scholarly journals Impact of merging of historical and future climate data sets on land carbon cycle projections for South America

2021 ◽  
Author(s):  
Chris Huntingford ◽  
Stephen A. Sitch ◽  
Michael O'Sullivan
Keyword(s):  
South America ◽  
Carbon Cycle ◽  
Future Climate ◽  
Data Sets ◽  
Climate Data

scholarly journals AgMIP Regional Integrated Assessment of Agricultural Systems in Nioro, Senegal

2022 ◽  
Vol 7 ◽  
Author(s):  
Ibrahima Hathie ◽  
Dilys MacCarthy ◽  
Bright Freduah ◽  
Mouhamed Ly ◽  
Ahmadou Ly ◽  
...  
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
Integrated Assessment ◽  
Future Climate ◽  
Sub Saharan Africa ◽  
Agricultural System ◽  
Data Sets ◽  
Agricultural Systems ◽  
Climate Data ◽  
Improvement Project

The Agricultural Model Intercomparison and Improvement Project (AgMIP) developed protocol-based methods for Regional Integrated Assessment (RIA) of agricultural systems. These methods have been applied by teams of scientists working with regional and national stakeholders across Sub-Saharan Africa and South Asia. This paper describes the data sets that were used to implement the AgMIP RIA methods for the Nioro region of Senegal. The goal of the RIA is to assess the potential impacts of climate change on the principal agricultural system in the Senegal peanut basin comprised of peanut, millet, maize and other minor crops and livestock, and to assess adaptations of that system to climate change, under current as well as future climate and socio-economic conditions. The data sets include: the Representative Agricultural Pathways (RAPs) developed for Nioro from 2000-2050; climate data used to implement crop yield simulations; the data used to parameterize the Agricultural Production Systems sIMulator (APSIM) and the Decision Support System for Agrotechnology Transfer (DSSAT) crop models, which include historical climate data and future climate scenarios; and the data used to parameterize the Tradeoff Analysis Model for Multi-dimensional Impact Assessment (TOA-MD) economic simulation model. The analysis is structured around four AgMIP “core questions'' of climate impact assessment.

scholarly journals Assessing Effects of Variation in Global Climate Data Sets on Spatial Predictions from Climate Envelope Models

2014 ◽  
Vol 5 (1) ◽  
pp. 14-25 ◽  
Author(s):  
James I. Watling ◽  
Robert J. Fletcher ◽  
Carolina Speroterra ◽  
David N. Bucklin ◽  
Laura A. Brandt ◽  
...  
Keyword(s):  
Climate Change ◽  
Initial Conditions ◽  
Model Performance ◽  
Future Climate ◽  
Data Sets ◽  
Climate Data ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Climate Envelope ◽  
Contemporary Climate

Abstract Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

scholarly journals Future climate change will impact the size and location of breeding and wintering areas of migratory thrushes in South America

The Condor ◽  
2021 ◽  
Author(s):  
Natália Stefanini Da Silveira ◽  
Maurício Humberto Vancine ◽  
Alex E Jahn ◽  
Marco Aurélio Pizo ◽  
Thadeu Sobral-Souza
Keyword(s):  
Climate Change ◽  
South America ◽  
Migratory Birds ◽  
Global Climate ◽  
Future Climate ◽  
Future Climate Change ◽  
Ecological Niche Models ◽  
Global Climate Changes ◽  
The Andes ◽  
Wintering Areas

Abstract Bird migration patterns are changing worldwide due to current global climate changes. Addressing the effects of such changes on the migration of birds in South America is particularly challenging because the details about how birds migrate within the Neotropics are generally not well understood. Here, we aim to infer the potential effects of future climate change on breeding and wintering areas of birds that migrate within South America by estimating the size and elevations of their future breeding and wintering areas. We used occurrence data from species distribution databases (VertNet and GBIF), published studies, and eBird for 3 thrush species (Turdidae; Turdus nigriceps, T. subalaris, and T. flavipes) that breed and winter in different regions of South America and built ecological niche models using ensemble forecasting approaches to infer current and future potential distributions throughout the breeding and wintering periods of each species. Our findings point to future shifts in wintering and breeding areas, mainly through elevational and longitudinal changes. Future breeding areas for T. nigriceps, which migrates along the Andes Mountains, will be displaced to the west, while breeding displacements to the east are expected for the other 2 species. An overall loss in the size of future wintering areas was also supported for 2 of the species, especially for T. subalaris, but an increase is anticipated for T. flavipes. Our results suggest that future climate change in South America will require that species shift their breeding and wintering areas to higher elevations in addition to changes in their latitudes and longitude. Our findings are the first to show how future climate change may affect migratory birds in South America throughout the year and suggest that even closely related migratory birds in South America will be affected in different ways, depending on the regions where they breed and overwinter.

scholarly journals Determining future aflatoxin contamination risk scenarios for corn in Southern Georgia, USA using spatio-temporal modelling and future climate simulations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruth Kerry ◽  
Ben Ingram ◽  
Esther Garcia-Cela ◽  
Naresh Magan ◽  
Brenda V. Ortiz ◽  
...  
Keyword(s):  
Strong Association ◽  
Growth Period ◽  
Aflatoxin Contamination ◽  
Adaptation Strategies ◽  
Future Climate ◽  
Climate Data ◽  
Emissions Scenarios ◽  
Rcp 8.5 ◽  
Maximum Temperatures ◽  
Southern Georgia

AbstractAflatoxins (AFs) are produced by fungi in crops and can cause liver cancer. Permitted levels are legislated and batches of grain are rejected based on average concentrations. Corn grown in Southern Georgia (GA), USA, which experiences drought during the mid-silk growth period in June, is particularly susceptible to infection by Aspergillus section Flavi species which produce AFs. Previous studies showed strong association between AFs and June weather. Risk factors were developed: June maximum temperatures > 33 °C and June rainfall < 50 mm, the 30-year normals for the region. Future climate data were estimated for each year (2000–2100) and county in southern GA using the RCP 4.5 and RCP 8.5 emissions scenarios. The number of counties with June maximum temperatures > 33 °C and rainfall < 50 mm increased and then plateaued for both emissions scenarios. The percentage of years thresholds were exceeded was greater for RCP 8.5 than RCP 4.5. The spatial distribution of high-risk counties changed over time. Results suggest corn growth distribution should be changed or adaptation strategies employed like planting resistant varieties, irrigating and planting earlier. There were significantly more counties exceeding thresholds in 2010–2040 compared to 2000–2030 suggesting that adaptation strategies should be employed as soon as possible.

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

scholarly journals STUDY ON BIAS CORRECTION METHODS OF CENTRAL ATMOSPHERIC PRESSURE OF TYPHOON IN A FUTURE CLIMATE DATA SET

2017 ◽  
Vol 73 (2) ◽  
pp. I_1417-I_1422
Author(s):  
Yoshihiko IDE ◽  
Yuji ISSHIKI ◽  
Mitsuyoshi KODAMA ◽  
Noriaki HASHIMOTO ◽  
Masaru YAMASHIRO
Keyword(s):  
Bias Correction ◽  
Atmospheric Pressure ◽  
Future Climate ◽  
Climate Data ◽  
Data Set
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