scholarly journals Sources of interannual yield variability in JULES-crop and implications for forcing with seasonal weather forecasts

2015 ◽  
Vol 8 (12) ◽  
pp. 3987-3997 ◽  
Author(s):  
K. E. Williams ◽  
P. D. Falloon
Keyword(s):  
Maize Yield ◽  
Sowing Date ◽  
Reanalysis Data ◽  
Specific Humidity ◽  
Seasonal Forecasts ◽  
Time Step ◽  
Data Set ◽  
Weather Forecasts ◽  
Start Date ◽  
The Impact

Abstract. JULES-crop is a parametrisation of crops in the Joint UK Land Environment Simulator (JULES). We investigate the sources of the interannual variability in the modelled maize yield, using global runs driven by reanalysis data, with a view to understanding the impact of various approximations in the driving data and initialisation. The standard forcing data set for JULES consists of a combination of meteorological variables describing precipitation, radiation, temperature, pressure, specific humidity and wind, at subdaily time resolution. We find that the main characteristics of the modelled yield can be reproduced with a subset of these variables and using daily forcing, with internal disaggregation to the model time step. This has implications in particular for the use of the model with seasonal forcing data, which may not have been provided at subdaily resolution for all required driving variables. We also investigate the effect on annual yield of initialising the model with climatology on the sowing date. This approximation has the potential to considerably simplify the use of the model with seasonal forecasts, since obtaining observations or reanalysis output for all the initialisation variables required by JULES for the start date of the seasonal forecast would present significant practical challenges.

scholarly journals Sources of interannual yield variability in JULES-crop and implications for forcing with seasonal weather forecasts

2015 ◽  
Vol 8 (6) ◽  
pp. 4599-4621 ◽  
Author(s):  
K. E. Williams ◽  
P. D. Falloon
Keyword(s):  
Maize Yield ◽  
Sowing Date ◽  
Reanalysis Data ◽  
Specific Humidity ◽  
Yield Variability ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
Start Date ◽  
The Impact ◽  
Seasonal Weather

Abstract. JULES-crop is a parametrisation of crops in the Joint UK Land Environment Simulator (JULES). We investigate the sources of the interannual variability in the modelled maize yield, using global runs driven by reanalysis data, with a view to understanding the impact of various approximations in the driving data and initialisation. The standard forcing dataset for JULES consists of a combination of meteorological variables describing precipitation, radiation, temperature, pressure, specific humidity and wind, at subdaily time resolution. We find that the main characteristics of the modelled yield can be reproduced with a subset of these variables and using daily forcing, with internal disaggregation to the model timestep. This has implications in particular for the use of the model with seasonal forcing data, which may not have been provided at subdaily resolution for all required driving variables. We also investigate the effect on annual yield of initialising the model with climatology on the sowing date. This approximation has the potential to considerably simplify the use of the model with seasonal forecasts, since obtaining observations or reanalysis output for all the initialisation variables required by JULES for the start date of the seasonal forecast would present significant practical challenges.

scholarly journals On the Use of Reanalysis Data for Downscaling

2012 ◽  
Vol 25 (7) ◽  
pp. 2517-2526 ◽  
Author(s):  
S. Brands ◽  
J. M. Gutiérrez ◽  
S. Herrera ◽  
A. S. Cofiño
Keyword(s):  
Climate Models ◽  
Pearson Correlation ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Reanalysis Data ◽  
Specific Humidity ◽  
Weather Forecasts ◽  
Kolmogorov Smirnov ◽  
Similarity Maps ◽  
Daily Time

Abstract In this study, a worldwide overview on the expected sensitivity of downscaling studies to reanalysis choice is provided. To this end, the similarity of middle-tropospheric variables—which are important for the development of both dynamical and statistical downscaling schemes—from 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and NCEP–NCAR reanalysis data on a daily time scale is assessed. For estimating the distributional similarity, two comparable scores are used: the two-sample Kolmogorov–Smirnov statistic and the probability density function (PDF) score. In addition, the similarity of the day-to-day sequences is evaluated with the Pearson correlation coefficient. As the most important results demonstrated, the PDF score is found to be inappropriate if the underlying data follow a mixed distribution. By providing global similarity maps for each variable under study, regions where reanalysis data should not assumed to be “perfect” are detected. In contrast to the geopotential and temperature, significant distributional dissimilarities for specific humidity are found in almost every region of the world. Moreover, for the latter these differences not only occur in the mean, but also in higher-order moments. However, when considering standardized anomalies, distributional and serial dissimilarities are negligible over most extratropical land areas. Since transformed reanalysis data are not appropriate for regional climate models—in opposition to statistical approaches—their results are expected to be more sensitive to reanalysis choice.

scholarly journals Simulating the Impact of Climate Change on Maize Productivity in Trans-gangetic Plains using Info Crop Model

2020 ◽  
Author(s):  
Baljeet Kaur ◽  
Som Pal Singh ◽  
P.K. Kingra
Keyword(s):  
Climate Change ◽  
Maize Yield ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Data Set ◽  
Gangetic Plains ◽  
Impact Of Climate Change ◽  
Model Average ◽  
In Trans ◽  
The Impact

Background: Climate change is a nonpareil threat to the food security of hundred millions of people who depends on agriculture for their livelihood. A change in climate affects agricultural production as climate and agriculture are intensely interrelated global processes. Global warming is one of such changes which is projected to have significant impacts on environment affecting agriculture. Agriculture is the mainstay economy in trans-gangetic plains of India and maize is the third most important crop after wheat and rice. Heat stress in maize cause several changes viz. morphological, anatomical and physiological and biochemical changes. Methods: In this study during 2014-2018, impact of climate change on maize yield in future scenarios was simulated using the InfoCrop model. Average maize yield from 2001-15 was collected for Punjab, Haryana and Delhi to calibrate and validate the model. Future climatic data set from 2020 to 2050 was used in the study to analyse the trends in climatic parameters.Result: Analysis of future data revealed increasing trends in maximum temperature and minimum temperature. Rainfall would likely follow the erratic behaviour in Punjab, Haryana and Delhi. Increase in temperature was predicted to have negative impact on maize yield under future climatic scenario.

scholarly journals A multi-instrument comparison of integrated water vapour measurements at a high latitude site

2012 ◽  
Vol 12 (22) ◽  
pp. 10925-10943 ◽  
Author(s):  
S. A. Buehler ◽  
S. Östman ◽  
C. Melsheimer ◽  
G. Holl ◽  
S. Eliasson ◽  
...  
Keyword(s):  
Water Vapour ◽  
Microwave Radiometer ◽  
Reanalysis Data ◽  
Data Set ◽  
Lower Altitude ◽  
Integrated Water Vapour ◽  
Ftir Spectrometer ◽  
The Impact ◽  
Instrument Comparison ◽  
Temporal Coverage

Abstract. We compare measurements of integrated water vapour (IWV) over a subarctic site (Kiruna, Northern Sweden) from five different sensors and retrieval methods: Radiosondes, Global Positioning System (GPS), ground-based Fourier-transform infrared (FTIR) spectrometer, ground-based microwave radiometer, and satellite-based microwave radiometer (AMSU-B). Additionally, we compare also to ERA-Interim model reanalysis data. GPS-based IWV data have the highest temporal coverage and resolution and are chosen as reference data set. All datasets agree reasonably well, but the ground-based microwave instrument only if the data are cloud-filtered. We also address two issues that are general for such intercomparison studies, the impact of different lower altitude limits for the IWV integration, and the impact of representativeness error. We develop methods for correcting for the former, and estimating the random error contribution of the latter. A literature survey reveals that reported systematic differences between different techniques are study-dependent and show no overall consistent pattern. Further improving the absolute accuracy of IWV measurements and providing climate-quality time series therefore remain challenging problems.

scholarly journals Spatial–Temporal Distribution of Tropospheric Specific Humidity in the Arid Region of Northwest China

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 349
Author(s):  
Hao Zhang ◽  
Meiping Sun ◽  
Xiaojun Yao ◽  
Zhilan Wang ◽  
Lei Zhang
Keyword(s):  
Spatial Distribution ◽  
Arid Region ◽  
Temporal Distribution ◽  
Northwest China ◽  
Atmospheric Temperature ◽  
Reanalysis Data ◽  
Dew Point ◽  
Specific Humidity ◽  
Positive Trend ◽  
Weather Forecasts

Based on the atmospheric temperature and dew point temperature difference series of mandatory levels in the arid region of northwest China, we calculated the specific humidity of stations at 200, 300, 400, 500, 700, and 850 hPa and analyzed the spatial and temporal distribution. The specific humidity of radiosonde is compared with two sets of reanalysis data (ERA-interim from European Centre for Medium Range Weather Forecasts and Modern Era Retrospective Analysis for Research and Applications: MERRA-2). The annual specific humidity and summer specific humidity show a positive trend in the vertical atmospheric levels during the period 1958–2018. Taking the middle of the 1980s and 2002 as boundaries, the selected levels show the trend of “declining-gentle rising-fluctuation rising”. The maximum specific humidity is observed at the level of 850–700 hPa during the warm months of the year, and the most vertical expansion in specific humidity is in July. In terms of spatial distribution, the specific humidity is greatly influenced by the topography and underlying surface at lower levels. The characteristics of spatial distribution of the trend are well described by the two sets of reanalysis data in the middle and upper levels, but there are some deficiencies in depicting the trend in the lower levels.

scholarly journals Perceived temperature in the course of climate change: an analysis of global heat index from 1979 to 2013

2015 ◽  
Vol 7 (2) ◽  
pp. 193-202 ◽  
Author(s):  
D. Lee ◽  
T. Brenner
Keyword(s):  
Climate Change ◽  
Human Health ◽  
Reanalysis Data ◽  
Absolute Temperature ◽  
Dew Point ◽  
Heat Index ◽  
Global Changes ◽  
Data Set ◽  
Economic Productivity ◽  
The Impact

Abstract. The increase in global mean temperatures resulting from climate change has wide reaching consequences for the earth's ecosystems and other natural systems. Many studies have been devoted to evaluating the distribution and effects of these changes. We go a step further and propose the use of the heat index, a measure of the temperature as perceived by humans, to evaluate global changes. The heat index, which is computed from temperature and relative humidity, is more important than temperature for the health of humans and animals. Even in cases where the heat index does not reach dangerous levels from a health perspective, it has been shown to be an important factor in worker productivity and thus in economic productivity. We compute the heat index from dew point temperature and absolute temperature 2 m above ground from the ERA-Interim reanalysis data set for the years 1979–2013. The described data set provides global heat index aggregated to daily minima, means and maxima per day (doi:10.1594/PANGAEA.841057). This paper examines these data, as well as showing aggregations to monthly and yearly values. Furthermore, the data are spatially aggregated to the level of countries after being weighted by population density in order to facilitate the analysis of its impact on human health and productivity. The resulting data deliver insights into the spatiotemporal development of near-ground heat index during the course of the past three decades. It is shown that the impact of changing heat index is unevenly distributed through space and time, affecting some areas differently than others. The data can serve as a basis for evaluating and understanding the evolution of heat index in the course of climate change, as well as its impact on human health and productivity.

scholarly journals Near-real-time adjusted reanalysis forcing data for hydrology

2018 ◽  
Vol 22 (2) ◽  
pp. 989-1000 ◽  
Author(s):  
Peter Berg ◽  
Chantal Donnelly ◽  
David Gustafsson
Keyword(s):  
Real Time ◽  
Global Scale ◽  
Reanalysis Data ◽  
The Arctic ◽  
Data Sets ◽  
Initial State ◽  
Time Step ◽  
Data Set ◽  
Global Data Sets ◽  
Global Data

Abstract. Extending climatological forcing data to current and real-time forcing is a necessary task for hydrological forecasting. While such data are often readily available nationally, it is harder to find fit-for-purpose global data sets that span long climatological periods through to near-real time. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especially relative to the data used for the calibration of the model. The lack of high-quality daily resolution data on a global scale has previously been solved by adjusting reanalysis data with global gridded observations. However, existing data sets of this type have been produced for a fixed past time period determined by the main global observational data sets. Long delays between updates of these data sets leaves a data gap between the present day and the end of the data set. Further, hydrological forecasts require initializations of the current state of the snow, soil and lake (and sometimes river) storage. This is normally conceived by forcing the model with observed meteorological conditions for an extended spin-up period, typically at a daily time step, to calculate the initial state. Here, we present and evaluate a method named HydroGFD (Hydrological Global Forcing Data) to combine different data sets in order to produce near-real-time updated hydrological forcing data of temperature and precipitation that are compatible with the products covering the climatological period. HydroGFD resembles the already established WFDEI (WATCH Forcing Data–ERA-Interim) method (Weedon et al., 2014) closely but uses updated climatological observations, and for the near-real time it uses interim products that apply similar methods. This allows HydroGFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the HydroGFD method and therewith produced data sets, which are evaluated against global data sets, as well as with hydrological simulations with the HYPE (Hydrological Predictions for the Environment) model over Europe and the Arctic regions. We show that HydroGFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data. For real-time updates until the current day, extending HydroGFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model.

A Reduced Radiation Grid for the ECMWF Integrated Forecasting System

2008 ◽  
Vol 136 (12) ◽  
pp. 4760-4772 ◽  
Author(s):  
Jean-Jacques Morcrette ◽  
George Mozdzynski ◽  
Martin Leutbecher
Keyword(s):  
High Resolution ◽  
Radiation Transfer ◽  
Specific Humidity ◽  
Ensemble Prediction ◽  
Seasonal Forecasts ◽  
Low Resolution ◽  
Ensemble Prediction System ◽  
Medium Resolution ◽  
Ecmwf Model ◽  
The Impact

Abstract A specific interface between the radiation transfer calculations and the rest of the ECMWF model was introduced in 2003, potentially providing substantial economy in computer time by reducing the spatial resolution at which radiation transfer is evaluated, without incurring some of the deficiencies produced by the sampling strategy previously used in the ECMWF model. The introduction of a new more-computer-intensive radiation package (McRad) in June 2007 has led to a differentiated use of this interface depending on the applications. The history of the interface, how it is used, and its impact when using the new radiation scheme are discussed here. For a given model resolution, the impact of a lower-resolution radiation grid on the model behavior is studied here, in the context of 10-day forecasts at high resolution (TL799L91), of medium-resolution forecasts (TL399L62) used in the Ensemble Prediction System (EPS), and of low-resolution simulations (TL159L91) as used for model development and seasonal forecasts with an interactive ocean. Results for the high-resolution forecasts are compared in terms of objective scores and of the quality of “surface” parameters (total cloud cover, 2-m temperature and specific humidity, and 10-m wind) usually verified in a meteorological context. For the medium-resolution forecasts, the impact of the radiation grid is studied in terms of the potential increase in the efficiency of the EPS system without deteriorating the probabilistic skill. The impact of changes in the radiation grid resolution on the low-resolution versions of model is discussed in terms of cloud–radiation interactions and ocean surface temperature. For these operational applications, a radiation grid with a coarsening factor even as large as 2.5 for TL799L91 and TL159L91 and 4.2 for the EPS TL399L62 is shown to give results free of any systematic differences linked to the spatial interpolation and to the coarser resolution of both the inputs to and the outputs from the radiation transfer schemes.

scholarly journals Analysis of the ozone profile specifications in the WRF-ARW model and their impact on the simulation of direct solar radiation

2015 ◽  
Vol 15 (5) ◽  
pp. 2693-2707 ◽  
Author(s):  
A. Montornès ◽  
B. Codina ◽  
J. W. Zack
Keyword(s):  
Solar Radiation ◽  
Weather Prediction ◽  
Reanalysis Data ◽  
Direct Solar Radiation ◽  
Spatial And Temporal Variability ◽  
Data Sets ◽  
Data Set ◽  
Ozone Profile ◽  
Arw Model ◽  
The Impact

Abstract. Although ozone is an atmospheric gas with high spatial and temporal variability, mesoscale numerical weather prediction (NWP) models simplify the specification of ozone concentrations used in their shortwave schemes by using a few ozone profiles. In this paper, a two-part study is presented: (i) an evaluation of the quality of the ozone profiles provided for use with the shortwave schemes in the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model and (ii) an assessment of the impact of deficiencies in those profiles on the performance of model simulations of direct solar radiation. The first part compares simplified data sets used to specify the total ozone column in six schemes (i.e., Goddard, New Goddard, RRTMG, CAM, GFDL and Fu–Liou–Gu) with the Multi-Sensor Reanalysis data set during the period 1979–2008 examining the latitudinal, longitudinal and seasonal limitations in the ozone profile specifications of each parameterization. The results indicate that the maximum deviations are over the poles and show prominent longitudinal patterns in the departures due to the lack of representation of the patterns associated with the Brewer–Dobson circulation and the quasi-stationary features forced by the land–sea distribution, respectively. In the second part, the bias in the simulated direct solar radiation due to these deviations from the simplified spatial and temporal representation of the ozone distribution is analyzed for the New Goddard and CAM schemes using the Beer–Lambert–Bouguer law and for the GFDL using empirical equations. For radiative applications those simplifications introduce spatial and temporal biases with near-zero departures over the tropics throughout the year and increasing poleward with a maximum in the high middle latitudes during the winter of each hemisphere.

scholarly journals Downscaling global seasonal weather forecasts for crop yield forecasting over Zimbabwe

2021 ◽  
Author(s):  
Sinclair Chinyoka ◽  
Gert-Jan Steeneveld
Keyword(s):  
Mesoscale Model ◽  
Irrigation System ◽  
Growing Season ◽  
Maize Yield ◽  
Seasonal Rainfall ◽  
Seasonal Forecasts ◽  
Crop Failure ◽  
Percent Correct ◽  
Rainfall Prediction ◽  
The Impact

<p>This study focuses on the assessment of the impact of downscaling seasonal forecasts from the Climate Forecast System version 2 (CFSv2) using the Weather Research and Forecasting (WRF) mesoscale model over Zimbabwe on a spatial resolution of 21km and 7km for Southern Africa and Zimbabwe respectively. We used a 7-day re-initialization simulation strategy for 212 days per season and was repeated for eights seasons between 2010 and 2018. The impact of downscaling global seasonal forecasts was further evaluated in crop forecasting using the WOrld FOod STudies (WOFOST) model. Statistical analysis of the forecasted seasonal rainfall revealed a reduction of the bias from about -2 mm/day from CFSv2 forecasts to about 0.5mm/day from WRF forecasts in most parts of the country. We also found that an improvement in seasonal tercile rainfall prediction from 25%, 50%, and 75% by CFSv2 in three different regions to about 62.5% by WRF in all regions. Substantial improvement was achieved in Standard Precipitation Index-driven seasonal forecasts with two regions with a percent correct of 75% and region 2 with 100% by WRF compared to 62.5% by CFSv2 in all regions. Hence, the characterization of seasonal rainfall in terms of drought forecasts is better than the tercile rainfall prediction system and will be more beneficial to farmers in Zimbabwe. WRF seasonal rain forecasts improved both in magnitude and in forecasting the onset of the growing season. This was indicated by the accumulated absolute maize yield error which factored in a miss of onset of the growing season by each model. WRF outperformed CFSv2 for maize and sorghum yield forecasts in 6, 6, and 8 (out of 8) seasons in Karoi, Masvingo, and Gweru sites respectively. WRF forced crop simulations reduced mean absolute percent error of maize yield by 12.2% and sorghum yield by 9.3 % from CFSv2 forced simulations. Our results also show that maize will be more productive and less risky at Karoi and Masvingo and sorghum at the Gweru site. In our view, there should be no farming of both maize and sorghum at Beitbridge due to the high risk of crop failure unless a proper irrigation system is in place.</p>

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