mentioned, five additional versions of the FSU model are used in the ensemble, each with a different initialization (analysis) based on different physical initialization procedures. On the basis of observations, the results show that the FSUSE provides the best forecasts of the mass and motion field variables to forecast day 5, when compared to both the models comprising the ensemble and the multi-model ensemble mean during the wet season of December-February over South America. Individual case studies show that the FSUSE provides excellent predictions of rainfall for particular synoptic events to forecast day 3. Copyright

Measuring the burden of disease due to climate change and developing a forecast model in South Korea.

PubMed

Yoon, S-J; Oh, I-H; Seo, H-Y; Kim, E-J

2014-08-01

Climate change influences human health in various ways, and quantitative assessments of the effect of climate change on health at national level are becoming essential for environmental health management. This study quantified the burden of disease attributable to climate change in Korea using disability-adjusted life years (DALY), and projected how this would change over time. Diseases related to climate change in Korea were selected, and meteorological data for each risk factor of climate change were collected. Mortality was calculated, and a database of incidence and prevalence was established. After measuring the burden of each disease, the total burden of disease related to climate change was assessed by multiplying population-attributable fractions. Finally, an estimation model for the burden of disease was built based on Korean climate data. The total burden of disease related to climate change in Korea was 6.85 DALY/1000 population in 2008. Cerebrovascular diseases induced by heat waves accounted for 72.1% of the total burden of disease (hypertensive disease 1.82 DALY/1000 population, ischaemic heart disease 1.56 DALY/1000 population, cerebrovascular disease 1.56 DALY/1000 population). According to the estimation model, the total burden of disease will be 11.48 DALY/1000 population in 2100, which is twice the total burden of disease in 2008. This study quantified the burden of disease caused by climate change in Korea, and provides valuable information for determining the priorities of environmental health policy in East Asian countries with similar climates. Copyright © 2014 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Providing peak river flow statistics and forecasting in the Niger River basin

NASA Astrophysics Data System (ADS)

Andersson, Jafet C. M.; Ali, Abdou; Arheimer, Berit; Gustafsson, David; Minoungou, Bernard

2017-08-01

Flooding is a growing concern in West Africa. Improved quantification of discharge extremes and associated uncertainties is needed to improve infrastructure design, and operational forecasting is needed to provide timely warnings. In this study, we use discharge observations, a hydrological model (Niger-HYPE) and extreme value analysis to estimate peak river flow statistics (e.g. the discharge magnitude with a 100-year return period) across the Niger River basin. To test the model's capacity of predicting peak flows, we compared 30-year maximum discharge and peak flow statistics derived from the model vs. derived from nine observation stations. The results indicate that the model simulates peak discharge reasonably well (on average + 20%). However, the peak flow statistics have a large uncertainty range, which ought to be considered in infrastructure design. We then applied the methodology to derive basin-wide maps of peak flow statistics and their associated uncertainty. The results indicate that the method is applicable across the hydrologically active part of the river basin, and that the uncertainty varies substantially depending on location. Subsequently, we used the most recent bias-corrected climate projections to analyze potential changes in peak flow statistics in a changed climate. The results are generally ambiguous, with consistent changes only in very few areas. To test the forecasting capacity, we ran Niger-HYPE with a combination of meteorological data sets for the 2008 high-flow season and compared with observations. The results indicate reasonable forecasting capacity (on average 17% deviation), but additional years should also be evaluated. We finish by presenting a strategy and pilot project which will develop an operational flood monitoring and forecasting system based in-situ data, earth observations, modelling, and extreme statistics. In this way we aim to build capacity to ultimately improve resilience toward floods, protecting lives and infrastructure in the region.

Can Recent Global Changes Explain the Dramatic Range Contraction of an Endangered Semi-Aquatic Mammal Species in the French Pyrenees?

PubMed

Charbonnel, Anaïs; Laffaille, Pascal; Biffi, Marjorie; Blanc, Frédéric; Maire, Anthony; Némoz, Mélanie; Sanchez-Perez, José Miguel; Sauvage, Sabine; Buisson, Laëtitia

2016-01-01

Species distribution models (SDMs) are the main tool to predict global change impacts on species ranges. Climate change alone is frequently considered, but in freshwater ecosystems, hydrology is a key driver of the ecology of aquatic species. At large scale, hydrology is however rarely accounted for, owing to the lack of detailed stream flow data. In this study, we developed an integrated modelling approach to simulate stream flow using the hydrological Soil and Water Assessment Tool (SWAT). Simulated stream flow was subsequently included as an input variable in SDMs along with topographic, hydrographic, climatic and land-cover descriptors. SDMs were applied to two temporally-distinct surveys of the distribution of the endangered Pyrenean desman (Galemys pyrenaicus) in the French Pyrenees: a historical one conducted from 1985 to 1992 and a current one carried out between 2011 and 2013. The model calibrated on historical data was also forecasted onto the current period to assess its ability to describe the distributional change of the Pyrenean desman that has been modelled in the recent years. First, we found that hydrological and climatic variables were the ones influencing the most the distribution of this species for both periods, emphasizing the importance of taking into account hydrology when SDMs are applied to aquatic species. Secondly, our results highlighted a strong range contraction of the Pyrenean desman in the French Pyrenees over the last 25 years. Given that this range contraction was under-estimated when the historical model was forecasted onto current conditions, this finding suggests that other drivers may be interacting with climate, hydrology and land-use changes. Our results imply major concerns for the conservation of this endemic semi-aquatic mammal since changes in climate and hydrology are expected to become more intense in the future.

Can Recent Global Changes Explain the Dramatic Range Contraction of an Endangered Semi-Aquatic Mammal Species in the French Pyrenees?

PubMed Central

Charbonnel, Anaïs; Laffaille, Pascal; Biffi, Marjorie; Blanc, Frédéric; Maire, Anthony; Némoz, Mélanie; Sanchez-Perez, José Miguel; Sauvage, Sabine

2016-01-01

Species distribution models (SDMs) are the main tool to predict global change impacts on species ranges. Climate change alone is frequently considered, but in freshwater ecosystems, hydrology is a key driver of the ecology of aquatic species. At large scale, hydrology is however rarely accounted for, owing to the lack of detailed stream flow data. In this study, we developed an integrated modelling approach to simulate stream flow using the hydrological Soil and Water Assessment Tool (SWAT). Simulated stream flow was subsequently included as an input variable in SDMs along with topographic, hydrographic, climatic and land-cover descriptors. SDMs were applied to two temporally-distinct surveys of the distribution of the endangered Pyrenean desman (Galemys pyrenaicus) in the French Pyrenees: a historical one conducted from 1985 to 1992 and a current one carried out between 2011 and 2013. The model calibrated on historical data was also forecasted onto the current period to assess its ability to describe the distributional change of the Pyrenean desman that has been modelled in the recent years. First, we found that hydrological and climatic variables were the ones influencing the most the distribution of this species for both periods, emphasizing the importance of taking into account hydrology when SDMs are applied to aquatic species. Secondly, our results highlighted a strong range contraction of the Pyrenean desman in the French Pyrenees over the last 25 years. Given that this range contraction was under-estimated when the historical model was forecasted onto current conditions, this finding suggests that other drivers may be interacting with climate, hydrology and land-use changes. Our results imply major concerns for the conservation of this endemic semi-aquatic mammal since changes in climate and hydrology are expected to become more intense in the future. PMID:27467269

How Insects Survive Winter in the Midwest

USDA-ARS?s Scientific Manuscript database

Understanding how insects cope with cold temperatures can not only help entomologists more accurately forecast when and where insects are active, but it may also help us understand how climate change will influence insect pests. This newsletter article provides a comprehensive overview of how Midwes...

Global Warming: Understanding and Teaching the Forecast. Part A The Greenhouse Effect.

ERIC Educational Resources Information Center

Andrews, Bill

1993-01-01

Provides information necessary for an interdisciplinary analysis of the greenhouse effect, enhanced greenhouse effect, global warming, global climate change, greenhouse gases, carbon dioxide, and scientific study of global warming for students grades 4-12. Several activity ideas accompany the information. (LZ)

Connecting Stakeholders and Climate Science: A Summary of Farmer, Rancher, and Forester Climate Data Needs and Climate Change Attitudes

NASA Astrophysics Data System (ADS)

Rango, A.; Crimmins, M.; Elias, E.; Steele, C. M.; Weiss, J. L.

2015-12-01

The mission of the USDA Southwest Regional Climate Hub is to provide farmers, ranchers and forest land owners and managers with information and resources to cope with the impacts of climate change. As such, a clear understanding of landowner needs for weather and climate data and their attitudes about climate change is required. Here we present a summary of results from 17 peer-reviewed articles on studies pertaining to landowner needs and attitudes towards climate change adaptation and mitigation that span much of the continental U.S. and ideally represent a cross-section of different geographies. In general, approximately 75% of landowners and farm advisors believe climate change is occurring, but disagree on the human contribution. Studies found that most farmers were supportive of adaptation responses, but fewer endorsed farm-based greenhouse gas reduction mitigation strategies. Adaptation is often driven by local concerns and requires locally specific strategies. Perceiving weather variability increased belief in human-caused climate change. Presently farmers and ranchers rely on past experience and short-range forecasts (weeks to seasons) whereas some foresters are requesting long-term predictions on the order of years to decades. Foresters indicated that most of them (74%) are presently unable to find needed long-term information. We augment peer-reviewed literature with observations from landowner workshops conducted in Nevada and Arizona during 2014, the first year of Climate Hub operation. To better collect information about climate change needs and attitudes of farmers, ranchers and foresters across the globe, we created a Climate Change Attitudes collection in JournalMap (https://journalmap.org/usda-southwest-regional-climate-hub/climate-change-attitudes). Users anywhere can add articles to this collection, ultimately generating a comprehensive spatial resource in support of adaptation and mitigation efforts on working lands.

On the Dominant Factor Controlling Seasonal Hydrological Forecast Skill in China

DOE PAGES

Zhang, Xuejun; Tang, Qiuhong; Leng, Guoyong; ...

2017-11-20

Initial conditions (ICs) and climate forecasts (CFs) are the two primary sources of seasonal hydrological forecast skill. However, their relative contribution to predictive skill remains unclear in China. In this study, we investigate the relative roles of ICs and CFs in cumulative runoff (CR) and soil moisture (SM) forecasts using 31-year (1980–2010) ensemble streamflow prediction (ESP) and reverse-ESP (revESP) simulations with the Variable Capacity Infiltration (VIC) hydrologic model. The results show that the relative importance of ICs and CFs largely depends on climate regimes. The influence of ICs is stronger in a dry or wet-to-dry climate regime that covers themore » northern and western interior regions during the late fall to early summer. In particular, ICs may dominate the forecast skill for up to three months or even six months during late fall and winter months, probably due to the low precipitation value and variability in the dry period. In contrast, CFs become more important for most of southern China or during summer months. The impact of ICs on SM forecasts tends to cover larger domains than on CR forecasts. These findings will greatly benefit future work that will target efforts towards improving current forecast levels for the particular regions and forecast periods.« less

On the Dominant Factor Controlling Seasonal Hydrological Forecast Skill in China

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

Zhang, Xuejun; Tang, Qiuhong; Leng, Guoyong

Initial conditions (ICs) and climate forecasts (CFs) are the two primary sources of seasonal hydrological forecast skill. However, their relative contribution to predictive skill remains unclear in China. In this study, we investigate the relative roles of ICs and CFs in cumulative runoff (CR) and soil moisture (SM) forecasts using 31-year (1980–2010) ensemble streamflow prediction (ESP) and reverse-ESP (revESP) simulations with the Variable Capacity Infiltration (VIC) hydrologic model. The results show that the relative importance of ICs and CFs largely depends on climate regimes. The influence of ICs is stronger in a dry or wet-to-dry climate regime that covers themore » northern and western interior regions during the late fall to early summer. In particular, ICs may dominate the forecast skill for up to three months or even six months during late fall and winter months, probably due to the low precipitation value and variability in the dry period. In contrast, CFs become more important for most of southern China or during summer months. The impact of ICs on SM forecasts tends to cover larger domains than on CR forecasts. These findings will greatly benefit future work that will target efforts towards improving current forecast levels for the particular regions and forecast periods.« less

Global climate changes as forecast by Goddard Institute for Space Studies three-dimensional model

NASA Technical Reports Server (NTRS)

Hansen, J.; Fung, I.; Lacis, A.; Rind, D.; Lebedeff, S.; Ruedy, R.; Russell, G.

1988-01-01

The global climate effects of time-dependent atmospheric trace gas and aerosol variations are simulated by NASA-Goddard's three-dimensional climate model II, which possesses 8 x 10-deg horizontal resolution, for the cases of a 100-year control run and three different atmospheric composition scenarios in which trace gas growth is respectively a continuation of current exponential trends, a reduced linear growth, and a rapid curtailment of emissions due to which net climate forcing no longer increases after the year 2000. The experiments begin in 1958, run to the present, and encompass measured or estimated changes in CO2, CH4, N2O, chlorofluorocarbons, and stratospheric aerosols. It is shown that the greenhouse warming effect may be clearly identifiable in the 1990s.

New watershed-based climate forecast products for hydrologists and water managers

NASA Astrophysics Data System (ADS)

Baker, S. A.; Wood, A.; Rajagopalan, B.; Lehner, F.; Peng, P.; Ray, A. J.; Barsugli, J. J.; Werner, K.

2017-12-01

Operational sub-seasonal to seasonal (S2S) climate predictions have advanced in skill in recent years but are yet to be broadly utilized by stakeholders in the water management sector. While some of the challenges that relate to fundamental predictability are difficult or impossible to surmount, other hurdles related to forecast product formulation, translation, relevance, and accessibility can be directly addressed. These include products being misaligned with users' space-time needs, products disseminated in formats users cannot easily process, and products based on raw model outputs that are biased relative to user climatologies. In each of these areas, more can be done to bridge the gap by enhancing the usability, quality, and relevance of water-oriented predictions. In addition, water stakeholder impacts can benefit from short-range extremes predictions (such as 2-3 day storms or 1-week heat waves) at S2S time-scales, for which few products exist. We present interim results of a Research to Operations (R2O) effort sponsored by the NOAA MAPP Climate Testbed to (1) formulate climate prediction products so as to reduce hurdles to in water stakeholder adoption, and to (2) explore opportunities for extremes prediction at S2S time scales. The project is currently using CFSv2 and National Multi-­Model Ensemble (NMME) reforecasts and forecasts to develop real-time watershed-based climate forecast products, and to train post-processing approaches to enhance the skill and reliability of raw real-time S2S forecasts. Prototype S2S climate data products (forecasts and associated skill analyses) are now being operationally staged at NCAR on a public website to facilitate further product development through interactions with water managers. Initial demonstration products include CFSv2-based bi-weekly climate forecasts (weeks 1-2, 2-3, and 3-4) for sub-regional scale hydrologic units, and NMME-based monthly and seasonal prediction products. Raw model mean skill at these time-space resolutions for some periods (e.g., weeks 3-4) is unusably low, but for other periods, and for multi-month leads with NMME, precipitation and particularly temperature forecasts exhibit useful skill. Website: http://hydro.rap.ucar.edu/s2s/

Application of a GCM Ensemble Seasonal Climate Forecasts to Crop Yield Prediction in East Africa

NASA Astrophysics Data System (ADS)

Ogutu, G.; Franssen, W.; Supit, I.; Hutjes, R. W. A.

2016-12-01

We evaluated the potential use of ECMWF System-4 seasonal climate forecasts (S4) for impacts analysis over East Africa. Using the 15 member, 7 months ensemble forecasts initiated every month for 1981-2010, we tested precipitation (tp), air temperature (tas) and surface shortwave radiation (rsds) forecast skill against the WATCH forcing Data ERA-Interim (WFDEI) re-analysis and other data. We used these forecasts as input in the WOFOST crop model to predict maize yields. Forecast skill is assessed using anomaly correlation (ACC), Ranked Probability Skill Score (RPSS) and the Relative Operating Curve Skill Score (ROCSS) for MAM, JJA and OND growing seasons. Predicted maize yields (S4-yields) are verified against historical observed FAO and nationally reported (NAT) yield statistics, and yields from the same crop model forced by WFDEI (WFDEI-yields). Predictability of the climate forecasts vary with season, location and lead-time. The OND tp forecasts show skill over a larger area up to three months lead-time compared to MAM and JJA. Upper- and lower-tercile tp forecasts are 20-80% better than climatology. Good tas forecast skill is apparent with three months lead-time. The rsds is less skillful than tp and tas in all seasons when verified against WFDEI but higher against others. S4-forecasts captures ENSO related anomalous years with region dependent skill. Anomalous ENSO influence is also seen in simulated yields. Focussing on the main sowing dates in the northern (July), equatorial (March-April) and southern (December) regions, WFDEI-yields are lower than FAO and NAT but anomalies are comparable. Yield anomalies are predictable 3-months before sowing in most of the regions. Differences in interannual variability in the range of ±40% may be related to sensitivity of WOFOST to drought stress while the ACCs are largely positive ranging from 0.3 to 0.6. Above and below-normal yields are predictable with 2-months lead time. We evidenced a potential use of seasonal climate forecasts with a crop simulation model to predict anomalous maize yields over East Africa. The findings open a window to better use of climate forecasts in food security early warning systems, and pre-season policy and farm management decisions.

Skillful seasonal predictions of winter precipitation over southern China

NASA Astrophysics Data System (ADS)

Lu, Bo; Scaife, Adam A.; Dunstone, Nick; Smith, Doug; Ren, Hong-Li; Liu, Ying; Eade, Rosie

2017-07-01

Southern China experiences large year-to-year variability in the amount of winter precipitation, which can result in severe social and economic impacts. In this study, we demonstrate prediction skill of southern China winter precipitation by three operational seasonal prediction models: the operational Global seasonal forecasting system version 5 (GloSea5), the NCEP Climate Forecast System (CFSv2) and the Beijing Climate Center Climate System Model (BCC-CSM1.1m). The correlation scores reach 0.76 and 0.67 in GloSea5 and CFSv2, respectively; and the amplitude of the ensemble mean forecast signal is comparable to the observed variations. The skilful predictions in GloSea5 and CFSv2 mainly benefit from the successful representation of the observed ENSO teleconnection. El Niño weakens the Walker circulation and leads to the strengthening of the subtropical high over the northwestern Pacific. The anti-cyclone then induces anomalous northward flow over the South China Sea and brings water vapor to southern China, resulting in more precipitation. This teleconnection pattern is too weak in BCC-CSM1.1m, which explains its low skill (0.13). Whereas the most skilful forecast system is also able to simulate the influence of the Indian Ocean on southern China precipitation via changes in southwesterly winds over the Bay of Bengal. Finally, we examine the real-time forecast for 2015/16 winter when a strong El Niño event led to the highest rainfall over southern China in recent decades. We find that the GloSea5 system gave good advice as it produced the third wettest southern China in the hindcast, but underestimated the observed amplitude. This is likely due to the underestimation of the Siberian High strength in 2015/2016 winter, which has driven strong convergence over southern China. We conclude that some current seasonal forecast systems can give useful warning of impending extremes. However, there is still need for further model improvement to fully represent the complex dynamics of the region.

Leveraging organismal biology to forecast the effects of climate change.

PubMed

Buckley, Lauren B; Cannistra, Anthony F; John, Aji

2018-04-26

Despite the pressing need for accurate forecasts of ecological and evolutionary responses to environmental change, commonly used modelling approaches exhibit mixed performance because they omit many important aspects of how organisms respond to spatially and temporally variable environments. Integrating models based on organismal phenotypes at the physiological, performance and fitness levels can improve model performance. We summarize current limitations of environmental data and models and discuss potential remedies. The paper reviews emerging techniques for sensing environments at fine spatial and temporal scales, accounting for environmental extremes, and capturing how organisms experience the environment. Intertidal mussel data illustrate biologically important aspects of environmental variability. We then discuss key challenges in translating environmental conditions into organismal performance including accounting for the varied timescales of physiological processes, for responses to environmental fluctuations including the onset of stress and other thresholds, and for how environmental sensitivities vary across lifecycles. We call for the creation of phenotypic databases to parameterize forecasting models and advocate for improved sharing of model code and data for model testing. We conclude with challenges in organismal biology that must be solved to improve forecasts over the next decade.acclimation, biophysical models, ecological forecasting, extremes, microclimate, spatial and temporal variability.

Complex systems approach to fire dynamics and climate change impacts

NASA Astrophysics Data System (ADS)

Pueyo, S.

2012-04-01

I present some recent advances in complex systems theory as a contribution to understanding fire regimes and forecasting their response to a changing climate, qualitatively and quantitatively. In many regions of the world, fire sizes have been found to follow, approximately, a power-law frequency distribution. As noted by several authors, this distribution also arises in the "forest fire" model used by physicists to study mechanisms that give rise to scale invariance (the power law is a scale-invariant distribution). However, this model does not give and does not pretend to give a realistic description of fire dynamics. For example, it gives no role to weather and climate. Pueyo (2007) developed a variant of the "forest fire" model that is also simple but attempts to be more realistic. It also results into a power law, but the parameters of this distribution change through time as a function of weather and climate. Pueyo (2007) observed similar patterns of response to weather in data from boreal forest fires, and used the fitted response functions to forecast fire size distributions in a possible climate change scenario, including the upper extreme of the distribution. For some parameter values, the model in Pueyo (2007) displays a qualitatively different behavior, consisting of simple percolation. In this case, fire is virtually absent, but megafires sweep through the ecosystem a soon as environmental forcings exceed a critical threshold. Evidence gathered by Pueyo et al. (2010) suggests that this is realistic for tropical rainforests (specifically, well-conserved upland rainforests). Some climate models suggest that major tropical rainforest regions are going to become hotter and drier if climate change goes ahead unchecked, which could cause such abrupt shifts. Not all fire regimes are well described by this model. Using data from a tropical savanna region, Pueyo et al. (2010) found that the dynamics in this area do not match its assumptions, even though fire sizes are also well fitted by a power law. A possible interpretation is that the spatial structure of fire in savannas is strongly constrained by the spatial structure of their environment. Instead of resulting from ecosystem self-organization as in the model, in this case the scale invariance in fire events would be just a reflection of scale invariance in the environment in which the ecosystem lives. These results suggest at least three major types of fire dynamics: endogenous scaling, percolating, and exogenous scaling, in addition to intermediate options. The world's biomes can be classified based on the type of dynamics that is most likely to apply in each of them, and forecasts can be carried out with the tools developed for each of these types.

Calibration of decadal ensemble predictions

NASA Astrophysics Data System (ADS)

Pasternack, Alexander; Rust, Henning W.; Bhend, Jonas; Liniger, Mark; Grieger, Jens; Müller, Wolfgang; Ulbrich, Uwe

2017-04-01

Decadal climate predictions are of great socio-economic interest due to the corresponding planning horizons of several political and economic decisions. Due to uncertainties of weather and climate, forecasts (e.g. due to initial condition uncertainty), they are issued in a probabilistic way. One issue frequently observed for probabilistic forecasts is that they tend to be not reliable, i.e. the forecasted probabilities are not consistent with the relative frequency of the associated observed events. Thus, these kind of forecasts need to be re-calibrated. While re-calibration methods for seasonal time scales are available and frequently applied, these methods still have to be adapted for decadal time scales and its characteristic problems like climate trend and lead time dependent bias. Regarding this, we propose a method to re-calibrate decadal ensemble predictions that takes the above mentioned characteristics into account. Finally, this method will be applied and validated to decadal forecasts from the MiKlip system (Germany's initiative for decadal prediction).

Mitigating the Impacts of Climate Nonstationarity on Seasonal Streamflow Predictability in the U.S. Southwest

NASA Astrophysics Data System (ADS)

Lehner, Flavio; Wood, Andrew W.; Llewellyn, Dagmar; Blatchford, Douglas B.; Goodbody, Angus G.; Pappenberger, Florian

2017-12-01

Seasonal streamflow predictions provide a critical management tool for water managers in the American Southwest. In recent decades, persistent prediction errors for spring and summer runoff volumes have been observed in a number of watersheds in the American Southwest. While mostly driven by decadal precipitation trends, these errors also relate to the influence of increasing temperature on streamflow in these basins. Here we show that incorporating seasonal temperature forecasts from operational global climate prediction models into streamflow forecasting models adds prediction skill for watersheds in the headwaters of the Colorado and Rio Grande River basins. Current dynamical seasonal temperature forecasts now show sufficient skill to reduce streamflow forecast errors in snowmelt-driven regions. Such predictions can increase the resilience of streamflow forecasting and water management systems in the face of continuing warming as well as decadal-scale temperature variability and thus help to mitigate the impacts of climate nonstationarity on streamflow predictability.

Decadal climate prediction with a refined anomaly initialisation approach

NASA Astrophysics Data System (ADS)

Volpi, Danila; Guemas, Virginie; Doblas-Reyes, Francisco J.; Hawkins, Ed; Nichols, Nancy K.

2017-03-01

In decadal prediction, the objective is to exploit both the sources of predictability from the external radiative forcings and from the internal variability to provide the best possible climate information for the next decade. Predicting the climate system internal variability relies on initialising the climate model from observational estimates. We present a refined method of anomaly initialisation (AI) applied to the ocean and sea ice components of the global climate forecast model EC-Earth, with the following key innovations: (1) the use of a weight applied to the observed anomalies, in order to avoid the risk of introducing anomalies recorded in the observed climate, whose amplitude does not fit in the range of the internal variability generated by the model; (2) the AI of the ocean density, instead of calculating it from the anomaly initialised state of temperature and salinity. An experiment initialised with this refined AI method has been compared with a full field and standard AI experiment. Results show that the use of such refinements enhances the surface temperature skill over part of the North and South Atlantic, part of the South Pacific and the Mediterranean Sea for the first forecast year. However, part of such improvement is lost in the following forecast years. For the tropical Pacific surface temperature, the full field initialised experiment performs the best. The prediction of the Arctic sea-ice volume is improved by the refined AI method for the first three forecast years and the skill of the Atlantic multidecadal oscillation is significantly increased compared to a non-initialised forecast, along the whole forecast time.

Vector-borne diseases and climate change: a European perspective

PubMed Central

Suk, Jonathan E

2017-01-01

Abstract Climate change has already impacted the transmission of a wide range of vector-borne diseases in Europe, and it will continue to do so in the coming decades. Climate change has been implicated in the observed shift of ticks to elevated altitudes and latitudes, notably including the Ixodes ricinus tick species that is a vector for Lyme borreliosis and tick-borne encephalitis. Climate change is also thought to have been a factor in the expansion of other important disease vectors in Europe: Aedes albopictus (the Asian tiger mosquito), which transmits diseases such as Zika, dengue and chikungunya, and Phlebotomus sandfly species, which transmits diseases including Leishmaniasis. In addition, highly elevated temperatures in the summer of 2010 have been associated with an epidemic of West Nile Fever in Southeast Europe and subsequent outbreaks have been linked to summer temperature anomalies. Future climate-sensitive health impacts are challenging to project quantitatively, in part due to the intricate interplay between non-climatic and climatic drivers, weather-sensitive pathogens and climate-change adaptation. Moreover, globalisation and international air travel contribute to pathogen and vector dispersion internationally. Nevertheless, monitoring forecasts of meteorological conditions can help detect epidemic precursors of vector-borne disease outbreaks and serve as early warning systems for risk reduction. PMID:29149298

Vector-borne diseases and climate change: a European perspective.

PubMed

Semenza, Jan C; Suk, Jonathan E

2018-02-01

Climate change has already impacted the transmission of a wide range of vector-borne diseases in Europe, and it will continue to do so in the coming decades. Climate change has been implicated in the observed shift of ticks to elevated altitudes and latitudes, notably including the Ixodes ricinus tick species that is a vector for Lyme borreliosis and tick-borne encephalitis. Climate change is also thought to have been a factor in the expansion of other important disease vectors in Europe: Aedes albopictus (the Asian tiger mosquito), which transmits diseases such as Zika, dengue and chikungunya, and Phlebotomus sandfly species, which transmits diseases including Leishmaniasis. In addition, highly elevated temperatures in the summer of 2010 have been associated with an epidemic of West Nile Fever in Southeast Europe and subsequent outbreaks have been linked to summer temperature anomalies. Future climate-sensitive health impacts are challenging to project quantitatively, in part due to the intricate interplay between non-climatic and climatic drivers, weather-sensitive pathogens and climate-change adaptation. Moreover, globalisation and international air travel contribute to pathogen and vector dispersion internationally. Nevertheless, monitoring forecasts of meteorological conditions can help detect epidemic precursors of vector-borne disease outbreaks and serve as early warning systems for risk reduction. © FEMS 2017.

Temperature variability is a key component in accurately forecasting the effects of climate change on pest phenology.

PubMed

Merrill, Scott C; Peairs, Frank B

2017-02-01

Models describing the effects of climate change on arthropod pest ecology are needed to help mitigate and adapt to forthcoming changes. Challenges arise because climate data are at resolutions that do not readily synchronize with arthropod biology. Here we explain how multiple sources of climate and weather data can be synthesized to quantify the effects of climate change on pest phenology. Predictions of phenological events differ substantially between models that incorporate scale-appropriate temperature variability and models that do not. As an illustrative example, we predicted adult emergence of a pest of sunflower, the sunflower stem weevil Cylindrocopturus adspersus (LeConte). Predictions of the timing of phenological events differed by an average of 11 days between models with different temperature variability inputs. Moreover, as temperature variability increases, developmental rates accelerate. Our work details a phenological modeling approach intended to help develop tools to plan for and mitigate the effects of climate change. Results show that selection of scale-appropriate temperature data is of more importance than selecting a climate change emission scenario. Predictions derived without appropriate temperature variability inputs will likely result in substantial phenological event miscalculations. Additionally, results suggest that increased temperature instability will lead to accelerated pest development. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Farmers' perceptions of and adaptation strategies to climate change and their determinants: the case of Punjab province, Pakistan

NASA Astrophysics Data System (ADS)

Abid, M.; Scheffran, J.; Schneider, U. A.; Ashfaq, M.

2015-05-01

Climate change is a global environmental threat to all economic sectors, particularly the agricultural sector. Pakistan is one of the countries negatively affected by climate change due to its high exposure to extreme events and low adaptive capacity. In Pakistan, farmers are the primary stakeholders in agriculture and are more at risk due to climate vulnerability. Based on farm household data from 450 households collected from three districts in three agroecological zones in the Punjab province of Pakistan, this study examines how farmers perceive climate change and how they adapt their farming in response to perceived changes in climate. The results demonstrate that awareness of climate change is widespread throughout the area, and farm households make adjustments to adapt their agriculture in response to climatic change. Overall 58% of the farm households adapted their farming to climate change. Changing crop varieties, changing planting dates, planting of shade trees and changing fertilizers were the main adaptation methods implemented by farm households in the study area. The results from the binary logistic model reveal that education, farm experience, household size, land area, tenancy status, ownership of a tube well, access to market information, information on weather forecasting and agricultural extension services all influence farmers' choices of adaptation measures. The results also indicate that adaptation to climate change is constrained by several factors such as lack of information, lack of money, resource constraints and shortage of irrigation water in the study area. Findings of the study suggest the need for greater investment in farmer education and improved institutional setup for climate change adaptation to improve farmers' wellbeing.