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

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

On the joint calibration of multivariate seasonal climate forecasts from GCMs

Multivariate seasonal climate forecasts are increasingly required for quantitative modeling in support of natural resources management and agriculture. GCM forecasts typically require postprocessing to reduce biases and improve reliability; however, current seasonal postprocessing methods often ignore multivariate dependence. In low-dimensional settings, fully parametric methods may sufficiently model intervariable covariance. On the other hand, empirical ensemble reordering techniques can inject desired multivariate dependence in ensembles from template data after univariate postprocessing. To investigate the best approach for seasonal forecasting, this study develops and tests several strategies for calibrating seasonal GCM forecasts of rainfall, minimum temperature, and maximum temperature with intervariable dependence: 1) simultaneous calibration of multiple climate variables using the Bayesian joint probability modeling approach; 2) univariate BJP calibration coupled with an ensemble reordering method (the Schaake shuffle); and 3) transformation-based quantile mapping, which borrows intervariable dependence from the raw forecasts. Applied to Australian seasonal forecasts from the ECMWF System4 model, univariate calibration paired with empirical ensemble reordering performs best in terms of univariate and multivariate forecast verification metrics, including the energy and variogram scores. However, the performance of empirical ensemble reordering using the Schaake shuffle is influenced by the selection of historical data in constructing a dependence template. 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 relative to the number of parameters. The continued development of multivariate forecast calibration methods will support the uptake of seasonal climate forecasts in complex application domains such as agriculture and hydrology

Application of a Hybrid Statistical–Dynamical System to Seasonal Prediction of North American Temperature and Precipitation

Recent research demonstrates that dynamical models sometimes fail to represent observed teleconnection patterns associated with predictable modes of climate variability. As a result, model forecast skill may be reduced. We address this gap in skill through the application of a Bayesian postprocessing technique—the calibration, bridging, and merging (CBaM) method—which previously has been shown to improve probabilistic seasonal forecast skill over Australia. Calibration models developed from dynamical model reforecasts and observations are employed to statistically correct dynamical model forecasts. Bridging models use dynamical model forecasts of relevant climate modes (e.g., ENSO) as predictors of remote temperature and precipitation. Bridging and calibration models are first developed separately using Bayesian joint probability modeling and then merged using Bayesian model averaging to yield an optimal forecast. We apply CBaM to seasonal forecasts of North American 2-m temperature and precipitation from the North American Multimodel Ensemble (NMME) hindcast. Bridging is done using the model-predicted Niño-3.4 index. Overall, the fully merged CBaM forecasts achieve higher Brier skill scores and better reliability compared to raw NMME forecasts. Bridging enhances forecast skill for individual NMME member model forecasts of temperature, but does not result in significant improvements in precipitation forecast skill, possibly because the models of the NMME better represent the ENSO–precipitation teleconnection pattern compared to the ENSO– temperature pattern. These results demonstrate the potential utility of the CBaM method to improve seasonal forecast skill over North America

An improved workflow for calibration and downscaling of GCM climate forecasts for agricultural applications – a case study on prediction of sugarcane yield in Australia

Seasonal climate forecasts can improve the accuracy of early-season estimates of crop yield and influence seasonal crop management decisions. Climate forecasting centres around the globe now routinely run global climate models (GCMs) to provide ensemble forecasts. However, raw GCM forecasts require post-processing to improve their reliability and to enable systematic integration with crop models. Post-processing to meet crop model input requirements is highly challenging and simple bias-correction methods can perform poorly in this regard. As a result of the difficulties, GCM forecasts are often sidelined in favour of other inputs such as climate analogues. In this study, we evaluate two variants of a recently-developed post-processing method designed to systematically and reliably calibrate and downscale GCM forecasts for use in crop models. In one variant, local GCM forecasts of rainfall, temperature and solar radiation are post-processed directly. The second variant is a novel adaption in which the predictive input is instead the GCM's forecast of a large-scale climate pattern, in this case related to the El Nino-Southern Oscillation. The post-processed climate forecasts, which are in the form of ensemble time series, are used to drive an APSIM-sugar model to generate long-lead forecasts of biomass in north-eastern Australia from 1982 to 2016. A rigorous probabilistic assessment of forecast attributes suggests that local GCM forecast calibration provides the most skilful forecasts overall although the ENSO-related forecasts give more skilful biomass forecasts at certain times, implying model combination could be worthwhile to maximise skill. The generated biomass forecasts are unbiased and reliable for short to long lead times, suggesting that the downscaling methods will be of value to trial in a range of crop forecast applications, and support the quantitative, meaningful use of GCM forecasts in agriculture

Calibration, bridging and merging to improve GCM seasonal temperature forecasts in Australia

There are a number of challenges that must be overcome if GCM forecasts are to be widely adopted in climate-sensitive industries such as agriculture and water management. GCM outputs are frequently biased relative to observations and their ensembles are unreliable in conveying uncertainty through appropriate spread. The calibration, bridging, and merging (CBaM) method has been shown to be an effective tool for postprocessing GCM rainfall forecasts to improve ensemble forecast attributes. In this study, CBaM is modified and extended to postprocess seasonal minimum and maximum temperature forecasts from the POAMA GCM in Australia. Calibration is postprocessing GCM forecasts using a statistical model. Bridging is producing additional forecasts using statistical models that have other GCM output variables (e.g., SST) as predictors. It is demonstrated that merging calibration and bridging forecasts through CBaM effectively improves the skill of POAMA seasonal minimum and maximum temperature forecasts for Australia. It is demonstrated that CBaM produces bias-corrected forecasts that are reliable in ensemble spread and reduces forecasts to climatology when there is no evidence of forecasting skill. This work will help enable the adoption of GCM forecasts by climate-sensitive industries for quantitative modeling and decision-making

Lead time and skill of Australian wheat yield forecasts based on ENSO-analogue or GCM-derived seasonal climate forecasts – A comparative analysis

Foresight of crop yield is fundamental to producers and industry to better manage climate risks and mitigate ebbs and troughs in crop production. Rain-fed grain production in Australia is highly volatile and producers and industry are progressively confronted with projected uncertainties due to climate variability and change, input costs and market prices. Thus, having advance knowledge of the likely impact of the coming season's climate on crop yield and production is critical for decisions across the supply chain. Here we explore and analyse the lead time and skill of a wheat yield forecasting system using a biophysical crop yield simulation model connected to either a statistical ENSO-analogue climate forecasting system or a dynamic general circulation model (GCM) derived climate forecasting system. The comparative skill was investigated for 16 wheat producing districts (shires) of the broad Australian winter cropping region, each containing 9–35 irregularly-spaced simulation points associated with climate stations. Both the ENSO-analogue and GCM-derived systems produced reliable wheat yield forecasts with the GCM-based approach having general improved skill, and particularly during the early months of the season (March to May) before sowing. The shift in the forecast yield distributions relative to the climatology-based yield distribution were dependent on location and time in the season, with the GCM-derived forecast shifts more widespread and earlier in the season. Overall, the GCM-based climate/crop forecasting system showed a significant improvement in lead time (greater than two months before the normal planting time of wheat), across the Australian wheat belt. This result demonstrates an avenue for improved efficacy in future commodity forecasting frameworks via likely enhanced relevance and utility to industry associated with the use of GCM-derived approaches