Seasonal Forecasts of Australian Rainfall through Calibration and Bridging of Coupled GCM Outputs

2014 ◽  
Vol 142 (5) ◽  
pp. 1758-1770 ◽  
Author(s):  
Andrew Schepen ◽  
Q. J. Wang ◽  
David E. Robertson
Keyword(s):  
General Circulation ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
General Circulation Models ◽  
Seasonal Rainfall ◽  
Climate Indices ◽  
Seasonal Forecasts ◽  
Spatial Coverage ◽  
Validation Procedure ◽  
Systematic Biases

Abstract Coupled general circulation models (GCMs) are increasingly being used to forecast seasonal rainfall, but forecast skill is still low for many regions. GCM forecasts suffer from systematic biases, and forecast probabilities derived from ensemble members are often statistically unreliable. Hence, it is necessary to postprocess GCM forecasts to improve skill and statistical reliability. In this study, the authors compare three methods of statistically postprocessing GCM output—calibration, bridging, and a combination of calibration and bridging—as ways to treat these problems and make use of multiple GCM outputs to increase the skill of Australian seasonal rainfall forecasts. Three calibration models are established using ensemble mean rainfall from three variants of the Predictive Ocean Atmosphere Model for Australia (POAMA) version M2.4 as predictors. Six bridging models are established using POAMA forecasts of seasonal climate indices as predictors. The calibration and bridging forecasts are merged through Bayesian model averaging. Forecast attributes including skill, sharpness, and reliability are assessed through a rigorous leave-three-years-out cross-validation procedure for forecasts of 1-month lead time. While there are overlaps in skill, there are regions and seasons where the calibration or bridging forecasts are uniquely skillful. The calibration forecasts are more skillful for January–March (JFM) to June–August (JJA). The bridging forecasts are more skillful for July–September (JAS) to December–February (DJF). Merging calibration and bridging forecasts retains, and in some seasons expands, the spatial coverage of positive skill achieved by the better of the calibration forecasts and bridging forecasts individually. The statistically postprocessed forecasts show improved reliability compared to the raw forecasts.

scholarly journals Evaluation of Surface Upward Longwave Radiation in the CMIP6 Models with Ground and Satellite Observations

2021 ◽  
Vol 13 (21) ◽  
pp. 4464
Author(s):  
Jiawen Xu ◽  
Xiaotong Zhang ◽  
Chunjie Feng ◽  
Shuyue Yang ◽  
Shikang Guan ◽  
...  
Keyword(s):  
General Circulation ◽  
Bayesian Model Averaging ◽  
Mean Squared Error ◽  
Radiant Energy ◽  
Coupled Model ◽  
Model Averaging ◽  
Longwave Radiation ◽  
Energy System ◽  
General Circulation Models ◽  
The Individual

Surface upward longwave radiation (SULR) is an indicator of thermal conditions over the Earth’s surface. In this study, we validated the simulated SULR from 51 Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) through a comparison with ground measurements and satellite-retrieved SULR from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF). Moreover, we improved the SULR estimations by a fusion of multiple CMIP6 GCMs using multimodel ensemble (MME) methods. Large variations were found in the monthly mean SULR among the 51 CMIP6 GCMs; the bias and root mean squared error (RMSE) of the individual CMIP6 GCMs at 133 sites ranged from −3 to 24 W m−2 and 22 to 38 W m−2, respectively, which were higher than those found between the CERES EBAF and GCMs. The CMIP6 GCMs did not improve the overestimation of SULR compared to the CMIP5 GCMs. The Bayesian model averaging (BMA) method showed better performance in simulating SULR than the individual GCMs and simple model averaging (SMA) method, with a bias of 0 W m−2 and an RMSE of 19.29 W m−2 for the 133 sites. In terms of the global annual mean SULR, our best estimation for the CMIP6 GCMs using the BMA method was 392 W m−2 during 2000–2014. We found that the SULR varied between 386 and 393 W m−2 from 1850 to 2014, exhibiting an increasing tendency of 0.2 W m−2 per decade (p < 0.05).

scholarly journals Merging Seasonal Rainfall Forecasts from Multiple Statistical Models through Bayesian Model Averaging

2012 ◽  
Vol 25 (16) ◽  
pp. 5524-5537 ◽  
Author(s):  
Q. J. Wang ◽  
Andrew Schepen ◽  
David E. Robertson
Keyword(s):  
Statistical Models ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Seasonal Rainfall ◽  
Multiple Models ◽  
Forecast Uncertainty ◽  
Spatial Coverage ◽  
Forecast Lead Time ◽  
Seasonal Rainfall Forecasts

Abstract Merging forecasts from multiple models has the potential to combine the strengths of individual models and to better represent forecast uncertainty than the use of a single model. This study develops a Bayesian model averaging (BMA) method for merging forecasts from multiple models, giving greater weights to better performing models. The study aims for a BMA method that is capable of producing relatively stable weights in the presence of significant sampling variability, leading to robust forecasts for future events. The BMA method is applied to merge forecasts from multiple statistical models for seasonal rainfall forecasts over Australia using climate indices as predictors. It is shown that the fully merged forecasts effectively combine the best skills of the models to maximize the spatial coverage of positive skill. Overall, the skill is low for the first half of the year but more positive for the second half of the year. Models in the Pacific group contribute the most skill, and models in the Indian and extratropical groups also produce useful and sometimes distinct skills. The fully merged probabilistic forecasts are found to be reliable in representing forecast uncertainty spread. The forecast skill holds well when forecast lead time is increased from 0 to 1 month. The BMA method outperforms the approach of using a model with two fixed predictors chosen a priori and the approach of selecting the best model based on predictive performance.

scholarly journals Toward Accurate and Reliable Forecasts of Australian Seasonal Rainfall by Calibrating and Merging Multiple Coupled GCMs

2013 ◽  
Vol 141 (12) ◽  
pp. 4554-4563 ◽  
Author(s):  
Andrew Schepen ◽  
Q. J. Wang
Keyword(s):  
General Circulation ◽  
Bayesian Model Averaging ◽  
Forecast Accuracy ◽  
Joint Probability ◽  
General Circulation Models ◽  
Seasonal Rainfall ◽  
Equal Weight ◽  
Rainfall Forecasting ◽  
Probability Models ◽  
Seasonal Rainfall Forecasts

Abstract The majority of international climate modeling centers now produce seasonal rainfall forecasts from coupled general circulation models (GCMs). Seasonal rainfall forecasting is highly challenging, and GCM forecast accuracy is still poor for many regions and seasons. Additionally, forecast uncertainty tends to be underestimated meaning that forecast probabilities are statistically unreliable. A common strategy employed to improve the overall accuracy and reliability of GCM forecasts is to merge forecasts from multiple models into a multimodel ensemble (MME). The most widely used technique is to simply pool all of the forecast ensemble members from multiple GCMs into what is known as a superensemble. In Australia, seasonal rainfall forecasts are produced using the Predictive Ocean–Atmosphere Model for Australia (POAMA). In this paper, the authors demonstrate that mean corrected superensembles formed by merging forecasts from POAMA with those from three international models in the ENSEMBLES dataset remain poorly calibrated in many cases. The authors propose and evaluate a two-step process for producing MMEs. First, forecast calibration of the individual GCMs is carried out by using Bayesian joint probability models that account for parameter uncertainty. The calibration leads to satisfactory forecast reliability. Second, the individually calibrated forecasts of the GCMs are merged through Bayesian model averaging (BMA). The use of multiple GCMs results in better forecast accuracy, while maintaining reliability, than using POAMA only. Compared with using equal-weight averaging, BMA weighting produces sharper and more accurate forecasts.

scholarly journals Evaluation of Bayesian Multimodel Estimation in Surface Incident Shortwave Radiation Simulation over High Latitude Areas

2019 ◽  
Vol 11 (15) ◽  
pp. 1776 ◽  
Author(s):  
Weiyu Zhang ◽  
Xiaotong Zhang ◽  
Wenhong Li ◽  
Ning Hou ◽  
Yu Wei ◽  
...  
Keyword(s):  
High Latitude ◽  
General Circulation ◽  
Bayesian Model Averaging ◽  
Radiant Energy ◽  
Coupled Model ◽  
Model Averaging ◽  
Energy System ◽  
General Circulation Models ◽  
Shortwave Radiation ◽  
Data Set

Surface incident shortwave radiation (SSR) is crucial for understanding the Earth’s climate change issues. Simulations from general circulation models (GCMs) are one of the most practical ways to produce long-term global SSR products. Although previous studies have comprehensively assessed the performance of the GCMs in simulating SSR globally or regionally, studies assessing the performance of these models over high-latitude areas are sparse. This study evaluated and intercompared the SSR simulations of 48 GCMs participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) using quality-controlled SSR surface measurements at 44 radiation sites from three observation networks (GC-NET, BSRN, and GEBA) and the SSR retrievals from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF) data set over high-latitude areas from 2000 to 2005. Furthermore, this study evaluated the performance of the SSR estimations of two multimodel ensemble methods, i.e., the simple model averaging (SMA) and the Bayesian model averaging (BMA) methods. The seasonal performance of the SSR estimations of individual GCMs, the SMA method, and the BMA method were also intercompared. The evaluation results indicated that there were large deficiencies in the performance of the individual GCMs in simulating SSR, and these GCM SSR simulations did not show a tendency to overestimate the SSR over high-latitude areas. Moreover, the ensemble SSR estimations generated by the SMA and BMA methods were superior to all individual GCM SSR simulations over high-latitude areas, and the estimations of the BMA method were the best compared to individual GCM simulations and the SMA method-based estimations. Compared to the CERES EBAF SSR retrievals, the uncertainties of the SSR estimations of the GCMs, the SMA method, and the BMA method are relatively large during summer.

scholarly journals Analog Downscaling of Seasonal Rainfall Forecasts in the Murray Darling Basin

2013 ◽  
Vol 141 (3) ◽  
pp. 1099-1117 ◽  
Author(s):  
Andrew Charles ◽  
Bertrand Timbal ◽  
Elodie Fernandez ◽  
Harry Hendon
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Seasonal Prediction ◽  
General Circulation Models ◽  
Meridional Wind ◽  
Seasonal Forecasts ◽  
Local Climate ◽  
Continental Scale ◽  
Predictive Skill ◽  
Murray Darling Basin

Abstract Seasonal predictions based on coupled atmosphere–ocean general circulation models (GCMs) provide useful predictions of large-scale circulation but lack the conditioning on topography required for locally relevant prediction. In this study a statistical downscaling model based on meteorological analogs was applied to continental-scale GCM-based seasonal forecasts and high quality historical site observations to generate a set of downscaled precipitation hindcasts at 160 sites in the South Murray Darling Basin region of Australia. Large-scale fields from the Predictive Ocean–Atmosphere Model for Australia (POAMA) 1.5b GCM-based seasonal prediction system are used for analog selection. Correlation analysis indicates modest levels of predictability in the target region for the selected predictor fields. A single best-match analog was found using model sea level pressure, meridional wind, and rainfall fields, with the procedure applied to 3-month-long reforecasts, initialized on the first day of each month from 1980 to 2006, for each model day of 10 ensemble members. Assessment of the total accumulated rainfall and number of rainy days in the 3-month reforecasts shows that the downscaling procedure corrects the local climate variability with no mean effect on predictive skill, resulting in a smaller magnitude error. The amount of total rainfall and number of rain days in the downscaled output is significantly improved over the direct GCM output as measured by the difference in median and tercile thresholds between station observations and downscaled rainfall. Confidence in the downscaled output is enhanced by strong consistency between the large-scale mean of the downscaled and direct GCM precipitation.

scholarly journals The challenge of forecasting high streamflows 1–3 months in advance with lagged climate indices in southeast Australia

2014 ◽  
Vol 14 (2) ◽  
pp. 219-233 ◽  
Author(s):  
J. C. Bennett ◽  
Q. J. Wang ◽  
P. Pokhrel ◽  
D. E. Robertson
Keyword(s):  
Large Scale ◽  
Bayesian Model Averaging ◽  
Emergency Services ◽  
Joint Probability ◽  
Model Averaging ◽  
Climate Indices ◽  
Warning Systems ◽  
Forecast Period ◽  
Considerable Benefit ◽  
Probability Modelling

Abstract. Skilful forecasts of high streamflows a month or more in advance are likely to be of considerable benefit to emergency services and the broader community. This is particularly true for mesoscale catchments (< 2000 km2) with little or no seasonal snowmelt, where real-time warning systems are only able to give short notice of impending floods. In this study, we generate forecasts of high streamflows for the coming 1-month and coming 3-month periods using large-scale ocean–atmosphere climate indices and catchment wetness as predictors. Forecasts are generated with a combination of Bayesian joint probability modelling and Bayesian model averaging. High streamflows are defined as maximum single-day streamflows and maximum 5-day streamflows that occur during each 1-month or 3-month forecast period. Skill is clearly evident in the 1-month forecasts of high streamflows. Surprisingly, in several catchments positive skill is also evident in forecasts of large threshold events (exceedance probabilities of 25%) over the next month. Little skill is evident in forecasts of high streamflows for the 3-month period. We show that including lagged climate indices as predictors adds little skill to the forecasts, and thus catchment wetness is by far the most important predictor. Accordingly, we recommend that forecasts may be improved by using accurate estimates of catchment wetness.

scholarly journals Annual and semiannual cycles of midlatitude surface temperature and baroclinicity: reanalysis data and AOGCMs simulations

2016 ◽  
Author(s):  
Valerio Lembo ◽  
Isabella Bordi ◽  
Antonio Speranza
Keyword(s):  
Surface Temperature ◽  
General Circulation ◽  
Relative Phase ◽  
North Pacific Ocean ◽  
General Circulation Models ◽  
Reanalysis Data ◽  
Bivariate Analysis ◽  
Seasonal Forecasts ◽  
Frequency Model ◽  
Annual Frequency

Abstract. Seasonal variability of surface air temperature and baroclinicity from the ECMWF ERA-Interim (ERAI) reanalysis and six coupled atmosphere-ocean general circulation models (AOGCMs) participating in the Coupled Model Intercomparison Project phase 3 and 5 (CMIP3 and CMIP5) are examined. In particular, the annual and semiannual cycles of hemispherically averaged fields are studied using spectral analysis. The aim is to assess the ability of coupled general circulation models to properly reproduce the observed amplitude and phase of these cycles, and investigate the relationship between surface temperature and baroclinicity (coherency and relative phase) in such frequency bands. The overall results of power spectra agree in displaying a statistically significant peak at the annual frequency in the zonally averaged fields of both hemispheres. The semiannual peak, instead, shows less power and in the NH seems to have a more regional character, as is observed in the North Pacific Ocean region. Results of bivariate analysis for such a region and Southern Hemisphere midlatitudes show some discrepancies between ERAI and model data, as well as among models, especially for the semiannual frequency. Specifically: (i) the coherency at the annual and semiannual frequency observed in the reanalysis data is well represented by models in both hemispheres; (ii) at the annual frequency, estimates of the relative phase between surface temperature and baroclinicity are bounded between about &amp;pm;15° around an average value of 220° (i.e., approximately 1 month phase shift), while at the semiannual frequency model phases show a wider dispersion in both hemispheres with larger errors in the estimates, denoting increased uncertainty and some disagreement among models. The most recent CMIP climate models (CMIP5) show several improvements when compared with CMIP3 but a degree of discrepancy still persists though masked by the large errors characterizing the semiannual frequency. These findings contribute to better characterize the cyclic response of current global atmosphere-ocean models to the external (solar) forcing that is of interest for seasonal forecasts.

scholarly journals On the role of Eurasian autumn snow cover in dynamical seasonal predictions 

2021 ◽  
Author(s):  
Paolo Ruggieri ◽  
Marianna Benassi ◽  
Stefano Materia ◽  
Daniele Peano ◽  
Constantin Ardilouze ◽  
...  
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Land Surface ◽  
General Circulation ◽  
Dominant Role ◽  
General Circulation Models ◽  
The Arctic ◽  
Seasonal Forecasts ◽  
Eurasian Snow

&lt;p&gt;Seasonal climate predictions leverage on many predictable or persistent components of the Earth system that can modify the state of the atmosphere and of relant weather related variable such as temprature and precipitation. With a dominant role of the ocean, the land surface provides predictability through various mechanisms, including snow cover, with particular reference to Autumn snow cover over the Eurasian continent. The snow cover alters the energy exchange between land surface and atmosphere and induces a diabatic cooling that in turn can affect the atmosphere both locally and remotely. Lagged relationships between snow cover in Eurasia and atmospheric modes of variability in the Northern Hemisphere have been investigated and documented but are deemed to be non-stationary and climate models typically do not reproduce observed relationships with consensus. The role of Autumn Eurasian snow in recent dynamical seasonal forecasts is therefore unclear. In this study we assess the role of Eurasian snow cover in a set of 5 operational seasonal forecast system characterized by a large ensemble size and a high atmospheric and oceanic resolution. Results are compemented with a set of targeted idealised simulations with atmospheric general circulation models forced by different snow cover conditions. Forecast systems reproduce realistically regional changes of the surface energy balance associated with snow cover variability. Retrospective forecasts and idealised sensitivity experiments converge in identifying a coherent change of the circulation in the Northern Hemisphere. This is compatible with a lagged but fast feedback from the snow to the Arctic Oscillation trough a tropospheric pathway.&lt;/p&gt;

scholarly journals Skillful Climate Forecasts of the Tropical Indo-Pacific Ocean Using Model-Analogs

2018 ◽  
Vol 31 (14) ◽  
pp. 5437-5459 ◽  
Author(s):  
Hui Ding ◽  
Matthew Newman ◽  
Michael A. Alexander ◽  
Andrew T. Wittenberg
Keyword(s):  
General Circulation ◽  
General Circulation Models ◽  
Coarse Grained ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Initial State ◽  
The North ◽  
Eastern Equatorial Pacific ◽  
Spread Model ◽  
The Individual

Seasonal forecasts made by coupled atmosphere–ocean general circulation models (CGCMs) undergo strong climate drift and initialization shock, driving the model state away from its long-term attractor. Here we explore initializing directly on a model’s own attractor, using an analog approach in which model states close to the observed initial state are drawn from a “library” obtained from prior uninitialized CGCM simulations. The subsequent evolution of those “model-analogs” yields a forecast ensemble, without additional model integration. This technique is applied to four of the eight CGCMs comprising the North American Multimodel Ensemble (NMME) by selecting from prior long control runs those model states whose monthly tropical Indo-Pacific SST and SSH anomalies best resemble the observations at initialization time. Hindcasts are then made for leads of 1–12 months during 1982–2015. Deterministic and probabilistic skill measures of these model-analog hindcast ensembles are comparable to those of the initialized NMME hindcast ensembles, for both the individual models and the multimodel ensemble. In the eastern equatorial Pacific, model-analog hindcast skill exceeds that of the NMME. Despite initializing with a relatively large ensemble spread, model-analogs also reproduce each CGCM’s perfect-model skill, consistent with a coarse-grained view of tropical Indo-Pacific predictability. This study suggests that with little additional effort, sufficiently realistic and long CGCM simulations provide the basis for skillful seasonal forecasts of tropical Indo-Pacific SST anomalies, even without sophisticated data assimilation or additional ensemble forecast integrations. The model-analog method could provide a baseline for forecast skill when developing future models and forecast systems.

scholarly journals A double ITCZ phenomenology of wind errors in the equatorial Atlantic in seasonal forecasts with ECMWF models

2019 ◽  
Vol 19 (17) ◽  
pp. 11383-11399
Author(s):  
Jonathan K. P. Shonk ◽  
Teferi D. Demissie ◽  
Thomas Toniazzo
Keyword(s):  
Boundary Layer ◽  
General Circulation ◽  
Marine Boundary Layer ◽  
General Circulation Models ◽  
Equatorial Atlantic ◽  
Seasonal Forecasts ◽  
Coupled Models ◽  
Trade Winds ◽  
Double Itcz ◽  
Rapid Transition

Abstract. Modern coupled general circulation models produce systematic biases in the tropical Atlantic that hamper the reliability of long-range predictions. This study focuses on a common springtime westerly wind bias in the equatorial Atlantic in seasonal hindcasts from two coupled models – ECMWF System 4 and EC-Earth v2.3 – and in hindcasts also based on System 4, but with prescribed sea-surface temperatures. The development of the equatorial westerly bias in early April is marked by a rapid transition from a wintertime easterly, cold tongue bias to a springtime westerly bias regime that displays a marked double intertropical convergence zone (ITCZ). The transition is a seasonal feature of the model climatology (independent of initialisation date) and is associated with a seasonal increase in rainfall where a second branch of the ITCZ is produced south of the Equator. Excess off-equatorial convergence redirects the trade winds away from the Equator. Based on arguments of temporal coincidence, the results of our analysis contrast with those from previous work, and alleged causes hereto identified as the likely cause of the equatorial westerly bias in other models must be discarded. Quite in general, we find no evidence of remote influences on the development of the springtime equatorial bias in the Atlantic in the IFS-based models. Limited evidence however is presented that supports the hypothesis of an incorrect representation of the meridional equatorward flow in the marine boundary layer of the southern Atlantic as a contributing factor. Erroneous dynamical constraints on the flow upstream of the Equator may generate convergence and associated rainfall south of the Equator. This directs attention to the representation of the properties of the subtropical boundary layer as a potential source for the double ITCZ bias.

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