Accurate and reliable seasonal climate forecasts are frequently sought by climate-sensitive sectors to support decision-making under climate variability and change. Temperature trend is discernible globally over the past decades, but seasonal forecasts produced by a global climate model (GCM) generally underestimate such trend. Current statistical methods used for calibrating seasonal climate forecasts mostly do not explicitly account for climate trends. Consequently, the calibrated forecasts also fail to capture the observed trend. Solving this problem can enhance user confidence in seasonal climate forecasts. In this study, we extend the capability of the Bayesian joint probability (BJP) modelling approach for statistical calibration of seasonal climate forecasts. A trend component is introduced into the BJP algorithm for embedding the observed trend into calibrated ensemble forecasts. We apply the new model (named BJP-t) to three test stations in Australia. Seasonal forecasts of daily maximum temperatures from the SEAS5 model, operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), are calibrated and evaluated. The BJP-t calibrated ensemble forecasts can reproduce the observed trend, when the raw ensemble forecasts and the BJP calibrated ensemble forecasts both fail to do so. The BJP-t calibration leads to more skilful, more reliable and sharper forecasts than the BJP calibration.
For managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast post-processing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical post-processing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this trend-aware forecast post-processing methodology to carefully treat the trend uncertainty associated with the sampling variability due to limited data records. This new methodology is validated on forecasting seasonal averages of daily maximum and minimum temperatures for Australia based on the SEAS5 climate model of the European Centre for Medium-Range Weather Forecasts. The resulting post-processed forecasts are shown to have proper trends embedded, leading to greater accuracy in regions with significant trends. The application of this new forecast post-processing is expected to boost user confidence in seasonal climate forecasts.
Climate trends have been observed over the recent decades in many parts of the world, but current global climate models (GCMs) for seasonal climate forecasting often fail to capture these trends. As a result, model forecasts may be biased above or below the trendline. In our previous research, we developed a trend-aware forecast post-processing method to overcome this problem. The method was demonstrated to be effective for embedding observed trends into seasonal temperature forecasts. In this study, we further develop the method for post-processing GCM seasonal precipitation forecasts. We introduce new formulation and evaluation features to cater for special characteristics of precipitation amounts, such as having a zero lower bound and highly positive skewness. We apply the improved method to calibrate ECMWF SEAS5 forecasts of seasonal precipitation for Australia. Our evaluation shows that the calibrated forecasts reproduce observed trends over the hindcast period of 36 years. In some regions where observed trends are statistically significant, forecast skill is greatly improved by embedding trends into the forecasts. In most regions, the calibrated forecasts outperform the raw forecasts in terms of bias, skill, and reliability. Wider applications of the new trend-aware post-processing method are expected to boost user confidence in seasonal precipitation forecasts.
Minimizing the risks associated with climate change by strategic investment in adaptation requires a spatially detailed understanding of future local changes in climate (Marengo & AmBrizzi, 2006;Wilby et al., 2004). Local climate change information is typically obtained by downscaling coarse resolution (50-250 km grid spacing) global climate models (GCMs) run with different emission scenarios (Van Vuuren et al., 2011). Due to substantial biases in GCMs (Collins et al., 2013;Raju & Kumar, 2020), analyzing large ensembles with one model, or using multiple models is common inferences (Semenov & Stratonovitch, 2010). Similarly, an ensemble of downscaled GCMs is required to assess the uncertainty of the projected climate change and its potential impacts at the local scale (Graham et al., 2007).
Dynamical downscaling, and machine learning (ML) based techniques have been widely applied to downscale global climate models and reanalyses to a finer spatiotemporal scale, but the relative performance of these two methods remains unclear. We implement an ML regression approach using a multi-layer perceptron (MLP) with a novel loss function to downscale coarse-resolution precipitation from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia from grids of 12-48 km to 5 km, using the Australia Gridded Climate Data observations as the target. A separate MLP is developed for each coarse grid to predict the fine grid values within it, by combining coarse-scale time-varying meteorological variables with fine-scale static surface properties as predictors. The resulting predictions (on out-of-sample test periods) are more accurate than dynamical downscaling in capturing the rainfall climatology, as well as the frequency distribution and spatiotemporal variability of daily precipitation, reducing biases in daily extremes by 15-85% with 12-km prediction fields. When prediction fields are coarsened, the skill of the MLP decreases—at 24 km relative bias increases by ~ 10%, and at 48 km it increases by another ~ 4%—but skill remains comparable to or, for some metrics, much better than dynamical downscaling. These results show that ML-based downscaling benefits from higher-resolution driving data but can still improve on dynamical downscaling (and at far less computational cost) when downscaling from a global climate model grid of ~50 km.
Skilful subseasonal forecasts are crucial for issuing early warnings of extreme weather events, such as heatwaves and floods. Operational subseasonal climate forecasts are often produced by global climate models not dissimilar to seasonal forecast models, which typically fail to reproduce observed temperature trends. In this study, we identify that the same issue exists in the subseasonal forecasting system. Subsequently, we adapt a trend-aware forecast postprocessing method, previously developed for seasonal forecasts, to calibrate and correct the trend in subseasonal forecasts. We modify the method to embed 30-year climate trends into the calibrated forecasts even when the available hindcast period is shorter. The use of 30-year trends is to robustly represent long-term climate changes and overcome the problem that trends inferred from a shorter period may be subject to large sampling variability. Calibration is applied to 20-year ECMWF subseasonal forecasts and AWAP observations of Australian minimum and maximum temperatures with forecast horizons of up to 4 weeks. Relative to day-of-year climatology, raw week-1 forecasts reproduce temperature trends of the 20-year observations in many regions while raw week-4 forecasts do not exhibit the 20-year observed trends. After trendaware postprocessing, the behaviour of forecast trends is related to raw forecast skill regarding accuracy. Calibrated week-1 forecasts show apparent trends consistent with the 20-year observations, as the calibration transfers forecast skill and embeds the 20-year observed trends into the forecasts when raw forecasts are inherently skilful. In contrast, calibrated week-4 forecasts exhibit the 30-year observed trends, as the calibration reverts the forecasts to the 30-year observed climatology with trends when raw forecasts have little skill. For both weeks, the trend-aware calibrated forecasts are more reliable, and as skilful as or more skilful than raw forecasts. The extended trend-aware method can be applied to deliver high-quality subseasonal forecasts and support decisionmaking in a changing climate.
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