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

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.

Download Full-text

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

Geoscientific Model Development ◽

10.5194/gmd-8-3987-2015 ◽

2015 ◽

Vol 8 (12) ◽

pp. 3987-3997 ◽

Cited By ~ 5

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.

Download Full-text

On the Use of Reanalysis Data for Downscaling

Journal of Climate ◽

10.1175/jcli-d-11-00251.1 ◽

2012 ◽

Vol 25 (7) ◽

pp. 2517-2526 ◽

Cited By ~ 55

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.

Download Full-text

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

Atmosphere ◽

10.3390/atmos12030349 ◽

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.

Download Full-text

A Reduced Radiation Grid for the ECMWF Integrated Forecasting System

Monthly Weather Review ◽

10.1175/2008mwr2590.1 ◽

2008 ◽

Vol 136 (12) ◽

pp. 4760-4772 ◽

Cited By ~ 29

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.

Download Full-text

Downscaling global seasonal weather forecasts for crop yield forecasting over Zimbabwe

10.5194/ems2021-402 ◽

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>

Download Full-text

Estimation of the Impact of Sampling Errors in the VOS Observations on Air–Sea Fluxes. Part I: Uncertainties in Climate Means

Journal of Climate ◽

10.1175/jcli4010.1 ◽

2007 ◽

Vol 20 (2) ◽

pp. 279-301 ◽

Cited By ~ 48

Author(s):

Sergey Gulev ◽

Thomas Jung ◽

Eberhard Ruprecht

Keyword(s):

Heat Flux ◽

Data Assimilation ◽

Random Sampling ◽

Specific Humidity ◽

Global Ocean ◽

Sampling Errors ◽

Seasonal Forecasts ◽

Synoptic Variability ◽

Individual Variables ◽

The Impact

Abstract Sampling uncertainties in the voluntary observing ship (VOS)-based global ocean–atmosphere flux fields were estimated using the NCEP–NCAR reanalysis and ECMWF 40-yr Re-Analysis (ERA-40) as well as seasonal forecasts without data assimilation. Air–sea fluxes were computed from 6-hourly reanalyzed individual variables using state-of-the-art bulk formulas. Individual variables and computed fluxes were subsampled to simulate VOS-like sampling density. Random simulation of the number of VOS observations and simulation of the number of observations with contemporaneous sampling allowed for estimation of random and total sampling uncertainties respectively. Although reanalyses are dependent on VOS, constituting an important part of data assimilation input, it is assumed that the reanalysis fields adequately reproduce synoptic variability at the sea surface. Sampling errors were quantified by comparison of the regularly sampled (i.e., 6 hourly) and subsampled monthly fields of surface variables and fluxes. In poorly sampled regions random sampling errors amount to 2.5°–3°C for air temperature, 3 m s−1 for the wind speed, 2–2.5 g kg−1 for specific humidity, and 15%–20% of the total cloud cover. The highest random sampling errors in surface fluxes were found for the sensible and latent heat flux and range from 30 to 80 W m−2. Total sampling errors in poorly sampled areas may be higher than random ones by 60%. In poorly sampled subpolar latitudes of the Northern Hemisphere and throughout much of the Southern Ocean the total sampling uncertainty in the net heat flux can amount to 80–100 W m−2. The highest values of the uncertainties associated with the interpolation/extrapolation into unsampled grid boxes are found in subpolar latitudes of both hemispheres for the turbulent fluxes, where they can be comparable with the sampling errors. Simple dependencies of the sampling errors on the number of samples and the magnitude of synoptic variability were derived. Sampling errors estimated from different reanalyses and from seasonal forecasts yield qualitatively comparable spatial patterns, in which the actual values of uncertainties are controlled by the magnitudes of synoptic variability. Finally, estimates of sampling uncertainties are compared with the other errors in air–sea fluxes and the reliability of the estimates obtained is discussed.

Download Full-text

Simulation of growth and yield of rainfed maize under varied agronomic management and changing climatic scenario in Nawalparasi, Nepal

Journal of Maize Research and Development ◽

10.3126/jmrd.v1i1.14250 ◽

2015 ◽

Vol 1 (1) ◽

pp. 123-133

Author(s):

Umesh Shrestha ◽

Lal Prasad Amgain ◽

Tika Bahadur Karki ◽

Khem Raj Dahal

Keyword(s):

Climate Change ◽

Grain Yield ◽

Maize Yield ◽

Sowing Date ◽

Growth And Yield ◽

Unit Weight ◽

Change Scenario ◽

Maize Cultivars ◽

Spring Maize ◽

The Impact

Correction: Figure 3 was corrupted and so the PDF was replaced on 29th December 2016 with the corrected Figure 3.A field experiment and simulation modeling study in combination for different maize cultivars planted at different sowing dates were accomplished at Kawasoti-5, Nawalparasi during spring season of 2013 to assess the impact of climate change scenario as predicted by IPCC in rainfed spring maize by using CSM-CERES-Maize model. Result showed that RML-4/RML-17 produced higher kernel rows/ ear (13.77), kernel per row (30.42) and test weight (244.9 g). Significantly higher grain yield was also found for RML-4/RML-17 (6.03 t/ha) compared to Poshilo makai-1 (4.73 t/ha), Arun-2 (3.55 t/ha) and Local (2.92 t/ha). Earlier sowing date (7th April) actually produced higher kernel/row (27.97), kernel rows/ear (12.89) and 1000 grain weight (230 g). Significantly higher grain yield (5.13t/ha) was obtained in earlier sowing date (7th April). The CSM-CERES-Maize model was calibrated and found well validated with days to anthesis (RMSE= 0.426 day and D-index= 0.998), days to physiological maturity (RMSE=0.674 day and D-index= 0.999), number of grain/m2 at maturity (RMSE= 85.287 grain /m2 and D-index= 0.993), unit weight at maturity (RMSE=0.012 g/kernel and D-index= 0.854) and grain yield (RMSE=54.94 kg/ha and D-index= 1.00). The model was found sensitive to climate change parameters. The sensitivity for various climate change parameter indicated that there was severely decreased trend in simulated rainfed spring maize yield with the increment of maximum and minimum temperature, decrease in solar radiation and decrease carbondioxide concentration. Even 2°C rise in temperature can decrease around 15-20% yield of spring maize and this negative effect was even more pronounced in hybrid than other cultivars.Journal of Maize Research and Development (2015) 1(1):123-133DOI: http://dx.doi.org/10.5281/zenodo.34289

Download Full-text

Impact Study of FY-3B MWRI Data Assimilation in WRFDA

Atmosphere ◽

10.3390/atmos12040497 ◽

2021 ◽

Vol 12 (4) ◽

pp. 497

Author(s):

Chun Yang ◽

Lijian Zhu ◽

Jinzhong Min

Keyword(s):

Wind Speed ◽

Data Assimilation ◽

Three Dimensional ◽

Analysis Data ◽

Specific Humidity ◽

Sea Level Pressure ◽

Weather Forecasts ◽

Warm Core ◽

Radiance Data ◽

The Impact

In the first attempt to configure the Fengyun-3B satellite’s Microwave Radiation Imager (MWRI) radiance data in the Weather Research Forecast (WRF) model’s Data Assimilation system (WRFDA), the impact of MWRI data assimilation on the analysis and forecast of Typhoon Son-Tinh in 2012 was evaluated with WRFDA’s three-dimensional variational (3DVAR) data-assimilation scheme. Compared to a benchmark experiment with no MWRI data, assimilating MWRI radiances improved the analyses of typhoon central sea level pressure (CSLP), warm core structure, and wind speed. Moreover, verified with European Center for Medium-Range Weather Forecasts (ECMWF) analysis data, significant improvements in model variable forecast, such as geopotential height and specific humidity, were produced. Substantial error reductions in track, CSLP, and maximum-wind-speed forecasts with MWRI assimilation was also obtained from analysis time to 48 h forecast.

Download Full-text

The impact of crop year and certain agrotechnical factors on maize yield

Acta Agraria Debreceniensis ◽

10.34101/actaagrar/52/2092 ◽

2013 ◽

pp. 13-16

Author(s):

Zsófia Becze

Keyword(s):

Plant Density ◽

Growing Season ◽

Maize Yield ◽

Sowing Date ◽

Development Rate ◽

Genetic Characteristics ◽

Maize Hybrids ◽

Ecological Optimum ◽

Set Up ◽

The Impact

The experiment was set up with eight maize hybrids with different genetic characteristics in 2012. In our study were included hybrids with different length of growing season. We studied the effect of NKP fertilization and plant density on the yield. Comparing to controll treatment it was found that highest yield was at N40+PK treatment. It was three times higher than agro-ecological optimum. Due to the droughty year the effect of plant density it was minimum. The development rate in case of sowing date I. and II. showed an almost identical picture in the scope of the sowing date trial. However, hybrids with excellent adaptability were capable of a yield above average even in this extreme year.

Download Full-text

Effects of the PDO Phase on the Tropical Belt Width

Journal of Climate ◽

10.1175/jcli-d-11-00244.1 ◽

2012 ◽

Vol 25 (9) ◽

pp. 3282-3290 ◽

Cited By ~ 32

Author(s):

Barbara Grassi ◽

Gianluca Redaelli ◽

Pablo Osvaldo Canziani ◽

Guido Visconti

Keyword(s):

Radiative Forcing ◽

Stratospheric Ozone ◽

Atmospheric Model ◽

Reanalysis Data ◽

Weather Forecasts ◽

Community Atmosphere Model ◽

The Pacific ◽

Belt Width ◽

The Impact ◽

Sst Anomalies

Recent studies have shown that the tropical belt (TB) has progressively expanded since at least the late 1970s. This trend has been largely attributed to the radiative forcing due to greenhouse gas (GHG) increase and stratospheric ozone depletion, even if an influence of sea surface temperature (SST) anomalies has been also suggested. The impact of the Pacific decadal oscillation (PDO) on the TB width is investigated in this work. The study is performed by using both Atmospheric Model Intercomparison Project (AMIP) and idealized simulations, produced by the NCAR Community Atmosphere Model, version 3 (CAM3) GCM and reanalysis data [40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), ERA-Interim, and Modern-Era Retrospective Analysis for Research and Applications (MERRA)]. Reanalyses show that a switch of the PDO from a positive to a negative phase can lead to a significant TB expansion during the equinoxes. This effect, indicating a possible PDO contribution to the widening that characterized the TB width during the last decades, is not correctly reproduced by model simulations. Deficiencies in the sensitivity of model-simulated convective processes to SST anomalies are suggested as a possible cause of the TB widening underestimation.

Download Full-text