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�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.

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 Pacific 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.

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.

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.

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.

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.

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.

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.

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−1 yield per kg ha−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.