scholarly journals Climate Variability and Corn Yields in Semiarid Ceará, Brazil

2007 ◽  
Vol 46 (2) ◽  
pp. 226-240 ◽  
Author(s):  
Liqiang Sun ◽  
Huilan Li ◽  
M. Neil Ward ◽  
David F. Moncunill
Keyword(s):  
General Circulation ◽  
Goodness Of Fit ◽  
Crop Yields ◽  
Circulation Model ◽  
Coefficient Of Determination ◽  
Climate Index ◽  
Corn Yield ◽  
Climate Variables ◽  
Potential Predictability ◽  
Weather Index

Abstract Understanding of climate influence on crop yields can help in the design of policies to reduce climate-related vulnerability in many parts of the world, including the target of this case study—the state of Ceará, Brazil. The study has examined the relationships between climate variations and corn yields and, in addition, has estimated the potential predictability of corn yields in Ceará drawing on the now well-established seasonal predictability of the region’s climate based on prevailing patterns of sea surface temperature (SST), especially in the tropical Atlantic and tropical Pacific Oceans. The relationships between corn yields and climate variables have been explored using observed data for the period of 1952–2001. A linear regression–based corn-yield model was evaluated by comparing the model-simulated yields with the observations using three goodness-of-fit measures: the coefficient of determination, the index of agreement, and the mean absolute error. A comparative performance analysis was carried out on several climate variables to determine the most appropriate climate index for simulating corn yields in Ceará. A weather index was defined to measure the severity of drought and flooding conditions in the growing season for corn. The analysis indicated that the weather index is the best climate parameter for simulating corn yields in Ceará. The observed weather index can explain 56.8% of the variance of the observed corn yields. High potential predictability of the weather index was revealed by the evaluation of an ensemble of 10 runs with the NCEP Regional Spectral Model nested into the ECHAM4.5 atmospheric general circulation model, driven with observed SSTs in each season for the period of 1971–2000. Whereas these runs are based on the actual observed SST pattern in each season, other studies have shown that persistence of SST over several months is sufficient for a true predictive capability. The aim here was to show that the SST-forced component of climate variation does translate into the weather features that are important for crop yields. Indeed, the results demonstrate the striking extent to which the year-to-year changes in SST force local climate characteristics that can specify the year-to-year variations in corn yields. The variance of corn yield explained by the SST-driven model was 49.5%.

scholarly journals Assessing Future Impacts of Climate Change on Streamflow within the Alabama River Basin

Climate ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 55
Author(s):  
Joseph E. Quansah ◽  
Amina B. Naliaka ◽  
Souleymane Fall ◽  
Ramble Ankumah ◽  
Gamal El Afandi
Keyword(s):  
Climate Change ◽  
River Basin ◽  
21St Century ◽  
General Circulation ◽  
Goodness Of Fit ◽  
Circulation Model ◽  
Coefficient Of Determination ◽  
Future Climate Change ◽  
Model Version ◽  
Monthly Streamflow

Global climate change is expected to impact future precipitation and surface temperature trends and could alter local hydrologic systems. This study assessed the likely hydrologic responses and changes in streamflow due to future climate change within the Alabama River Basin (ARB) for the mid-21st century 2045 (“2030–2060”) and end-21st century 2075 (“2060–2090”). Using an integrated modeling approach, General Circulation Model (GCM) datasets; the Centre National de Recherches Météorologiques Climate Model 5 (CNRM-CM5), the Community Earth System Model, version 1–Biogeochemistry (CESM1- BGC.1), and the Hadley Centre Global Environment Model version 2 (HADGEM2-AO.1), under medium Representative Concentration Pathway (RCP) 4.5, and based on World Climate Research Program (WCRP)’s Couple Model Intercomparison Phase 5 (CMIP5), were assimilated into calibrated Soil and Water Assessment Tool (SWAT). Mann–Kendall and Theil Sen’s slope were used to assess the trends and magnitude of variability of the historical climate data used for setting up the model. The model calibration showed goodness of fit with minimum Nash–Sutcliffe Efficiency (NSE) coefficient values of 0.83 and Coefficient of Determination (R2) of 0.88 for the three gages within the ARB. Next, the research assessed changes in streamflow for the years 2045 and 2075 against that of the reference baseline year of 1980. The results indicate situations of likely increase and decrease in mean monthly streamflow discharge and increase in the frequency and variability in peak flows during the periods from the mid to end of the 21st century. Seasonally, monthly streamflow increases between 50% and 250% were found for spring and autumn months with decreases in summer months for 2045. Spring and summer months for 2075 resulted in increased monthly streamflow between 50% and 300%, while autumn and spring months experienced decreased streamflow. While the results are prone to inherent uncertainties in the downscaled GCM data used, the simulated dynamics in streamflow and water availability provide critical information for stakeholders to develop sustainable water management and climate change adaptation options for the ARB.

scholarly journals Mitigating future climate change effects on wheat and soybean yields in Central region of Madhya Pradesh by shifting sowing dates

2021 ◽  
Vol 21 (4) ◽  
pp. 468-473
Author(s):  
Ankit Balvanshi ◽  
H. L. Tiwari
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Crop Yields ◽  
Circulation Model ◽  
Planting Date ◽  
Madhya Pradesh ◽  
Coefficient Of Determination ◽  
Future Climate Change ◽  
Yield Model ◽  
Adaptation Measure

The present work focuses on (1) estimation of future yield of wheat and soybean crop under RCPs scenario 2.6, 4.5 and 8.5 for years 2020, 2050 and 2080 using FAO AquaCrop yield simulating model and (2) assessment of shifting planting date as adaptation measure to mitigate climate change impact for Sehore district, Madhya Pradesh. Statistically downscaled General Circulation Model CanESM2 data was used as input to AquaCrop for generation of future data. The AquaCrop yield model was first checked for its suitability and accuracy in prediction of yield for years 2000–2015, model nash sutcliffe efficiency 0.79, 0.84, RMSE 300.7, 104.4 and coefficient of determination (R2) 0.91, 0.88 were obtained for wheat and soybean crops, respectively. The results depicts that RCP 8.5 shows the highest impact with reduction in wheat and soybean yield for projected year 2080. Under the changed climate, shifting planting date from of wheat from 15th November to 30th November and 1st July to 10th July for soybean resulted in least decline in crop yields and surfaced as a practical adaptation measure for sustaining future yields.

scholarly journals Evaluation of Climate Change Impacts on Cotton Yield using Cropsyst and Regression Models

2018 ◽  
Vol 8 ◽  
pp. 1433-1451 ◽  
Author(s):  
Pantazis Georgiou ◽  
Panagiota Koukouli
Keyword(s):  
Climate Change ◽  
Regression Models ◽  
General Circulation ◽  
Management Practices ◽  
Potential Evapotranspiration ◽  
Circulation Model ◽  
Planting Date ◽  
Climate Variables ◽  
Cotton Yield ◽  
Cotton Production

The regional as well as the international crop production is expected to be influenced by climate change. This study describes an assessment of simulated potential cotton yield using CropSyst, a cropping systems simulation model, in Northern Greece. CropSyst was used under the General Circulation Model CGCM3.1/T63 of the climate change scenario SRES B1 for time periods of climate change 2020-2050 and 2070-2100 for two planting dates. Additionally, an appraisal of the relationship between climate variables, potential evapotranspiration and cotton yield was done based on regression models. Multiple linear regression models based on climate variables and potential evapotranspiration could be used as a simple tool for the prediction of crop yield changes in response to climate change in the future. The CropSyst simulation under SRES B1, resulted in an increase by 6% for the period 2020-2050 and a decrease by about 15% in cotton yield for 2070-2100. For the earlier planting date a higher increase and a slighter reduction was observed in cotton yield for 2020-2050 and 2070-2100, respectively. The results indicate that alteration of crop management practices, such as changing the planting date could be used as potential adaptation measures to address the impacts of climate change on cotton production.

scholarly journals Potential Predictability of Long-Term Drought and Pluvial Conditions in the U.S. Great Plains

2008 ◽  
Vol 21 (4) ◽  
pp. 802-816 ◽  
Author(s):  
Siegfried D. Schubert ◽  
Max J. Suarez ◽  
Philip J. Pegion ◽  
Randal D. Koster ◽  
Julio T. Bacmeister
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
General Circulation ◽  
Storm Track ◽  
Warm Season ◽  
Circulation Model ◽  
Skewed Distribution ◽  
Soil Conditions ◽  
Unexpected Result ◽  
Potential Predictability

Abstract This study examines the predictability of seasonal mean Great Plains precipitation using an ensemble of century-long atmospheric general circulation model (AGCM) simulations forced with observed sea surface temperatures (SSTs). The results show that the predictability (intraensemble spread) of the precipitation response to SST forcing varies on interannual and longer time scales. In particular, this study finds that pluvial conditions are more predictable (have less intraensemble spread) than drought conditions. This rather unexpected result is examined in the context of the physical mechanisms that impact precipitation in the Great Plains. These mechanisms include El Niño–Southern Oscillation’s impact on the planetary waves and hence the Pacific storm track (primarily during the cold season), the role of Atlantic SSTs in forcing changes in the Bermuda high and low-level moisture flux into the continent (primarily during the warm season), and soil moisture feedbacks (primarily during the warm season). It is found that the changes in predictability are primarily driven by changes in the strength of the land–atmosphere coupling, such that under dry conditions a given change in soil moisture produces a larger change in evaporation and hence precipitation than the same change in soil moisture would produce under wet soil conditions. The above changes in predictability are associated with a negatively skewed distribution in the seasonal mean precipitation during the warm season—a result that is not inconsistent with the observations.

scholarly journals Identification of dominant sources of sea level pressure for precipitation forecasting over Wales

2012 ◽  
Vol 15 (3) ◽  
pp. 1002-1021 ◽  
Author(s):  
Azadeh Ahmadi ◽  
Dawei Han
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Circulation Model ◽  
Principal Component ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Climate Variables ◽  
Forward Selection ◽  
Precipitation Forecasting ◽  
Variable Combination

Downscaling methods are utilized to assess the effects of large scale atmospheric circulation on local hydrological variables such as precipitation and runoff. In this paper, a methodology of statistical downscaling using a support vector machine (SVM) approach is presented to simulate and predict the precipitation using general circulation model (GCM) data. Due to the complexity and issues related to finding a relationship between the large scale climatic parameters and local precipitation, the climate variables (predictors) affecting monthly precipitation variations over Wales are identified using a combination of the methods including the principal component analysis (PCA), fuzzy clustering, backward selection, forward selection, and Gamma test (GT). The effectiveness of those tools is illustrated through their implementations in the case study. It has been found that although the GT itself fails to identify the best input variable combination, it provides useful and narrowed-down options for further exploration. The best input variable combination is achieved by the GT and forward selection method. This approach can be a useful way for assessing the impacts of climate variables on precipitation forecasting.

scholarly journals Model simulations of atmospheric methane and their evaluation using AGAGE/NOAA surface- and IAGOS-CARIBIC aircraft observations, 1997–2014

2018 ◽  
Author(s):  
Peter H. Zimmermann ◽  
Carl A. M. Brenninkmeijer ◽  
Andrea Pozzer ◽  
Patrick Jöckel ◽  
Andreas Zahn ◽  
...  
Keyword(s):  
Shale Gas ◽  
Atmospheric Chemistry ◽  
General Circulation ◽  
A Priori ◽  
Circulation Model ◽  
Gas Production ◽  
Coefficient Of Determination ◽  
Atmospheric Methane ◽  
Synoptic Variability ◽  
Methane Distribution

Abstract. The global budget and trends of atmospheric methane (CH4) have been simulated with the EMAC atmospheric chemistry – general circulation model for the period 1997 through 2014. Observations from AGAGE and NOAA surface stations and intercontinental CARIBIC flights indicate a transient period of declining methane increase during 1997 through 1999, followed by seven years of stagnation and a sudden resumed increase after 2006. Starting the simulation with a global methane distribution, scaled to match the station measurements in January 1997 and using inter-annually constant CH4 sources from eleven categories together with photochemical and soil sinks, the model reproduces the observations during the transient and constant period from 1997 through 2006 in magnitude as well as seasonal and synoptic variability. The atmospheric CH4 calculations in our model setup are linearly dependent on the source strengths, allowing source segregated simulation of eleven biogenic and fossil emission categories (tagging), with the aim to analyze global observations and derive the source specific CH4 steady state lifetimes. Moreover, tagging enables a-posteriori rescaling of individual emissions with proportional effects on the corresponding inventories and offers a method to approximate the station measurements in terms of lowest RMS. Enhancing the a priori biogenic tropical wetland emissions by ~ 29 Tg/y, compensated by a reduction of anthropogenic fossil CH4 emissions, the all-station mean dry air mole fraction of 1792 nmol/mol could be simulated within a RMS of 0.37 %. The coefficient of determination R2 = 0.87 indicates good agreement with observed variability and the calculated 2000–2005 average interhemispheric methane difference between selected NH and SH stations of 119 nmol/mol matches the observations. The CH4 samples from 95 intercontinental CARIBIC flights for the period 1997–2006 are also accurately simulated by the model, with a 2000–2006 average CH4 mixing ratio of 1786 nmol/mol, and 65 % of the measured variability being captured. This includes tropospheric and stratospheric data. To explain the growth of CH4 from 2007 through 2013 in term of sources, an emission increase of 28.3 Tg/y CH4 is needed. We explore the contributions of two potential causes, one representing natural emissions from wetlands in the tropics and the other anthropogenic shale gas production emissions in North America. A 62.6 % tropical wetland contribution and of 37.4 % by shale gas emissions optimally fit the trend, and simulates CH4 from 2007–2013 with an RMS of 7.1 nmol/mol (0.39 %). The coefficient of determination of R2 = 0.91 indicates even higher significance than before 2006. The 4287 samples collected during 232 CARIBIC flights after 2007 are simulated with an RMS of 1.3 % and R2 = 0.8, indicating that the model reproduces the seasonal and synoptic variability of CH4 in the upper troposphere and lower stratosphere.

scholarly journals Comparison of Seasonal Potential Predictability of Precipitation

2014 ◽  
Vol 27 (11) ◽  
pp. 4094-4110 ◽  
Author(s):  
Xia Feng ◽  
Timothy DelSole ◽  
Paul Houser
Keyword(s):  
General Circulation ◽  
Markov Chain Model ◽  
Bootstrap Method ◽  
Circulation Model ◽  
Analysis Of Covariance ◽  
Estimation Methods ◽  
Chain Model ◽  
Noise Variance ◽  
Potential Predictability ◽  
Daily Data

Abstract Three methods for estimating potential seasonal predictability of precipitation from a single realization of daily data are assessed. The estimation methods include a first-order Markov chain model proposed by Katz (KZ), and an analysis of covariance (ANOCOVA) method and a bootstrap method proposed by the authors. The assessment is based on Monte Carlo experiments, ensemble atmospheric general circulation model (AGCM) simulations, and observation-based data. For AGCM time series, ANOCOVA produces the most accurate estimates of weather noise variance, despite the fact that it makes the most unrealistic assumptions about precipitation (in particular, it assumes precipitation is generated by a Gaussian autoregressive model). The KZ method significantly underestimates noise variance unless the autocorrelation of precipitation amounts on consecutive wet days is taken into account. Both AGCM and observation-based data reveal that the fraction of potentially predictable variance is greatest in the tropics, smallest in the extratropics, and undergoes a strong seasonal variation. The three methods give consistent estimates of potential predictability for 67% of the globe.

scholarly journals Application of LARS - WG downscaling model for building climate change scenarios in the Srepok watershed

2014 ◽  
Vol 17 (2) ◽  
pp. 108-122
Author(s):  
Khoi Nguyen Dao ◽  
Nhung Thi Hong Nguyen ◽  
Canh Thanh Truong
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Emission Scenario ◽  
Circulation Model ◽  
Climate Change Scenarios ◽  
Climate Variables ◽  
Climate Data ◽  
Policy Makers ◽  
Rain Gauges ◽  
Temperature And Precipitation

There are statistical downscaling methods such as: SDSM, LARS-WG, WGEN…, used to convert information on climate variables from the simulation results of General Circulation Model (GCM) to build climate change scenarios for local region. In this study, we used the LARS-WG model and HadCM3 GCM for two emission scenarios: B1 (low emission scenario) and A1B (medium emission scenario) to generate future scenarios for temperature and precipitation at meteorological stations and rain gauges in the Srepok watershed. The LARS-WG model was calibrated and validated against observed climate data for the period 1980-2009, and the calibrated LARS-WG was then used to generate future climate variables for the 2020s (2011-2030), 2055s (2046-2065), and 2090s (2080-2099). The climate change scenarios suggested that the climate in the study area will become warmer and drier in the future. The results obtained in this study could be useful for policy makers in planning climate change adaptation strategies for the study area.

Reproducibility of South American Precipitation due to Subtropical South Atlantic SSTs

2008 ◽  
Vol 21 (12) ◽  
pp. 2835-2851 ◽  
Author(s):  
Andréa S. Taschetto ◽  
Ilana Wainer
Keyword(s):  
General Circulation ◽  
Climate Model ◽  
Function Analysis ◽  
Regional Climate ◽  
Circulation Model ◽  
Internal Variability ◽  
South Atlantic ◽  
The South ◽  
Potential Predictability ◽  
The Impact

Abstract This work investigates the reproducibility of precipitation simulated with an atmospheric general circulation model (AGCM) forced by subtropical South Atlantic sea surface temperature (SST) anomalies. This represents an important test of the model prior to investigating the impact of SSTs on regional climate. A five-member ensemble run was performed using the National Center for Atmospheric Research (NCAR) Community Climate Model, version 3 (CCM3). The CCM3 was forced by observed monthly SST over the South Atlantic from 20° to 60°S. The SST dataset used is from the Hadley Centre covering the period of September 1949–October 2001; this covers more than 50 yr of simulation. A statistical technique is used to determine the reproducibility in the CCM3 runs and to assess potential predictability in precipitation. Empirical orthogonal function analysis is used to reconstruct the ensemble using the most reproducible forced modes in order to separate the atmospheric response to local SST forcing from its internal variability. Results for reproducibility show a seasonal dependence, with higher values during austral autumn and spring. The spatial distribution of reproducibility shows that the tropical atmosphere is dominated by the underlying SSTs while variations in the subtropical–extratropical regions are primarily driven by internal variability. As such, changes in the South Atlantic convergence zone (SACZ) region are mainly dominated by internal atmospheric variability while the ITCZ has greater external dependence, making it more predictable. The reproducibility distribution reveals increased values after the reconstruction of the ensemble.

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