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Publications

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Bastola, S., Misra, V., & Li, H. (2013). Seasonal hydrological forecasts for watersheds over the Southeastern United States for boreal summer and fall seasons. Earth Interact., 17, 25.
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Abstract: We evaluate the skill of a suite of seasonal hydrological prediction experiments over 28 watersheds throughout the Southeastern United States (SEUS), including Florida, Georgia, Alabama, South Carolina, and North Carolina. The seasonal climate retrospective forecasts (the Florida Climate Institute&#65533;Florida State University Seasonal Hindcast at 50 km [FISH50]) is initialized in June and integrated through November of each year from 1982 through 2001. Each seasonal climate forecast has six ensemble members. An earlier study showed that FISH50 represents state-of-the-art seasonal climate prediction skill for the summer and fall seasons, especially in the subtropical and higher latitudes. The retrospective prediction of streamflow is based on multiple calibrated rainfall-runoff models. The hydrological models are forced with rainfall from FISH50, (quantile-based) bias-corrected FISH50 rainfall (FISH50_BC), and resampled historical rainfall observations based on matching observed analogues of forecasted quartile seasonal rainfall anomalies (FISH50_Resamp). The results show that direct use of output from the climate model (FISH50) results in huge biases in predicted streamflow, which is significantly reduced with bias correction (FISH50_BC) or by FISH50_Resamp. On a discouraging note, we find that the deterministic skill of retrospective streamflow prediction as measured by the normalized root mean square error is poor compared to the climatological forecast irrespective of how FISH50 (e.g., FISH50_BC, FISH50_Resamp) is used to force the hydrological models. However, our analysis of probabilistic skill from the same suite of retrospective prediction experiments reveals that over the majority of the 28 watersheds in the SEUS, significantly higher probabilistic skill than climatological forecast of streamflow can be harvested for the wet/dry seasonal anomalies (i.e., extreme quartiles) using FISH50_Resamp as the forcing. We contend that given the nature of the relatively low climate predictability over the SEUS, high deterministic hydrological prediction skills will be elusive. Therefore, probabilistic hydrological prediction for the SEUS watersheds is very appealing, especially with the current capability of generating a comparatively huge ensemble of seasonal hydrological predictions for each watershed and for each season, which offers a robust estimate of associated forecast uncertainty.
Keywords: Seasonal climate forecast; Ensemble streamflow prediction; Rainfall-runoff model
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Bell, R. J., Gray, S. L., & Jones, O. P. (2017). North Atlantic storm driving of extreme wave heights in the North Sea. J. Geophys. Res. Oceans, 122(4), 3253–3268.
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Keywords: extreme ocean waves; North Sea; extratropical cyclones; wave climatology; prediction; warning
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Clarke, A. J. (2014). El Nino Physics and El Nino Predictability. Annu. Rev. Marine. Sci., 6(1), 79–99.
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Abstract: Much of the year-to-year climate variability on the Earth is associated with El Nino and the Southern Oscillation (ENSO). This variability is generated ˜ primarily by a coupled ocean-atmosphere instability near the eastern edge of the western equatorial Paci&#64257;c warm pool. Here, I discuss the physics of this variability, including its phase locking to the seasonal cycle. ENSO growth typically occurs from April/May to November, and by July the perturbation is usually strong enough that it persists to the beginning of the following year, when ENSO events usually end. Consequently, predicting ENSO is easy from July to February but is more challenging across the April/May transition to the next event. I discuss precursors of this transition and recent results from dynamical and statistical models used for ENSO forecasting.
Keywords: ENSO engine, ENSO predictions, Southern Oscillation
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Dietze, M. C., Fox, A., Beck-Johnson, L. M., Betancourt, J. L., Hooten, M. B., Jarnevich, C. S., et al. (2018). Iterative near-term ecological forecasting: Needs, opportunities, and challenges. Proc Natl Acad Sci USA, 115(7), 1424–1432.
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Keywords: forecast; ecology; prediction
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Elliott, J., Glotter, M., Ruane, A. C., Boote, K. J., Hatfield, J. L., Jones, J. W., et al. (2018). Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management. Agricultural Systems, 159, 275–281.
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Keywords: Climate extremes; Drought impacts; Agriculture; Seasonal prediction; Adaptation
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Elsner, J. B., & Widen, H. M. (2013). Predicting Spring Tornado Activity in the Central Great Plains By March 1st. Mon. Wea. Rev., 142(1), 259–267.
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Abstract: The authors illustrate a statistical model for predicting tornado activity in the central Great Plains by 1 March. The model predicts the number of tornado reports during April-June using February sea surface temperature (SST) data from the Gulf of Alaska (GAK) and the western Caribbean Sea (WCA). The model uses a Bayesian formulation where the likelihood on the counts is a negative binomial distribution and where the nonstationarity in tornado reporting is included as a trend term plus first-order autocorrelation. Posterior densities for the model parameters are generated using the method of integrated nested Laplacian approximation (INLA). The model yields a 51% increase in the number of tornado reports per degree Celsius increase in SST over the WCA and a 15% decrease in the number of reports per degree Celsius increase in SST over the GAK. These significant relationships are broadly consistent with a physical understanding of large-scale atmospheric patterns conducive to severe convective storms across the Great Plains. The SST covariates explain 11% of the out-of-sample variability in observed F1-F5 tornado reports. The paper demonstrates the utility of INLA for fitting Bayesian models to tornado climate data.
Keywords: Tornadoes; Bayesian methods; Climate prediction; Seasonal forecasting; Statistical forecasting
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Fournier-Level, A., Perry, E. O., Wang, J. A., Braun, P. T., Migneault, A., Cooper, M. D., et al. (2016). Predicting the evolutionary dynamics of seasonal adaptation to novel climates in_Arabidopsis thaliana_. Proc Natl Acad Sci USA, 113(20), E2812–E2821.
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Abstract: Predicting whether and how populations will adapt to rapid climate change is a critical goal for evolutionary biology. To examine the genetic basis of fitness and predict adaptive evolution in novel climates with seasonal variation, we grew a diverse panel of the annual plant Arabidopsis thaliana (multiparent advanced generation intercross lines) in controlled conditions simulating four climates: a present-day reference climate, an increased-temperature climate, a winter-warming only climate, and a poleward-migration climate with increased photoperiod amplitude. In each climate, four successive seasonal cohorts experienced dynamic daily temperature and photoperiod variation over a year. We measured 12 traits and developed a genomic prediction model for fitness evolution in each seasonal environment. This model was used to simulate evolutionary trajectories of the base population over 50 y in each climate, as well as 100-y scenarios of gradual climate change following adaptation to a reference climate. Patterns of plastic and evolutionary fitness response varied across seasons and climates. The increased-temperature climate promoted genetic divergence of subpopulations across seasons, whereas in the winter-warming and poleward-migration climates, seasonal genetic differentiation was reduced. In silico "resurrection experiments" showed limited evolutionary rescue compared with the plastic response of fitness to seasonal climate change. The genetic basis of adaptation and, consequently, the dynamics of evolutionary change differed qualitatively among scenarios. Populations with fewer founding genotypes and populations with genetic diversity reduced by prior selection adapted less well to novel conditions, demonstrating that adaptation to rapid climate change requires the maintenance of sufficient standing variation.
Keywords: climate change; annual plant; genomic prediction; season
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Hong, S. - Y., Park, H., Cheong, H. - B., Kim, J. - E. E., Koo, M. - S., Jang, J., et al. (2013). The Global/Regional Integrated Model system (GRIMs). Asia-Pacific J Atmos Sci, 49(2), 219–243.
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Abstract: A multiscale atmospheric/oceanic model system with unified physics, the Global/Regional Integrated Model system (GRIMs) has been created for use in numerical weather prediction, seasonal simulations, and climate research projects, from global to regional scales. It includes not only the model code, but also the test cases and scripts. The model system is developed and practiced by taking advantage of both operational and research applications. This article outlines the history of GRIMs, its current applications, and plans for future development, providing a summary useful to present and future users.
Keywords: Numerical weather prediction; seasonal prediction; general circulation model; regional climate modeling; physics parameterization; climate modeling; GRIMs; WRF
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Infanti, J. M., & Kirtman, B. P. (2016). North American rainfall and temperature prediction response to the diversity of ENSO. Clim Dyn, 46(9-10), 3007–3023.
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Abstract: Research has shown that there is significant diversity in the location of the maximum sea surface temperature anomaly (SSTA) associated with the El Nio Southern Oscillation (ENSO). In one extreme, warm SSTA peak near the South American coast (often referred to as Eastern Pacific of EP El Nio), and at the other extreme, warm SSTA peak in the central Pacific (Central Pacific or CP El Nio). Due to the differing tropical Pacific SSTA and precipitation structure, there are differing extratropical responses, particularly over North America. Recent work involving the North American Multi-Model Ensemble (NMME) system for intra-seasonal to inter-annual prediction on prediction of the differences between El Nio events found excess warming in the eastern Pacific during CP El Nio events. This manuscript investigates the ensemble and observational agreement of the NMME system when forecasting the North American response to the diversity of ENSO, focusing on regional land-based 2-meter temperature and precipitation. NMME forecasts of North American precipitation and T2m agree with observations more often during EP events. Ensemble agreement of NMME forecasts is regional. For instance, ensemble agreement in Southeast North America demonstrates a strong connection to NINO3 precipitation and SSTA amplitude during warm ENSO events. Ensemble agreement in Northwest North America demonstrates a weak connection to NINO4 precipitation and SSTA amplitude during warm ENSO events. Still other regions do not show a strong connection between ensemble agreement and strength of warm ENSO events.
Keywords: ENSO diversity; El Nino; Teleconnections; North America; Climate prediction
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Infanti, J. M., & Kirtman, B. P. (2019). A comparison of CCSM4 high-resolution and low-resolution predictions for south Florida and southeast United States drought. Clim Dyn, 52(11), 6877–6892.
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Abstract: It is important to have confidence in seasonal climate predictions of precipitation, particularly related to drought, as implications can be far-reaching and costlythis is particularly true for Florida. Precipitation can vary on fine spatial resolutions, and high-resolution coupled models may be needed to correctly represent precipitation variability. We study south Florida and southeast United States drought in Community Climate System version 4 low resolution (1 degrees atmosphere/ocean) and high-resolution (0.5 degrees atmosphere/0.1 degrees ocean) predictions for time means ranging from 3 to 36months. The very high-resolution in the ocean is of interest here given the potential importance of Gulf Stream on south Florida rainfall. Skill of shorter time-mean South Florida predictions (i.e. 3- and 12-months) are not impacted by increased resolution, but skill of 36-month mean south Florida precipitation is somewhat increased in the high resolution predictions. Notably, over the broader southeast United States the high-resolution model has higher skill for the 36-month mean rainfall predictions, associated with an improved relationship with tropical Pacific and Gulf Stream SSTA. Why this improvement in the broader southeast United States does not extend to Florida is an open question, but does suggest that even further resolution refinements may be needed.
Keywords: Climate; Prediction; CCSM4; Rainfall; Florida; Southeast US
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Kirtman, B. P., Misra, V., Anandhi, A., Palko, D., & Infanti, J. (2017). Future climate change scenarios for Florida. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.), Florida's climate: Changes, variations, & impacts (pp. 533–555). Gainesville, FL: Florida Climate Institute.
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Abstract: This chapter describes both the nature of and anthropogenic mechanisms for climate change, as well as how scenarios and projections of future climate change are made. Specific emphasis is placed on understanding the changes over the near-term (i.e., adaption timescale) where the emission scenario has little impact vs. changes beyond the mid-century where the projections are conditional on the emission scenario. The various tools and models used to assess climate change are also summarized, and projections from global and regional models are presented. Finally, the new science of decadal prediction is presented as it has the potential to improve climate information in the near-term.
Keywords: Anthropogenically forced climate change; Decadal climate prediction; Climate projection; Climate scenario; Mitigation; Adaptation
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Kirtman, B. P., Misra, V., Burgman, R. J., Infanti, J., & Obeysekera, J. (2017). Florida climate variability and prediction. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.), Florida's climate: Changes, variations, & impacts (pp. 511–532). Gainesville, FL: Florida Climate Institute.
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Abstract: This chapter describes the sources and mechanisms for climate variability in Florida across timescales (i.e., seasonal-to-decadal) and how they are used to make predictions. Current capabilities in terms of prediction quality, with an emphasis on precipitation and land surface temperature on seasonal timescales, are introduced as well as challenges and opportunities for the future. The longer decadal time scales are discussed in the next chapter in conjunction with climate change associated with anthropogenic forcing.
Keywords: Multi-model ensembles; Regional climate prediction; Dynamical downscaling; Statistical downscaling
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Kren,, Cucurull,, & Wang,. (2018). Impact of UAS Global Hawk Dropsonde Data on Tropical and Extratropical Cyclone Forecasts in 2016. Wea. Forecasting, 33(5), 1121–1141.
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Abstract: A preliminary investigation into the impact of dropsonde observations from the Global Hawk (GH) on tropical and extratropical forecasts is performed using the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS). Experiments are performed during high-impact weather events that were sampled as part of the NOAA Unmanned Aerial Systems (UAS) Sensing Hazards with Operational Unmanned Technology (SHOUT) field campaigns in 2016: 1) three extratropical systems in February 2016 and 2) Hurricanes Matthew and Nicole in the western Atlantic. For these events, the benefits of GH observations under a satellite data gap scenario are also investigated. It is found that the assimilation of GH dropsondes reduces the track error for both Matthew and Nicole; the improvements are as high as 20% beyond 60 h. Additionally, the localized dropsondes reduce global forecast track error for four tropical cyclones by up to 9%. Results are mixed under a satellite gap scenario, where only Hurricane Matthew is improved from assimilated dropsondes. The improved storm track is attributed to a better representation of the steering flow and atmospheric midlevel pattern. For all cases, dropsondes reduce the root-mean-square error in temperature, relative humidity, wind, and sea level pressure by 3%-8% out to 96 h. Additional benefits from GH dropsondes are obtained for precipitation, with higher skill scores over the southeastern United States versus control forecasts of up to 8%, as well as for low-level parameters important for severe weather prediction. The findings from this study are preliminary and, therefore, more cases are needed for statistical significance.
Keywords: Tropical cyclones; Aircraft observations; Forecast verification; skill; Mesoscale forecasting; Numerical weather prediction; forecasting; Short-range prediction
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Onyejekwe, O., Holman, B., & Kachouie, N. N. (2017). Multivariate models for predicting glacier termini. Environ Earth Sci, 76(23).
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Keywords: Climate change; Mountain glaciers; Statistical analysis; Regression; Multivariate models; Correlation; Prediction; Terminus location; Climate factors
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Roncoli, C., Jost, C., Kirshen, P., Sanon, M., Ingram, K. T., Woodin, M., et al. (2009). From accessing to assessing forecasts: an end-to-end study of participatory climate forecast dissemination in Burkina Faso (West Africa). Climatic Change, , 433–460.
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Abstract: This study compares responses to seasonal climate forecasts conducted by farmers of three agro-ecological zones of Burkina Faso, including some who had attended local level workshops and others who had not attended the workshops. While local inequalities and social tensions contributed to excluding some groups, about two-thirds of non-participants interviewed received the forecast from the participants or through various means deployed by the project. Interviews revealed that almost all those who received the forecasts by some mechanism (workshop or other) shared them with others. The data show that participants were more likely to understand the probabilistic aspect of the forecasts and their limitations, to use the information in making management decisions and by a wider range of responses. These differences are shown to be statistically significant. Farmers evaluated the forecasts as accurate and useful in terms of both material and non-material considerations. These findings support the hypothesis that participatory workshops can play a positive role in the provision of effective climate services to African rural producers. However, this role must be assessed in the context of local dynamics of power, which shape information flows and response options. Participation must also be understood beyond single events (such as workshops) and be grounded in sustained interaction and commitments among stakeholders. The conclusion of this study point to lessons learned and critical insights on the role of participation in climate-based decision support systems for rural African communities.
Keywords: Decision-Making Subsistence Farmers Potential Benefits Agriculture Information Prediction Responses Variability Zimbabwe Impacts
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Tian, D., Martinez, C. J., & Graham, W. D. (2014). Seasonal Prediction of Regional Reference Evapotranspiration Based on Climate Forecast System Version 2. J. Hydrometeor, 15(3), 1166–1188.
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Abstract: Reference evapotranspiration (ETo) is an important hydroclimatic variable for water planning and management. This research explored the potential of using the Climate Forecast System, version 2 (CFSv2), for seasonal predictions of ETo over the states of Alabama, Georgia, and Florida. The 12-km ETo forecasts were produced by downscaling coarse-scale ETo forecasts from the CFSv2 retrospective forecast archive and by downscaling CFSv2 maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), solar radiation (Rs), and wind speed (Wind) individually and calculating ETo using those downscaled variables. All the ETo forecasts were calculated using the Penman–Monteith equation. Sensitivity coefficients were evaluated to quantify how and how much does each of the variables influence ETo. Two statistical downscaling methods were tested: 1) spatial disaggregation (SD) and 2) spatial disaggregation with quantile mapping bias correction (SDBC). The downscaled ETo from the coarse-scale ETo showed similar skill to those by first downscaling individual variables and then calculating ETo. The sensitivity coefficients showed Tmax and Rs had the greatest influence on ETo, followed by Tmin and Tmean, and Wind. The downscaled Tmax showed highest predictability, followed by Tmean, Tmin, Rs, and Wind. SDBC had slightly better performance than SD for both probabilistic and deterministic forecasts. The skill was locally and seasonally dependent. The CFSv2-based ETo forecasts showed higher predictability in cold seasons than in warm seasons. The CFSv2 model could better predict ETo in cold seasons during El Niño–Southern Oscillation (ENSO) events only when the forecast initial condition was in either the El Niño or La Niña phase of ENSO.
Keywords: Climate prediction; Evapotranspiration; Hydrologic cycle; Statistical techniques; Ensembles; Seasonal forecasting
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Wallach, D., Hwang, C., Correll, M. J., Jones, J. W., Boote, K., Hoogenboom, G., et al. (2018). A dynamic model with QTL covariables for predicting flowering time of common bean (Phaseolus vulgaris) genotypes. European Journal of Agronomy, 101, 200–209.
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Abstract: Multi-genotype multi-environment trials, associated with characterization of the environment, marker information for the genotypes and measurements of the phenotypic traits of interest can potentially provide the basis for models to predict the behavior of untested genotypes in new environments. However, there is as yet no clear indication of the best form of such models, nor how to parameterize them. The purpose of this study was to propose and test an approach to crop-QTL modeling, applied to prediction of time to flowering in common bean (Phaseolus vulgaris), which avoids the pitfall of estimating separately the parameters for each genotype. The environmental model is a dynamic model with development rates that depend on daily temperature and day length. Three of the model parameters are expressed as linear functions of the QTLs for time to flowering, resulting in a model that combines environmental variables and QTLs. An innovative approach to parameter estimation is proposed, based on least squares, which makes it quite easy to estimate all the parameters of this model simultaneously, using all the data. The parameterized model explains most of the genotypic and environmental variability in the data, and 47% of the genotype by environment (GxE) interaction. Cross validation shows that the model extrapolates well to new genotypes in the same environments as those of the data, and also to new environments if they are similar in terms of temperature and photoperiod to those in the training data.
Keywords: Model; Flowering; Prediction; Common bean; QTL; Multi-Environment trial
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Wallach, D., Thorburn, P., Asseng, S., Challinor, A. J., Ewert, F., Jones, J. W., et al. (2016). Estimating model prediction error: Should you treat predictions as fixed or random? Environmental Modelling & Software, 84, 529–539.
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Abstract: Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEPfixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEPuncertain(X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEPuncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEPuncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.
Keywords: Crop model; Uncertainty; Prediction error; Parameter uncertainty; Input uncertainty; Model structure uncertainty
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Woli, P., Jones, J. W., Ingram, K. T., & Hoogenboom, G. (2014). Predicting Crop Yields with the Agricultural Reference Index for Drought. J Agro Crop Sci, 200(3), 163–171.
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Abstract: A generic agricultural drought index, called Agricultural Reference Index for Drought (ARID), was designed recently to quantify water stress for use in predicting crop yield loss from drought. This study evaluated ARID in terms of its ability to predict crop yields. Daily historical weather data and yields of cotton, maize, peanut and soybean were obtained for several locations and years in the south-eastern USA. Daily values of ARID were computed for each location and converted to monthly average values. Using regression analyses of crop yields vs. monthly ARID values during the crop growing season, ARID-yield relationships were developed for each crop. The ability of ARID to predict yield loss from drought was evaluated using the root mean square error (RMSE), the Willmott index and the modelling efficiency (ME). The ARID-based yield models predicted relative yields with the RMSE values of 0.144, 0.087, 0.089 and 0.142 (kg ha&#8722;1 yield per kg ha&#8722;1 potential yield); the Willmott index values of 0.70, 0.92, 0.86 and 0.79; and the ME values of 0.33, 0.73, 0.60 and 0.49 for cotton, maize, peanut and soybean, respectively. These values indicated that the ARID-based yield models can predict the yield loss from drought for these crops with reasonable accuracy.
Keywords: agricultural reference index for drought; drought index; stress sensitivity; water stress; yield model; yield prediction
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Xu, X., Chassignet, E. P., & Wang, F. (2019). On the variability of the Atlantic meridional overturning circulation transports in coupled CMIP5 simulations. Clim Dyn, 52(11), 6511–6531.
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Abstract: The Atlantic meridional overturning circulation (AMOC) plays a fundamental role in the climate system, and long-term climate simulations are used to understand the AMOC variability and to assess its impact. This study examines the basic characteristics of the AMOC variability in 44 CMIP5 (Phase 5 of the Coupled Model Inter-comparison Project) simulations, using the 18 atmospherically-forced CORE-II (Phase 2 of the Coordinated Ocean-ice Reference Experiment) simulations as a reference. The analysis shows that on interannual and decadal timescales, the AMOC variability in the CMIP5 exhibits a similar magnitude and meridional coherence as in the CORE-II simulations, indicating that the modeled atmospheric variability responsible for AMOC variability in the CMIP5 is in reasonable agreement with the CORE-II forcing. On multidecadal timescales, however, the AMOC variability is weaker by a factor of more than 2 and meridionally less coherent in the CMIP5 than in the CORE-II simulations. The CMIP5 simulations also exhibit a weaker long-term atmospheric variability in the North Atlantic Oscillation (NAO). However, one cannot fully attribute the weaker AMOC variability to the weaker variability in NAO because, unlike the CORE-II simulations, the CMIP5 simulations do not exhibit a robust NAO-AMOC linkage. While the variability of the wintertime heat flux and mixed layer depth in the western subpolar North Atlantic is strongly linked to the AMOC variability, the NAO variability is not.
Keywords: NORTH-ATLANTIC; MULTIDECADAL OSCILLATION; SURFACE-TEMPERATURE; OCEAN; IMPACT; CLIMATE; AMOC; PREDICTION; ANOMALIES
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