scholarly journals Assessing Maize and Peanut Yield Simulations with Various Seasonal Climate Data in the Southeastern United States

2010 ◽  
Vol 49 (4) ◽  
pp. 592-603 ◽  
Author(s):  
D. W. Shin ◽  
G. A. Baigorria ◽  
Y-K. Lim ◽  
S. Cocke ◽  
T. E. LaRow ◽  
...  
Keyword(s):  
United States ◽  
Crop Yield ◽  
Climate Model ◽  
Southeastern United States ◽  
Regional Model ◽  
Climate Data ◽  
Model Data ◽  
Global Models ◽  
Seasonal Climate ◽  
Dry Spell

Abstract A comprehensive evaluation of crop yield simulations with various seasonal climate data is performed to improve the current practice of crop yield projections. The El Niño–Southern Oscillation (ENSO)-based historical data are commonly used to predict the upcoming season crop yields over the southeastern United States. In this study, eight different seasonal climate datasets are generated using the combinations of two global models, a regional model, and a statistical downscaling technique. One of the global models and the regional model are run with two different convective schemes. These datasets are linked to maize and peanut dynamic models to assess their impacts on crop yield simulations and are then compared with the ENSO-based approach. Improvement of crop yield simulations with the climate model data is varying, depending on the model configuration and the crop type. Although using the global climate model data directly provides no improvement, the dynamically and statistically downscaled data show increased skill in the crop yield simulations. A statistically downscaled operational seasonal climate model forecast shows statistically significant (at the 5% level) interannual predictability in the peanut yield simulation. Since the yield amount simulated by the dynamical crop model is highly sensitive to wet/dry spell sequences (water stress) during the growing season, fidelity in simulating the precipitation variability is essential.

scholarly journals Seasonal Hydrological Forecasts for Watersheds over the Southeastern United States for the Boreal Summer and Fall Seasons

2013 ◽  
Vol 17 (25) ◽  
pp. 1-22 ◽  
Author(s):  
Satish Bastola ◽  
Vasubandhu Misra ◽  
Haiqin Li
Keyword(s):  
United States ◽  
South Carolina ◽  
Climate Model ◽  
Southeastern United States ◽  
Boreal Summer ◽  
Hydrological Models ◽  
State University ◽  
Streamflow Prediction ◽  
Seasonal Climate ◽  
Hydrological Prediction

Abstract The authors 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 Hindcasts at 50-km resolution (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 analogs 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, the authors 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. The authors 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.

scholarly journals What is the importance of climate model bias when projecting the impacts of climate change on land surface processes?

2014 ◽  
Vol 11 (10) ◽  
pp. 2601-2622 ◽  
Author(s):  
M. Liu ◽  
K. Rajagopalan ◽  
S. H. Chung ◽  
X. Jiang ◽  
J. Harrison ◽  
...  
Keyword(s):  
Crop Yield ◽  
Bias Correction ◽  
Land Surface ◽  
Climate Model ◽  
Biogenic Emissions ◽  
Climate Data ◽  
Post Processing ◽  
Trade Offs ◽  
Impact Models ◽  
No Emissions

Abstract. Regional climate change impact (CCI) studies have widely involved downscaling and bias correcting (BC) global climate model (GCM)-projected climate for driving land surface models. However, BC may cause uncertainties in projecting hydrologic and biogeochemical responses to future climate due to the impaired spatiotemporal covariance of climate variables and a breakdown of physical conservation principles. Here we quantify the impact of BC on simulated climate-driven changes in water variables (evapotranspiration (ET), runoff, snow water equivalent (SWE), and water demand for irrigation), crop yield, biogenic volatile organic compounds (BVOC), nitric oxide (NO) emissions, and dissolved inorganic nitrogen (DIN) export over the Pacific Northwest (PNW) region. We also quantify the impacts on net primary production (NPP) over a small watershed in the region (HJ-Andrews). Simulation results from the coupled ECHAM5–MPI-OM model with A1B emission scenario were first dynamically downscaled to 12 km resolution with the WRF model. Then a quantile-mapping-based statistical downscaling model was used to downscale them into 1/16° resolution daily climate data over historical and future periods. Two climate data series were generated, with bias correction (BC) and without bias correction (NBC). Impact models were then applied to estimate hydrologic and biogeochemical responses to both BC and NBC meteorological data sets. These impact models include a macroscale hydrologic model (VIC), a coupled cropping system model (VIC-CropSyst), an ecohydrological model (RHESSys), a biogenic emissions model (MEGAN), and a nutrient export model (Global-NEWS). Results demonstrate that the BC and NBC climate data provide consistent estimates of the climate-driven changes in water fluxes (ET, runoff, and water demand), VOCs (isoprene and monoterpenes) and NO emissions, mean crop yield, and river DIN export over the PNW domain. However, significant differences rise from projected SWE, crop yield from dry lands, and HJ-Andrews's ET between BC and NBC data. Even though BC post-processing has no significant impacts on most of the studied variables when taking PNW as a whole, their effects have large spatial variations and some local areas are substantially influenced. In addition, there are months during which BC and NBC post-processing produces significant differences in projected changes, such as summer runoff. Factor-controlled simulations indicate that BC post-processing of precipitation and temperature both substantially contribute to these differences at regional scales. We conclude that there are trade-offs between using BC climate data for offline CCI studies versus directly modeled climate data. These trade-offs should be considered when designing integrated modeling frameworks for specific applications; for example, BC may be more important when considering impacts on reservoir operations in mountainous watersheds than when investigating impacts on biogenic emissions and air quality, for which VOCs are a primary indicator.

scholarly journals Assessing Predictability of Cotton Yields in the Southeastern United States Based on Regional Atmospheric Circulation and Surface Temperatures

2008 ◽  
Vol 47 (1) ◽  
pp. 76-91 ◽  
Author(s):  
Guillermo A. Baigorria ◽  
James W. Hansen ◽  
Neil Ward ◽  
James W. Jones ◽  
James J. O’Brien
Keyword(s):  
United States ◽  
Atmospheric Circulation ◽  
Climate Models ◽  
Southeastern United States ◽  
Circulation Model ◽  
Temperature Fields ◽  
Climate Data ◽  
Global Circulation Model ◽  
Surface Temperatures ◽  
Skill Scores

Abstract The potential to predict cotton yields up to one month before planting in the southeastern United States is assessed in this research. To do this, regional atmospheric variables that are related to historic summer rainfall and cotton yields were identified. The use of simulations of those variables from a global circulation model (GCM) for estimating cotton yields was evaluated. The authors analyzed detrended cotton yields (1970–2004) from 48 counties in Alabama and Georgia, monthly rainfall from 53 weather stations, monthly reanalysis data of 850- and 200-hPa winds and surface temperatures over the southeast U.S. region, and monthly predictions of the same variables from the ECHAM 4.5 GCM. Using the reanalysis climate data, it was found that meridional wind fields and surface temperatures around the Southeast were significantly correlated with county cotton yields (explaining up to 52% of the interannual variability of observed yields), and with rainfall over most of the region, especially during April and July. The tendency for cotton yields to be lower during years with atmospheric circulation patterns that favor higher humidity and rainfall is consistent with increased incidence of disease in cotton during flowering and harvest periods under wet conditions. Cross-validated yield estimations based on ECHAM retrospective simulations of wind and temperature fields forced by observed SSTs showed significant predictability skill (up to 55% and 60% hit skill scores based on terciles and averages, respectively). It is concluded that there is potential to predict cotton yields in the Southeast by using variables that are forecast by numerical climate models.

scholarly journals Is the future already here? The impact of climate change on the distribution of the eastern coral snake (Micrurus fulvius)

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4647 ◽  
Author(s):  
Jennifer N. Archis ◽  
Christopher Akcali ◽  
Bryan L. Stuart ◽  
David Kikuchi ◽  
Amanda J. Chunco
Keyword(s):  
Climate Change ◽  
United States ◽  
Species Distribution ◽  
Southeastern United States ◽  
Species Distribution Modeling ◽  
Future Climate ◽  
Range Shifts ◽  
Climate Data ◽  
Coral Snake ◽  
Distribution Modeling

Anthropogenic climate change is a significant global driver of species distribution change. Although many species have undergone range expansion at their poleward limits, data on several taxonomic groups are still lacking. A common method for studying range shifts is using species distribution models to evaluate current, and predict future, distributions. Notably, many sources of ‘current’ climate data used in species distribution modeling use the years 1950–2000 to calculate climatic averages. However, this does not account for recent (post 2000) climate change. This study examines the influence of climate change on the eastern coral snake (Micrurus fulvius). Specifically, we: (1) identified the current range and suitable environment of M. fulvius in the Southeastern United States, (2) investigated the potential impacts of climate change on the distribution of M. fulvius, and (3) evaluated the utility of future models in predicting recent (2001–2015) records. We used the species distribution modeling program Maxent and compared both current (1950–2000) and future (2050) climate conditions. Future climate models showed a shift in the distribution of suitable habitat across a significant portion of the range; however, results also suggest that much of the Southeastern United States will be outside the range of current conditions, suggesting that there may be no-analog environments in the future. Most strikingly, future models were more effective than the current models at predicting recent records, suggesting that range shifts may already be occurring. These results have implications for both M. fulvius and its Batesian mimics. More broadly, we recommend future Maxent studies consider using future climate data along with current data to better estimate the current distribution.

scholarly journals Warm Season Dry Spells in the Central and Eastern United States: Diverging Skill in Climate Model Representation

2016 ◽  
Vol 29 (15) ◽  
pp. 5617-5624 ◽  
Author(s):  
Siyu Zhao ◽  
Yi Deng ◽  
Robert X. Black
Keyword(s):  
United States ◽  
Large Scale ◽  
Climate Model ◽  
Warm Season ◽  
Hierarchical Cluster ◽  
Model Representation ◽  
Cmip5 Models ◽  
Eastern United States ◽  
Dry Spells ◽  
Dry Spell

Abstract Warm season dry spells over the central and eastern United States are classified into three canonical types via a hierarchical cluster analysis for the period 1950–2005. Four CMIP5 models exhibit diverging skill in representing the observed behavior, ranging from southern Great Plains dry spells that are reasonably simulated by all four models to southeastern U.S. dry spells that are only accurately captured by one model. A model’s skill in representing a particular dry spell cluster is positively correlated with the model’s ability to simulate the large-scale meteorological patterns (LMPs) accompanying the dry spell. The interannual variability and overall observed decreasing trend in dry spell days are represented with varying degrees of accuracy by the four models. The results 1) highlight existing shortcomings in the climate model representation of regional dry spells and 2) illustrate the importance of properly simulating the observed spectrum of LMPs in minimizing these shortcomings.

scholarly journals Assessment of Dynamic Downscaling of the Continental U.S. Regional Climate Using the Eta/SSiB Regional Climate Model

2007 ◽  
Vol 20 (16) ◽  
pp. 4172-4193 ◽  
Author(s):  
Yongkang Xue ◽  
Ratko Vasic ◽  
Zavisa Janjic ◽  
Fedor Mesinger ◽  
Kenneth E. Mitchell
Keyword(s):  
United States ◽  
Boundary Conditions ◽  
Regional Climate Model ◽  
North American ◽  
Climate Model ◽  
Spectral Power ◽  
Regional Climate ◽  
Southeastern United States ◽  
Dynamic Downscaling ◽  
Southern Boundary

Abstract This study investigates the capability of the dynamic downscaling method (DDM) in a North American regional climate study using the Eta/Simplified Simple Biosphere (SSiB) Regional Climate Model (RCM). The main objective is to understand whether the Eta/SSiB RCM is capable of simulating North American regional climate features, mainly precipitation, at different scales under imposed boundary conditions. The summer of 1998 was selected for this study and the summers of 1993 and 1995 were used to confirm the 1998 results. The observed precipitation, NCEP–NCAR Global Reanalysis (NNGR), and North American Regional Reanalysis (NARR) were used for evaluation of the model’s simulations and/or as lateral boundary conditions (LBCs). A spectral analysis was applied to quantitatively examine the RCM’s downscaling ability at different scales. The simulations indicated that choice of domain size, LBCs, and grid spacing were crucial for the DDM. Several tests with different domain sizes indicated that the model in the North American climate simulation was particularly sensitive to its southern boundary position because of the importance of moisture transport by the southerly low-level jet (LLJ) in summer precipitation. Among these tests, only the RCM with 32-km resolution and NNGR LBC or with 80-km resolution and NARR LBC, in conjunction with appropriate domain sizes, was able to properly simulate precipitation and other atmospheric variables—especially humidity over the southeastern United States—during all three summer months—and produce a better spectral power distribution than that associated with the imposed LBC (for the 32-km case) and retain spectral power for large wavelengths (for the 80-km case). The analysis suggests that there might be strong atmospheric components of high-frequency variability over the Gulf of Mexico and the southeastern United States.

scholarly journals Drought in the Southeastern United States: Causes, Variability over the Last Millennium, and the Potential for Future Hydroclimate Change*

2009 ◽  
Vol 22 (19) ◽  
pp. 5021-5045 ◽  
Author(s):  
Richard Seager ◽  
Alexandrina Tzanova ◽  
Jennifer Nakamura
Keyword(s):  
Climate Change ◽  
United States ◽  
Tree Ring ◽  
Climate Model ◽  
Southeastern United States ◽  
Precipitation Variability ◽  
Historical Record ◽  
Last Millennium ◽  
Intergovernmental Panel ◽  
Model Simulations

Abstract An assessment of the nature and causes of drought in the southeastern United States is conducted as well as an assessment of model projections of anthropogenically forced hydroclimate change in this region. The study uses observations of precipitation, model simulations forced by historical SSTs from 1856 to 2007, tree-ring records of moisture availability over the last millennium, and climate change projections conducted for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. From the perspective of the historical record, the recent drought that began in winter 2005/06 was a typical event in terms of amplitude and duration. Observations and model simulations are used to show that dry winter half-years in the Southeast are weakly associated with La Niñas in the tropical Pacific but that this link varies over time and was possibly of opposite sign from about 1922 to 1950. Summer-season precipitation variability in the Southeast appears governed by purely internal atmospheric variability. As such, model simulations forced by historical SSTs have very limited skill in reproducing the instrumental record of Southeast precipitation variability and actual predictive skill is also presumably low. Tree-ring records show that the twentieth century has been moist from the perspective of the last millennium and free of long and severe droughts that were abundant in previous centuries. The tree-ring records show a 21-yr-long uninterrupted drought in the mid-sixteenth century, a long period of dry conditions in the early to mid-nineteenth century, and that the Southeast was also affected by some of the medieval megadroughts centered in western North America. Climate model projections predict that in the near term, future precipitation in the Southeast will increase but that evaporation will also increase. The median of the projections predicts a modest reduction in the atmospheric supply of water vapor to the region; however, the multimodel ensemble exhibits considerable variation, with a quarter to a third of the models projecting an increase in precipitation minus evaporation. The recent drought, forced by reduced precipitation and with reduced evaporation, has no signature of model-projected anthropogenic climate change.

scholarly journals Forecasting extreme events: making sense of noisy climate data in support of water resources planning

2019 ◽  
Vol 2 (1) ◽  
pp. 45-57
Author(s):  
Timothy Cox ◽  
Jenny Bywater ◽  
Mitchell Heineman ◽  
Dan Rodrigo ◽  
Shayne Wood
Keyword(s):  
Water Resources ◽  
Climate Model ◽  
Extreme Event ◽  
Global Climate Model ◽  
Drought Severity ◽  
Water Resources Planning ◽  
Storm Events ◽  
Climate Data ◽  
Model Data ◽  
Resources Planning

Abstract Global climate model (GCM) projections are generally considered the best source of information for predicting future climate and hydrologic conditions in the face of a changing climate. Understanding and interpreting GCM projections is therefore critical for water resources planning. Unfortunately, this can be a challenging task as climate model data, particularly precipitation data, are notoriously noisy with large scatter and lacking in apparent patterns or trends. There is also usually large projection variability between models and model scenarios. This paper demonstrates a simple, practical method for synthesizing climate model data into more informative metrics using case studies of Atlanta, Georgia and Austin, Texas. Monthly and daily GCM projections, as well as historical observations, were translated into commonly used summary metrics for extreme event planning: peak 24-hour storm events and the Palmer Drought Severity Index (PDSI). Statistical trend analyses on these two metrics were used as a simple means to better understand the data. As expected, results identified significant, increasing, trends in projected 21st century temperatures for most GCM projections. Less expectedly, significant trends were also identified for projected future monthly and 24-hour maximum precipitation and drought severity. Implications of this work for water resources planning are discussed.

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