Harnessing seasonal GCM forecasts for crop yield forecasting through multivariate forecast post-processing methods

Schepen, Andrew David (2019) Harnessing seasonal GCM forecasts for crop yield forecasting through multivariate forecast post-processing methods. PhD thesis, James Cook University.

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Abstract

Seasonal climate forecasts may be coupled with crop models to provide quantitative forecasts of crop yield, assess sensitivity to farm management decisions and manage risk associated with seasonal climate variability. Today, seasonal climate forecasts are produced by computationally expensive, physically-based global climate models, which capture large-scale climate patterns well. However, their coarse spatial resolution (typically >50km) means they do not reliably depict daily weather at sub-grid locations, limiting their direct use in crop models. Consequently, operational crop forecasting systems in Australia typically use alternative meteorological forcings such as historical climate analogues based on El Niño - Southern Oscillation phases, which may be less skillful than global climate model forecasts.

An emerging tactic for coupling global climate model forecasts and crop models is to apply quantile mapping (otherwise known as cumulative distribution function matching) to adjust forecast ensemble members according to the historical distribution of observations. However, quantile mapping assumes the global climate model forecasts are highly skilful and well-behaved (which they are often not). The overly simplistic formulation of quantile-mapping propagates an assortment of model errors. Additionally, quantile-mapping cannot be used for downscaling to multiple sub-grid locations owing to its deterministic nature. Accordingly, an increasing number of studies are reporting negative results arising from coupling global climate model forecasts and crop models using quantile mapping. Hence, the overarching objective of this thesis is to develop more robust, spatially and temporally relevant post-processing methods to harness global climate model forecasts for use in crop models. To this end, I develop a new multivariate forecast post-processing workflow that combines Bayesian parametric methods and non-parametric methods to calibrate and downscale global climate model forecasts for use in crop models.

Forecast calibration means to

(1) minimise systematic error such as forecast bias,

(2) ensure forecast uncertainty is reliably conveyed by ensemble spread, and

(3) ensure forecasts are at least as skilful as climatology.

Downscaling means, depending on the context, either:

(1) producing a revised forecast with the correct local weather variability at a spatial scale smaller than the GCM grid

(2) producing a local forecast based on large-scale climate drivers (e.g. sea surface temperature patterns) (this approach is also referred to as bridging), or

(3) spatial or temporal disaggregation of a forecast.

Crop forecasting models require physically-coherent inputs of rainfall, temperature and solar radiation. Previous research has established the suitability of the Bayesian joint probability modelling approach for calibrating monthly and three-monthly rainfall forecasts from global climate models. The Bayesian joint probability modelling approach has not previously been applied to post-process temperature or solar radiation forecasts or to post-process multivariate forecasts. However, it is formed on the general assumption that the joint distribution of two or more variables can be modelled as a multivariate normal distribution in transformed space. It can theoretically be extended for multivariate forecast post-processing with a relevant transformation for each variable. Thus the first objective of this thesis is to develop and evaluate several strategies for calibrating multivariate global climate model forecasts using the Bayesian joint probability modelling approach. Three strategies are compared: (1) simultaneous calibration of multiple climate variables in a single statistical model, which explicitly models inter-variable dependence via the covariance matrix; (2) univariate calibration coupled with an empirical ensemble reordering method (the Schaake Shuffle) that injects inter-variable dependence from historical data; and (3) quantile-mapping, which borrows inter-variable dependence from the raw forecasts. Applied to Australian seasonal (three-month) forecasts from the European Centre for Medium-range Weather Forecasts System4 model, univariate calibration paired with the Schaake Shuffle performs best in terms of univariate and multivariate forecast verification metrics. Direct multivariate calibration is the second-best method, with its far superior performance in in-sample testing vanishing in cross-validation, likely because of insufficient data to reliably infer the sizeable covariance matrix. Bayesian joint probability post-processing is confirmed to outperform quantile-mapping. Hence the Bayesian joint probability modelling approach and the Schaake Shuffle should, therefore, be preferred to quantile-mapping as a basis for calibrating GCM forecasts for crop forecasting applications.

Global climate model forecast skill is best captured by post-processing on seasonal time scales. However, crop models require daily forecast sequences. Also, it is observed that some operational crop forecasting systems run separate crop models for multiple locations within a region and then aggregate the results into a regional forecast. Therefore, spatial forecasts are also needed. Accordingly, the second objective of this thesis is to develop and evaluate downscaling and disaggregation methods for post-processing global climate model forecasts to higher spatial and temporal resolutions. To this end, I develop an empirical multivariate downscaling method that imparts observed spatial, temporal and inter-variable relationships into disaggregated forecasts whilst completely preserving the joint distribution of forecasts post-processed at coarser spatial and/or temporal scales. Specifically, a Euclidean distance metric is devised to identify a nearest-neighbour in historical observations for each forecast ensemble member. The method of fragments is subsequently applied to simultaneously disaggregate the forecast spatial and temporally. The new method is demonstrated to perform well for downscaling skilful forecasts of rainfall, temperature and solar radiation for six locations in northeast Australia. The climatological distributions of the downscaled forecasts mirror observations and the observed frequency of wet days is also reproduced in forecasts. The new downscaling method is a step towards full integration of calibrated seasonal climate forecasts into crop models and has a significant advantage over quantile-mapping in that it can be applied for multiple sub-grid locations.

The final objective of this thesis is to feed global climate model forecasts, post-processed using the new methods, to a crop decision support system to demonstrate an end-to-end solution for linking global climate model forecasts with a crop model to produce yield forecasts. The first crop forecasting application of the new methods is for sugarcane yield forecasting in Tully. The region is selected because it is a non-irrigated region, and it is thus suitable for assessing the value of climate forecasts. Two sets of post-processed forecasts are produced for the Tully Mill weather station in North-east Queensland. The first set is obtained by applying the Bayesian joint probability modelling approach to calibrate monthly rainfall, temperature and solar radiation forecasts for the grid cell containing Tully. The second set is obtained by using global climate model forecasts of the Niño 3.4 climate index (commonly associated with the El Niño Southern Oscillation), also using the Bayesian joint probability modelling approach, to produce local forecasts of monthly rainfall, temperature and solar radiation. In both cases, the monthly forecasts are subjected to the Schaake Shuffle and subsequently downscaled to daily sequences using identical methods. The calibration and bridging forecasts are used to drive a sugarcane crop model to generate long-lead forecasts of biomass in north-eastern Australia from 1982-2016. A rigorous probabilistic assessment of forecast attributes suggests that the calibration forecasts provide the most skilful forecasts overall although the bridging forecasts give more skilful yield forecasts at certain times. The biomass forecasts are unbiased and reliable for short to long lead times, suggesting that the new downscaling methods are effective.

My end-to-end solution for linking global climate model forecasts and crop models enables quantitative modelling and risk management at the farm level. It has the potential to improve farm productivity and profitability through better decisions. Future research should investigate the value of the post-processing methods for a wide range of crops.

Item ID: 61205
Item Type: Thesis (PhD)
Keywords: operational crop forecasting, global climate model forecasts, general circulation model (GCM), post-processing GCM forecasts, seasonal forecasts, sugarcane yield
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Copyright Information: Copyright © 2019 Andrew David Schepen.
Additional Information:

One publication arising from this thesis is stored in ResearchOnline@JCU, at the time of processing. Please see the Related URLs field. The publication is:

Appendix A: Schepen, Andrew, Wang , Q.J., and Everingham, Yvette (2016) Calibration, bridging and merging to improve GCM seasonal temperature forecasts in Australia. Monthly Weather Review, 144. pp. 2421-2441.

Date Deposited: 17 Dec 2019 05:29
FoR Codes: 04 EARTH SCIENCES > 0401 Atmospheric Sciences > 040104 Climate Change Processes @ 40%
04 EARTH SCIENCES > 0401 Atmospheric Sciences > 040107 Meteorology @ 40%
07 AGRICULTURAL AND VETERINARY SCIENCES > 0703 Crop and Pasture Production > 070301 Agro-ecosystem Function and Prediction @ 20%
SEO Codes: 96 ENVIRONMENT > 9603 Climate and Climate Change > 960303 Climate Change Models @ 40%
82 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 8203 Industrial Crops > 820304 Sugar @ 20%
96 ENVIRONMENT > 9602 Atmosphere and Weather > 960203 Weather @ 40%
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