scholarly journals From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact

2005 ◽  
Vol 360 (1463) ◽  
pp. 2095-2108 ◽  
Author(s):  
Christian Baron ◽  
Benjamin Sultan ◽  
Maud Balme ◽  
Benoit Sarr ◽  
Seydou Traore ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Climate Variability ◽  
General Circulation ◽  
Crop Yields ◽  
Grid Cell ◽  
General Circulation Models ◽  
Crop Model ◽  
Weather Data ◽  
Climatic Data ◽  
Cell Means

General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10°N–17° N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel–Guillot–Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10–50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level.

Integrated Assessment of Climate Change Impacts on Corn Yield in the U.S. Using a Crop Model

2017 ◽  
Vol 60 (6) ◽  
pp. 2123-2136
Author(s):  
Kenichi Tatsumi
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Climate Change Impacts ◽  
General Circulation Models ◽  
Crop Model ◽  
Corn Yield ◽  
Negative Impacts ◽  
Growing Conditions ◽  
Almost All ◽  
The U.S

Abstract. A detailed analysis was conducted of the effects of climate change and increased carbon dioxide (CO2) concentrations on corn yield in the U.S. with a crop model using outputs from multiple general circulation models (multi-GCMs). Corn yield was simulated for 1999-2010, for the 2050s (average for 2041-2060), and for the 2070s (average for 2061-2080) under the representative concentration pathway 8.5 (RCP8.5) climate scenario. Results indicated a shortening of the growing period (GP), decreased water use efficiency (WUE) in almost all regions, and increased evapotranspiration (ET) during GP in almost all regions except for the southern U.S. Using multi-GCMs, the simulations under the RCP8.5 scenario resulted in negative effects of climate change on yield in almost all regions during both future periods. Especially strong negative impacts were reported south of latitude 40° N due to less optimal growing conditions. On the other hand, there were relatively smaller negative impacts in high-latitude regions (approximately north of latitude 40° N) due to more optimal growing conditions because of larger temperature changes compared to low-latitude and mid-latitude regions. Higher CO2 concentrations have the potential to increase corn yield. CO2 effects resulted in an approximately 0.04% to 0.05% increase in yield per 1 ppm increase in CO2 concentration under the RCP8.5 scenario, but the negative impacts of increased temperatures fully outweighed the CO2-fertilization effects. Keywords: Climate change impacts, CO2 effects, Corn yield, Multiple GCMs, Uncertainty.

scholarly journals On the use of Schwarz–Christoffel conformal mappings to the grid generation for global ocean models

2015 ◽  
Vol 8 (10) ◽  
pp. 3471-3485 ◽  
Author(s):  
S. Xu ◽  
B. Wang ◽  
J. Liu
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Grid Cell ◽  
Grid Generation ◽  
General Circulation Models ◽  
Conformal Mappings ◽  
Ocean Modeling ◽  
Global Ocean ◽  
Ocean General Circulation Models ◽  
Ocean General Circulation

Abstract. In this article we propose two grid generation methods for global ocean general circulation models. Contrary to conventional dipolar or tripolar grids, the proposed methods are based on Schwarz–Christoffel conformal mappings that map areas with user-prescribed, irregular boundaries to those with regular boundaries (i.e., disks, slits, etc.). The first method aims at improving existing dipolar grids. Compared with existing grids, the sample grid achieves a better trade-off between the enlargement of the latitudinal–longitudinal portion and the overall smooth grid cell size transition. The second method addresses more modern and advanced grid design requirements arising from high-resolution and multi-scale ocean modeling. The generated grids could potentially achieve the alignment of grid lines to the large-scale coastlines, enhanced spatial resolution in coastal regions, and easier computational load balance. Since the grids are orthogonal curvilinear, they can be easily utilized by the majority of ocean general circulation models that are based on finite difference and require grid orthogonality. The proposed grid generation algorithms can also be applied to the grid generation for regional ocean modeling where complex land–sea distribution is present.

scholarly journals Agro-Climatic Data by County: A Spatially and Temporally Consistent U.S. Dataset for Agricultural Yields, Weather and Soils

Data ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 66
Author(s):  
Seong Do Yun ◽  
Benjamin M. Gramig
Keyword(s):  
Spatial Data ◽  
Crop Yields ◽  
Original Data ◽  
Data Sources ◽  
Weather Data ◽  
Climatic Data ◽  
Soil Characteristic ◽  
Growing Seasons ◽  
Aggregation And Disaggregation ◽  
Agricultural Yields

Agro-climatic data by county (ACDC) is designed to provide the major agro-climatic variables from publicly available spatial data sources to diverse end-users. ACDC provides USDA NASS annual (1981–2015) crop yields for corn, soybeans, upland cotton and winter wheat by county. Customizable growing degree days for 1 °C intervals between −60 °C and +60 °C, and total precipitation for two different crop growing seasons from the PRISM weather data are included. Soil characteristic data from USDA-NRCS gSSURGO are also provided for each county in the 48 contiguous US states. All weather and soil data are processed to include only data for land being used for non-forestry agricultural uses based on the USGS NLCD land cover/land use data. This paper explains the numerical and geo-computational methods and data generating processes employed to create ACDC from the original data sources. Essential considerations for data management and use are discussed, including the use of the agricultural mask, spatial aggregation and disaggregation, and the computational requirements for working with the raw data sources.

scholarly journals Impact Assessment of Climate Change on Paddy Yield: A Case Study of Nepal Agriculture Research Council (NARC), Tarahara, Nepal

1970 ◽  
Vol 8 (3) ◽  
pp. 147-167 ◽  
Author(s):  
Yam K Rai ◽  
Bhakta B Ale ◽  
Jawed Alam
Keyword(s):  
Climate Change ◽  
Solar Radiation ◽  
Research Council ◽  
Crop Yields ◽  
Rice Yield ◽  
Weather Data ◽  
Maximum Temperature ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
The Impact

Climate change and global warming are burning issues, which significantly threat agriculture and global food security. Change in solar radiation, temperature and precipitation will influence the change in crop yields and hence economy of agriculture. It is possible to understand the phenomenon of climate change on crop production and to develop adaptation strategies for sustainability in food production, using a suitable crop simulation model. CERES-Rice model of DSSAT v4.0 was used to simulate the rice yield of the region under climate change scenarios using the historical weather data at Nepal Agriculture Research Council (NARC) Tarahara (1989-2008). The Crop Model was calibrated using the experimental crop data, climate data and soil data for two years (2000-2001) and was validated by using the data of the year 2002 at NARC Tarahara. In this study various scenarios were undertaken to analyze the rice yield. The change in values of weather parameters due to climate change and its effects on the rice yield were studied. It was observed that increase in maximum temperature up to 2°C and 1°C in minimum temperature have positive impact on rice yield but beyond that temperature it was observed negative impact in both cases of paddy production in ambient temperature. Similarly, it was observed that increased in mean temperature, have negative impacts on rice yield. The impact of solar radiation in rice yield was observed positive during the time of study period. Adjustments were made in the fertilizer rate, plant density per square meter, planting date and application of water rate to investigate suitable agronomic options for adaptation under the future climate change scenarios. Highest yield was obtained when the water application was increased up to 3 mm depth and nitrogen application rate was 140 kg/ha respectively. DOI: http://dx.doi.org/10.3126/jie.v8i3.5941 JIE 2011; 8(3): 147-167

scholarly journals Determination of humidity and temperature fluctuations based on MOZAIC data and parametrisation of persistent contrail coverage for general circulation models

1997 ◽  
Vol 15 (8) ◽  
pp. 1057-1066 ◽  
Author(s):  
K. M. Gierens ◽  
U. Schumann ◽  
H. G. J. Smit ◽  
M. Helten ◽  
G. Zängl
Keyword(s):  
High Frequency ◽  
General Circulation ◽  
Climate Model ◽  
Joint Probability ◽  
Grid Cell ◽  
Temperature Fluctuations ◽  
General Circulation Models ◽  
Joint Probability Distribution ◽  
Large Fluctuations

Abstract. Humidity and temperature fluctuations at pressure levels between 166 and 290 hPa on the grid scale of general circulation models for a region covered by the routes of airliners, mainly over the Atlantic, have been determined by evaluation of the data obtained with almost 2000 flights within the MOZAIC programme. It is found that the distributions of the fluctuations cannot be modelled by Gaussian distributions, because large fluctuations appear with a relatively high frequency. Lorentz distributions were used for the analytical representation of the fluctuation distributions. From these a joint probability distribution has been derived for simultaneous temperature and humidity fluctuations. This function together with the criteria for the formation and persistence of contrails are used to derive the maximum possible fractional coverage of persistent contrails in a grid cell of a GCM. This can be employed in a statistical formulation of contrail appearance in a climate model.

scholarly journals Multiscale Modeling of the Moist-Convective Atmosphere

2016 ◽  
Vol 56 ◽  
pp. 16.1-16.17 ◽  
Author(s):  
Akio Arakawa ◽  
Joon-Hee Jung ◽  
Chien-Ming Wu
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Global Climate ◽  
Grid Cell ◽  
General Circulation Models ◽  
Horizontal Resolution ◽  
Tropical Meteorology ◽  
Cumulus Parameterization ◽  
Technical Problems ◽  
Cloud Resolving Model

Abstract One of the most important contributions of Michio Yanai to tropical meteorology is the introduction of the concepts of apparent heat source Q1 and apparent moisture sink Q2 in the large-scale heat and moisture budgets of the atmosphere. Through the inclusion of unresolved eddy effects, the vertical profiles of apparent sources (and sinks) are generally quite different from those of true sources taking place locally. In low-resolution models, such as the conventional general circulation models (GCMs), cumulus parameterization is supposed to determine the apparent sources for each grid cell from the explicitly predicted grid-scale processes. Because of the recent advancement of computer technology, however, increasingly higher horizontal resolutions are being used even for studying the global climate, and, therefore, the concept of apparent sources must be expanded rather drastically. Specifically, the simulated apparent sources should approach and eventually converge to the true sources as the horizontal resolution is refined. For this transition to take place, the conventional cumulus parameterization must be either generalized so that it is applicable to any horizontal resolutions or replaced with the mean effects of cloud-scale processes explicitly simulated by a cloud-resolving model (CRM). These two approaches are called ROUTE I and ROUTE II for unifying low- and high-resolution models, respectively. This chapter discusses the conceptual and technical problems in exploring these routes and reviews the authors’ recent work on these subjects.

scholarly journals Historical and Future Typical Meteorological Years for 33 locations in Greece: a handy tool for various applications

2021 ◽  
Author(s):  
Basil Psiloglou ◽  
Harry D. Kambezidis ◽  
Konstantinos V. Varotsos ◽  
Dimitris G. Kaskaoutis ◽  
Dimiitris Karagiannis ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Design Tool ◽  
Weather Data ◽  
Climate Simulation ◽  
Total Solar Radiation ◽  
Climatic Data ◽  
Data Set ◽  
Specific Location ◽  
Daily Data

<p>It is generally accepted that a climatic data set of meteorological measurements with true sequences and real interdependencies between meteorological variables is needed for a representative climate simulation. In the late 1970s the Typical Meteorological Year (TMY) concept was introduced in USA as a design tool for approximating expected climate conditions at specific locations, at a time when computers were much slower and had less memory than today. A TMY is a collation of selected weather data for a specific location, listing usually hourly values of meteorological and solar radiation elements for one-year period. The values are generated from a data bank much longer than a year in duration, at least 10 years. It is specially selected so that it presents the range of weather phenomena for the location in question, while still giving annual averages that are consistent with the long-term averages for the specific location. Each TMY data file consists of 12 months chosen as most “typical“ among the years present in the long-term data set. Although TMYs do not provide information about extreme events and do not necessarily represent actual conditions at any given time, they still reflect all the climatic information of the location. TMY sets remain in popular use until today providing a relatively concise data set from which system performance estimates can be developed, without the need of incorporating large amounts of data into simulation models. </p><p>TMY sets for 33 locations in Greece distributed all over the country were developed, covering for the first time all climatic zones, for both historical and future periods. Historical TMY sets generation was based on meteorological data collected from the Hellenic National Meteorological Service (HNMS) network in Greece in the period 1985-2014, while the corresponding total solar radiation values have been derived through the Meteorological Radiation Model (MRM).</p><p>Moreover, the generation of future TMY sets for Greece was also performed, for all 33 locations. To this aim, bias adjusted daily data for the closest grid point to the HNMS station’s location were employed from the RCA4 Regional Climate Model of the Swedish Meteorological and Hydrological Institute (SMHI) driven by the Earth system model of the Max Planck Institute for Meteorology (MPI-M). Simulations were carried out in the framework of the EURO-CORDEX modeling experiment, with a horizontal RCA4 model resolution of 0.11<sup>o</sup> (~12 x 12 km). We used daily data for four periods: the 1985-2014 used as reference period and the 2021-2050, 2046-2070 and 2071-2100 future periods under RCP4.5 and RCP8.5 scenarios. </p><p>This work was carried out in the framework of the “Development of synergistic and integrated methods and tools for monitoring, management and forecasting of environmental parameters and pressures” (KRIPIS-THESPIA-II) Greek national funded project.</p>

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