James C. Bennett

Performance of downscaled regional climate simulations using a variable‐resolution regional climate model: Tasmania as a test case

[1] In this study we develop methods for dynamically downscaling output from six general circulation models (GCMs) for two emissions scenarios using a variable-resolution atmospheric climate model. The use of multiple GCMs and emissions scenarios gives an estimate of model range in projected changes to the mean climate across the region. By modeling the atmosphere at a very fine scale, the simulations capture processes that are important to regional weather and climate at length scales that are subgrid scale for the host GCM. We find that with a multistaged process of increased resolution and the application of bias adjustment methods, the ability of the simulation to reproduce observed conditions improves, with greater than 95% of the spatial variance explained for temperature and about 90% for rainfall. Furthermore, downscaling leads to a significant improvement for the temporal distribution of variables commonly used in applied analyses, reproducing seasonal variability in line with observations. This seasonal signal is not evident in the GCMs. This multistaged approach allows progressive improvement in the skill of the simulations in order to resolve key processes over the region with quantifiable improvements in the correlations with observations.

Reliable long‐range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model

We present a new streamflow forecasting system called forecast guided stochastic scenarios (FoGSS). FoGSS makes use of ensemble seasonal precipitation forecasts from a coupled ocean-atmosphere general circulation model (CGCM). The CGCM forecasts are post-processed with the method of calibration, bridging and merging (CBaM) to produce ensemble precipitation forecasts over river catchments. CBaM corrects biases and removes noise from the CGCM forecasts, and produces highly reliable ensemble precipitation forecasts. The post-processed CGCM forecasts are used to force the Wapaba monthly rainfall-runoff model. Uncertainty in the hydrological modelling is accounted for with a 3-stage error model. Stage 1 applies the log-sinh transformation to normalize residuals and homogenize their variance; Stage 2 applies a conditional bias-correction to correct biases and help remove negative forecast skill; Stage 3 applies an autoregressive model to improve forecast accuracy at short lead-times and propagate uncertainty through the forecast. FoGSS generates ensemble forecasts in the form of time series for the coming 12-months. In a case study of two catchments, FoGSS produces reliable forecasts at all lead-times. Forecast skill with respect to climatology is evident to lead-times of about 3 months. At longer lead-times, forecast skill approximates that of climatology forecasts; that is, forecasts become like stochastic scenarios. Because forecast skill is virtually never negative at long lead-times, forecasts of accumulated volumes can be skillful. Forecasts of accumulated 12-month streamflow volumes are significantly skillful in several instances, and ensembles of accumulated volumes are reliable. We conclude that FoGSS forecasts could be highly useful to water managers. This article is protected by copyright. All rights reserved.

Improving Precipitation Forecasts by Generating Ensembles through Postprocessing

AbstractThis paper evaluates a postprocessing method for deterministic quantitative precipitation forecasts (raw QPFs) from a numerical weather prediction model. The postprocessing aims to produce calibrated QPF ensembles that are bias free, more accurate than raw QPFs, and reliable for use in streamflow forecasting applications. The method combines a simplified version of the Bayesian joint probability (BJP) modeling approach and the Schaake shuffle. The BJP modeling approach relates raw QPFs and observed precipitation by modeling their joint distribution. It corrects biases in the raw QPFs and generates ensemble forecasts that reflect the uncertainty in the raw QPFs. The BJP modeling approach is applied to each lead time and each forecast location separately. The Schaake shuffle is then employed to produce calibrated QPFs with appropriate space–time correlations by linking ensemble members generated by the BJP modeling approach.Calibrated QPFs are produced for 10 Australian catchments that cover a wide ...

How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts

AbstractGCMs are used by many national weather services to produce seasonal outlooks of atmospheric and oceanic conditions and fluxes. Postprocessing is often a necessary step before GCM forecasts can be applied in practice. Quantile mapping (QM) is rapidly becoming the method of choice by operational agencies to postprocess raw GCM outputs. The authors investigate whether QM is appropriate for this task. Ensemble forecast postprocessing methods should aim to 1) correct bias, 2) ensure forecasts are reliable in ensemble spread, and 3) guarantee forecasts are at least as skillful as climatology, a property called “coherence.” This study evaluates the effectiveness of QM in achieving these aims by applying it to precipitation forecasts from the POAMA model. It is shown that while QM is highly effective in correcting bias, it cannot ensure reliability in forecast ensemble spread or guarantee coherence. This is because QM ignores the correlation between raw ensemble forecasts and observations. When raw foreca...

Calibrating hourly rainfall-runoff models with daily forcings for streamflow forecasting applications in meso-scale catchments

The absence of long sub-daily rainfall records can hamper development of continuous streamflow forecasting systems run at sub-daily time steps. We test the hypothesis that simple disaggregation of daily rainfall data to hourly data, combined with hourly streamflow data, can be used to establish efficient hourly rainfall-runoff models. The approach is tested on four rainfall-runoff models and a range of meso-scale catchments (150-3500?km2). We also compare our disaggregation approach to a method of parameter scaling that attains an hourly parameter-set from daily data.Simple disaggregation of daily rainfall produces hourly streamflow models that perform almost as well as those developed from hourly rainfall data. Rainfall disaggregation performs at least as well as parameter scaling, and often better. For the catchments and models we test, simple disaggregation is a very straightforward and effective way to establish hydrological models for continuous sub-daily streamflow forecasting systems when sub-daily rainfall data are unavailable. Daily rainfall is disaggregated to hourly to calibrate hourly hydrological models.Models perform almost as well as models calibrated with observed hourly rainfall.Disaggregation performed at least as well as parameter scaling.A way to develop hourly river forecast systems with daily rainfall.

A regional response in mean westerly circulation and rainfall to projected climate warming over Tasmania, Australia

Coupled ocean–atmosphere general circulation models (GCMs) lack sufficient resolution to model the regional detail of changes to mean circulation and rainfall with projected climate warming. In this paper, changes in mean circulation and rainfall in GCMs are compared to those in a variable resolution regional climate model, the Conformal Cubic Atmospheric Model (CCAM), under a high greenhouse gas emissions scenario. The study site is Tasmania, Australia, which is positioned within the mid-latitude westerlies of the southern hemisphere. CCAM projects a different response in mean sea level pressure and mid-latitude westerly circulation to climate warming to the GCMs used as input, and shows greater regional detail of the boundaries between regions of increasing and decreasing rainfall. Changes in mean circulation dominate the mean rainfall response in western Tasmania, whereas changes to rainfall in the East Coast are less related to mean circulation changes. CCAM projects an amplification of the dominant westerly circulation over Tasmania and this amplifies the seasonal cycle of wet winters and dry summers in the west. There is a larger change in the strength than in the incidence of westerly circulation and rainfall events. We propose the regional climate model displays a more sensitive atmospheric response to the different rates of warming of land and sea than the GCMs as input. The regional variation in these results highlight the need for dynamical downscaling of coupled general circulation models to finely resolve the influence of mean circulation and boundaries between regions of projected increases and decreases in rainfall.

Improved regional climate modelling through dynamical downscaling

Coupled Ocean-Atmosphere General Circulation Models (GCMs) provide the best estimates for assessing potential changes to our climate on a global scale out to the end of this century. Because coupled GCMs have a fairly coarse resolution they do not provide a detailed picture of climate (and climate change) at the local scale. Tasmania, due to its diverse geography and range of climate over a small area is a particularly difficult region for drawing conclusions regarding climate change when relying solely on GCMs. The foundation of the Climate Futures for Tasmania project is to take the output produced by multiple GCMs, using multiple climate change scenarios, and use this output as input into the Conformal Cubic Atmospheric Model (CCAM) to downscale the GCM output. CCAM is a full atmospheric global general circulation model, formulated using a conformal-cubic grid that covers the globe but can be stretched to provide higher resolution in the area of interest (Tasmania). By modelling the atmosphere at a much finer scale than is possible using a coupled GCM we can more accurately capture the processes that drive Tasmanias weather/climate, and thus can more clearly answer the question of how Tasmanias climate will change in the future. We present results that show the improvements in capturing the local-scale climate and climate drivers that can be achieved through downscaling, when compared to a gridded observational data set. The underlying assumption of this work is that a better simulated current climatology will also produce a more credible climate change signal.