scholarly journals Multiconvective Parameterizations as a Multimodel Proxy for Seasonal Climate Studies

2005 ◽  
Vol 18 (15) ◽  
pp. 2963-2978 ◽  
Author(s):  
T. E. LaRow ◽  
S. D. Cocke ◽  
D. W. Shin
Keyword(s):  
Initial Conditions ◽  
Coupled Model ◽  
Skill Score ◽  
Model Ensemble ◽  
Single Model ◽  
Multimodel Ensemble ◽  
Temperature And Precipitation ◽  
Skill Scores ◽  
Climate Studies ◽  
Start Dates

Abstract A six-member multicoupled model ensemble is created by using six state-of-the-art deep atmospheric convective schemes. The six convective schemes are used inside a single model and make up the ensemble. This six-member ensemble is compared against a multianalysis ensemble, which is created by varying the initial start dates of the atmospheric component of the coupled model. Both ensembles were integrated for seven months (November–May) over a 12-yr period from 1987 to 1998. Examination of the sea surface temperature and precipitation show that while deterministic skill scores are slightly better for the multicoupled model ensemble the probabilistic skill scores favor the multimodel approach. Combining the two ensembles to create a larger ensemble size increases the probabilistic skill score compared to the multimodel. This altering physics approach to create a multimodel ensemble is seen as an easy way for small modeling centers to generate ensembles with better reliability than by only varying the initial conditions.

scholarly journals Generalization of the Discrete Brier and Ranked Probability Skill Scores for Weighted Multimodel Ensemble Forecasts

2007 ◽  
Vol 135 (7) ◽  
pp. 2778-2785 ◽  
Author(s):  
Andreas P. Weigel ◽  
Mark A. Liniger ◽  
Christof Appenzeller
Keyword(s):  
Practical Importance ◽  
Skill Score ◽  
Ensemble Size ◽  
Seasonal Forecasts ◽  
Single Model ◽  
Multimodel Ensemble ◽  
Ensemble Forecasts ◽  
Model Case ◽  
Skill Scores ◽  
Small Ensemble

Abstract This note describes how the widely used Brier and ranked probability skill scores (BSS and RPSS, respectively) can be correctly applied to quantify the potential skill of probabilistic multimodel ensemble forecasts. It builds upon the study of Weigel et al. where a revised RPSS, the so-called discrete ranked probability skill score (RPSSD), was derived, circumventing the known negative bias of the RPSS for small ensemble sizes. Since the BSS is a special case of the RPSS, a debiased discrete Brier skill score (BSSD) could be formulated in the same way. Here, the approach of Weigel et al., which so far was only applicable to single model ensembles, is generalized to weighted multimodel ensemble forecasts. By introducing an “effective ensemble size” characterizing the multimodel, the new generalized RPSSD can be expressed such that its structure becomes equivalent to the single model case. This is of practical importance for multimodel assessment studies, where the consequences of varying effective ensemble size need to be clearly distinguished from the true benefits of multimodel combination. The performance of the new generalized RPSSD formulation is illustrated in examples of weighted multimodel ensemble forecasts, both in a synthetic random forecasting context, and with real seasonal forecasts of operational models. A central conclusion of this study is that, for small ensemble sizes, multimodel assessment studies should not only be carried out on the basis of the classical RPSS, since true changes in predictability may be hidden by bias effects—a deficiency that can be overcome with the new generalized RPSSD.

scholarly journals DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER)

2004 ◽  
Vol 85 (6) ◽  
pp. 853-872 ◽  
Author(s):  
T. N. Palmer ◽  
A. Alessandri ◽  
U. Andersen ◽  
P. Cantelaube ◽  
M. Davey ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Economic Value ◽  
Ensemble Prediction ◽  
Model Ensemble ◽  
Single Model ◽  
Multimodel Ensemble ◽  
Coupled Models ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Climate Simulations

A multi-model ensemble-based system for seasonal-to-interannual prediction has been developed in a joint European project known as DEMETER (Development of a European Multimodel Ensemble Prediction System for Seasonal to Interannual Prediction). The DEMETER system comprises seven global atmosphere–ocean coupled models, each running from an ensemble of initial conditions. Comprehensive hindcast evaluation demonstrates the enhanced reliability and skill of the multimodel ensemble over a more conventional single-model ensemble approach. In addition, innovative examples of the application of seasonal ensemble forecasts in malaria and crop yield prediction are discussed. The strategy followed in DEMETER deals with important problems such as communication across disciplines, downscaling of climate simulations, and use of probabilistic forecast information in the applications sector, illustrating the economic value of seasonal-to-interannual prediction for society as a whole.

scholarly journals Improved Simulation of Regional Climate by Global Models with Higher Resolution: Skill Scores Correlated with Grid Length*

2015 ◽  
Vol 28 (15) ◽  
pp. 5985-6000 ◽  
Author(s):  
I. G. Watterson
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Coupled Model ◽  
Skill Score ◽  
Cmip5 Models ◽  
Four Seasons ◽  
Skill Scores ◽  
Resolution Model ◽  
High Resolution Model ◽  
Grid Length

Abstract The current generation of climate models, as represented by phase 5 of the Coupled Model Intercomparison Project (CMIP5), has previously been assessed as having more skill in simulating the observed climate than the previous ensemble from phase 3 of CMIP (CMIP3). Furthermore, the skill of models in reproducing seasonal means of precipitation, temperature, and pressure from two observational datasets, quantified by the nondimensional Arcsin–Mielke skill score, appeared to be influenced by model resolution. The analysis is extended to 42 CMIP5 and 24 CMIP3 models. For the combined skill scores for six continents, averaged over the three variables and four seasons, the correlation with model grid length in the 66-model ensemble is −0.73. Focusing on the comparison with ERA-Interim data at higher resolution and with greater regional detail, correlations are nearly as strong for scores over the ocean domain as for land. For the global domain (excluding the Antarctic cap), the correlation of the overall skill score with grid length is −0.61, and it is nearly as strong for each variable. For most tests the improved averaged score of CMIP5 models relative to those from CMIP3 is largely consistent with their increased resolution. However, the improvement for precipitation and the correlations with length are both smaller if rmse is used as a metric. They are smaller again using the GPCP observational data, as the regional detail from a high-resolution model can lead to larger differences when compared to relatively smooth observational fields.

scholarly journals Evaluation of Temperature and Precipitation Trends and Long-Term Persistence in CMIP5 Twentieth-Century Climate Simulations

2013 ◽  
Vol 26 (12) ◽  
pp. 4168-4185 ◽  
Author(s):  
Sanjiv Kumar ◽  
Venkatesh Merwade ◽  
James L. Kinter ◽  
Dev Niyogi
Keyword(s):  
Twentieth Century ◽  
Climate Models ◽  
Coupled Model ◽  
Research Unit ◽  
Trend Detection ◽  
Climatic Research Unit ◽  
Multimodel Ensemble ◽  
Temperature And Precipitation ◽  
Precipitation Trends

Abstract The authors have analyzed twentieth-century temperature and precipitation trends and long-term persistence from 19 climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This study is focused on continental areas (60°S–60°N) during 1930–2004 to ensure higher reliability in the observations. A nonparametric trend detection method is employed, and long-term persistence is quantified using the Hurst coefficient, taken from the hydrology literature. The authors found that the multimodel ensemble–mean global land–average temperature trend (0.07°C decade−1) captures the corresponding observed trend well (0.08°C decade−1). Globally, precipitation trends are distributed (spatially) at about zero in both the models and in the observations. There are large uncertainties in the simulation of regional-/local-scale temperature and precipitation trends. The models’ relative performances are different for temperature and precipitation trends. The models capture the long-term persistence in temperature reasonably well. The areal coverage of observed long-term persistence in precipitation is 60% less (32% of land area) than that of temperature (78%). The models have limited capability to capture the long-term persistence in precipitation. Most climate models underestimate the spatial variability in temperature trends. The multimodel ensemble–average trend generally provides a conservative estimate of local/regional trends. The results of this study are generally not biased by the choice of observation datasets used, including Climatic Research Unit Time Series 3.1; temperature data from Hadley Centre/Climatic Research Unit, version 4; and precipitation data from Global Historical Climatology Network, version 2.

scholarly journals Choices in the Verification of S2S Forecasts and Their Implications for Climate Services

2020 ◽  
Vol 148 (10) ◽  
pp. 3995-4008
Author(s):  
Andrea Manrique-Suñén ◽  
Nube Gonzalez-Reviriego ◽  
Verónica Torralba ◽  
Nicola Cortesi ◽  
Francisco J. Doblas-Reyes
Keyword(s):  
Skill Score ◽  
Climate Services ◽  
Near Surface ◽  
Weather Forecasts ◽  
Probabilistic Evaluation ◽  
Skill Scores ◽  
Medium Range ◽  
The Impact ◽  
Start Dates

AbstractSubseasonal predictions bridge the gap between medium-range weather forecasts and seasonal climate predictions. This time scale is crucial for operations and planning in many sectors such as energy and agriculture. For users to trust these predictions and efficiently make use of them in decision-making, the quality of predicted near-surface parameters needs to be systematically assessed. However, the method to follow in a probabilistic evaluation of subseasonal predictions is not trivial. This study aims to offer an illustration of the impact that the verification setup might have on the calculation of the skill scores, thus providing some guidelines for subseasonal forecast evaluation. For this, several forecast verification setups to calculate the fair ranked probability skill score for tercile categories have been designed. These setups use different number of samples to compute the fair RPSS as well as different ways to define the climatology, characterized by different time periods to average (week or month). These setups have been tested by evaluating 2-m temperature in ECMWF-Ext-ENS 20-yr hindcasts for all of the initializations in 2016 against the ERA-Interim reanalysis. Then, the implications on skill score values of each of the setups are analyzed. Results show that to obtain a robust skill score several start dates need to be employed. It is also shown that a constant monthly climatology over each calendar month may introduce spurious skill score associated with the seasonal cycle. A weekly climatology bears similar results to a monthly running-window climatology; however, the latter provides a better reference climatology when bias adjustment is applied.

scholarly journals Global Distribution of the Skill of Tropical Cyclone Activity Forecasts on Short- to Medium-Range Time Scales

2015 ◽  
Vol 30 (6) ◽  
pp. 1695-1709 ◽  
Author(s):  
Munehiko Yamaguchi ◽  
Frédéric Vitart ◽  
Simon T. K. Lang ◽  
Linus Magnusson ◽  
Russell L. Elsberry ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Time Scales ◽  
Time Window ◽  
Time Windows ◽  
Skill Score ◽  
North Indian Ocean ◽  
Model Ensemble ◽  
Single Model ◽  
The North ◽  
Medium Range

Abstract Operational global medium-range ensemble forecasts of tropical cyclone (TC) activity (genesis plus the subsequent track) are systematically evaluated to understand the skill of the state-of-the-art ensembles in forecasting TC activity as well as the relative benefits of a multicenter grand ensemble with respect to a single-model ensemble. The global ECMWF, JMA, NCEP, and UKMO ensembles are evaluated from 2010 to 2013 in seven TC basins around the world. The verification metric is the Brier skill score (BSS), which is calculated within a 3-day time window over a forecast length of 2 weeks to examine the skill from short- to medium-range time scales (0–14 days). These operational global medium-range ensembles are capable of providing guidance on TC activity forecasts that extends into week 2. Multicenter grand ensembles (MCGEs) tend to have better forecast skill (larger BSSs) than does the best single-model ensemble, which is the ECMWF ensemble in most verification time windows and most TC basins. The relative benefit of the MCGEs is relatively large in the north Indian Ocean and TC basins in the Southern Hemisphere where the BSS of the single-model ensemble is relatively small. The BSS metric and the reliability are found to be sensitive to the choice of threshold wind values that are used to define the model TCs.

scholarly journals Coupling WRF and NRCS-CN Models for Flood Forecasting in Paraíba do Meio River Basin in Alagoas, Brazil

2019 ◽  
Vol 34 (4) ◽  
pp. 545-556
Author(s):  
André Gonçalo dos Santos ◽  
José Nilson Beserra Campos ◽  
Rosiberto Salustiano Silva Junior
Keyword(s):  
River Basin ◽  
Initial Conditions ◽  
Model Performance ◽  
Coupled Model ◽  
Predictive Ability ◽  
Final Analysis ◽  
Flood Forecasting ◽  
Skill Score ◽  
Model Parameters ◽  
Forecast System

Abstract Coupling the WRF and NRCS-CN models was assessed as a tool for a flood forecast system. The models were applied to the Paraíba do Meio River basin, located in Alagoas, Brazil. FNL (Final Analysis GFS) data provided by the Global Forecast System model were used as initial conditions for WRF. Precipitations and observed discharges were collected in data collection platforms. Nine microphysics configurations were used to optimize WRF forecast. For hydrological, the automatic calibrations, available in HMS was used to get the optimum CN model parameters. Optimized precipitations Model performance was assessed with the indicators: bias, root-mean-square error, Pearson’s linear correlation coefficient, Nash-Sutcliffe coefficient, Heidke skill score, hit rate and false alarm rate. WRF´s predictive ability for the optimum configuration was satisfactory. The NRCS-CN yielded good results. The predictive ability of the hydrological model was ranked between satisfactory and acceptable. In a flood forecasting step, the coupled model yielded Nash-Sutcliffe of 0.749 and 0.572 for Atalaia and Viçosa basins. Overall, the method showed potential for the development of a flood alert system.

scholarly journals Effects of forcing differences and initial conditions on inter-model agreement in the VolMIP volc-pinatubo-full experiment

2021 ◽  
Author(s):  
Davide Zanchettin ◽  
Claudia Timmreck ◽  
Myriam Khodri ◽  
Anja Schmidt ◽  
Matthew Toohey ◽  
...  
Keyword(s):  
Experimental Design ◽  
Initial Conditions ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Model Intercomparison ◽  
Model Ensemble ◽  
Implementation Model ◽  
Volcanic Forcing ◽  
Intercomparison Project ◽  
The Tropics

Abstract. This paper provides initial results from a multi-model ensemble analysis based on the volc-pinatubo-full experiment performed within the Model Intercomparison Project on the climatic response to volcanic forcing (VolMIP) as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The volc-pinatubo-full experiment is based on ensemble of volcanic forcing-only climate simulations with the same volcanic aerosol dataset across the participating models (the 1991–1993 Pinatubo period from the CMIP6-GloSSAC dataset). The simulations are conducted within an idealized experimental design where initial states are sampled consistently across models from the CMIP6-piControl simulation providing unperturbed pre-industrial background conditions. The multi-model ensemble includes output from an initial set of six participating Earth system models (CanESM5, GISS-E2.1-G, IPSL-CM6A-LR, MIROC-E2SL, MPI-ESM1.2-LR and UKESM1). The results show overall good agreement between the different models on the global and hemispheric scale concerning the surface climate responses, thus demonstrating the overall effectiveness of VolMIP’s experimental design. However, small yet significant inter-model discrepancies are found in radiative fluxes especially in the tropics, that preliminary analyses link with minor differences in forcing implementation, model physics, notably aerosol-radiation interactions, the simulation and sampling of El Niño-Southern Oscillation (ENSO) and, possibly, the simulation of climate feedbacks operating in the tropics. We discuss the volc-pinatubo-full protocol and highlight the advantages of volcanic forcing experiments defined within a carefully designed protocol with respect to emerging modeling approaches based on large ensemble transient simulations. We identify how the VolMIP strategy could be improved in future phases of the initiative to ensure a cleaner sampling protocol with greater focus on the evolving state of ENSO in the pre-eruption period.

scholarly journals Improving Reliability of Coupled Model Forecasts of Australian Seasonal Rainfall

2013 ◽  
Vol 141 (2) ◽  
pp. 728-741 ◽  
Author(s):  
Sally Langford ◽  
Harry H. Hendon
Keyword(s):  
Initial Conditions ◽  
Forecast Accuracy ◽  
Coupled Model ◽  
Seasonal Rainfall ◽  
The European Union ◽  
Multimodel Ensemble ◽  
Austral Spring ◽  
Additional Information ◽  
Ensembles Project ◽  
Improved Accuracy

Abstract Seasonal rainfall predictions for Australia from the Predictive Ocean Atmosphere Model for Australia (POAMA), version P15b, coupled model seasonal forecast system, which has been run operationally at the Australian Bureau of Meteorology since 2002, are overconfident (too low spread) and only moderately reliable even when forecast accuracy is highest in the austral spring season. The lack of reliability is a major impediment to operational uptake of the coupled model forecasts. Considerable progress has been made to reduce reliability errors with the new version of POAMA2, which makes use of a larger ensemble from three different versions of the model. Although POAMA2 can be considered to be multimodel, its individual models and forecasts are similar as a result of using the same perturbed initial conditions and the same model lineage. Reliability of the POAMA2 forecasts, although improved, remains relatively low. Hence, the authors explore the additional benefit that can be attained using more independent models available in the European Union Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project. Although forecast skill and reliability of seasonal predictions of Australian rainfall are similar for POAMA2 and the ENSEMBLES models, forming a multimodel ensemble using POAMA2 and the ENSEMBLES models is shown to markedly improve reliability of Australian seasonal rainfall forecasts. The benefit of including POAMA2 into this multimodel ensemble is due to the additional information and skill of the independent model, and not just due to an increase in the number of ensemble members. The increased reliability, as well as improved accuracy, of regional rainfall forecasts from this multimodel ensemble system suggests it could be a useful operational prediction system.

scholarly journals An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles

2020 ◽  
Vol 11 (3) ◽  
pp. 807-834 ◽  
Author(s):  
Anna Louise Merrifield ◽  
Lukas Brunner ◽  
Ruth Lorenz ◽  
Iselin Medhaug ◽  
Reto Knutti
Keyword(s):  
Coupled Model ◽  
Internal Variability ◽  
System Model ◽  
Initial Condition ◽  
Earth System ◽  
Model Intercomparison ◽  
Model Ensemble ◽  
Single Model ◽  
Large Ensembles ◽  
Model Ensembles

Abstract. Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single-model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs allow for the quantification of internal variability, a non-negligible component of uncertainty on regional scales, but may also serve to inappropriately narrow uncertainty by giving a single model many additional votes. In advance of the mixed multi-model, the SMILE Coupled Model Intercomparison version 6 (CMIP6) ensemble, we investigate weighting approaches to incorporate 50 members of the Community Earth System Model (CESM1.2.2-LE), 50 members of the Canadian Earth System Model (CanESM2-LE), and 100 members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble. The weights assigned are based on ability to reproduce observed climate (performance) and scaled by a measure of redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) predictors are used to determine the weights, and relationships between present and future predictor behavior are discussed. The estimated residual thermodynamic trend is proposed as an alternative predictor to replace 50-year regional SAT trends, which are more susceptible to internal variability. Uncertainty in estimates of northern European winter and Mediterranean summer end-of-century warming is assessed in a CMIP5 and a combined SMILE–CMIP5 multi-model ensemble. Five different weighting strategies to account for the mix of initial condition (IC) ensemble members and individually represented models within the multi-model ensemble are considered. Allowing all multi-model ensemble members to receive either equal weight or solely a performance weight (based on the root mean square error (RMSE) between members and observations over nine predictors) is shown to lead to uncertainty estimates that are dominated by the presence of SMILEs. A more suitable approach includes a dependence assumption, scaling either by 1∕N, the number of constituents representing a “model”, or by the same RMSE distance metric used to define model performance. SMILE contributions to the weighted ensemble are smallest (<10 %) when a model is defined as an IC ensemble and increase slightly (<20 %) when the definition of a model expands to include members from the same institution and/or development stream. SMILE contributions increase further when dependence is defined by RMSE (over nine predictors) amongst members because RMSEs between SMILE members can be as large as RMSEs between SMILE members and other models. We find that an alternative RMSE distance metric, derived from global SAT and hemispheric SLP climatology, is able to better identify IC members in general and SMILE members in particular as members of the same model. Further, more subtle dependencies associated with resolution differences and component similarities are also identified by the global predictor set.

Sign in / Sign up

Export Citation Format

Share Document