Skill and predictability in multimodel ensemble forecasts for Northern Hemisphere regions with dominant winter precipitation

Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) were used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere with lead times of 1–7 days based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and UK Met Office (UKMO) ensemble prediction systems. The performance of BMA and EMOS were compared with each other and with the raw ensembles and climatological forecasts from the perspective of both deterministic and probabilistic forecasting. The results show that the deterministic forecasts of the 500 hPa geopotential height distribution obtained from BMA and EMOS are more similar to the observed distribution than the raw ensembles, especially for the prediction of the western Pacific subtropical high. BMA and EMOS provide a better calibrated and sharper probability density function than the raw ensembles. They are also superior to the raw ensembles and climatological forecasts according to the Brier score and the Brier skill score. Comparisons between BMA and EMOS show that EMOS performs slightly better for lead times of 1–4 days, whereas BMA performs better for longer lead times. In general, BMA and EMOS both improve the prediction skill of the 500 hPa geopotential height field.

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Using Percentiles to Communicate Snowfall Uncertainty

Weather and Forecasting ◽

10.1175/waf-d-14-00019.1 ◽

2014 ◽

Vol 29 (5) ◽

pp. 1259-1265 ◽

Cited By ~ 7

Author(s):

David R. Novak ◽

Keith F. Brill ◽

Wallace A. Hogsett

Keyword(s):

Probability Distribution ◽

False Alarm ◽

Weather Prediction ◽

Winter Precipitation ◽

Risk Tolerance ◽

Distribution Functions ◽

Precipitation Forecast ◽

Ensemble Forecasts ◽

False Alarm Ratio ◽

Probability Distribution Functions

Abstract An objective technique to determine forecast snowfall ranges consistent with the risk tolerance of users is demonstrated. The forecast snowfall ranges are based on percentiles from probability distribution functions that are assumed to be perfectly calibrated. A key feature of the technique is that the snowfall range varies dynamically, with the resultant ranges varying based on the spread of ensemble forecasts at a given forecast projection, for a particular case, for a particular location. Furthermore, this technique allows users to choose their risk tolerance, quantified in terms of the expected false alarm ratio for forecasts of snowfall range. The technique is applied to the 4–7 March 2013 snowstorm at two different locations (Chicago, Illinois, and Washington, D.C.) to illustrate its use in different locations with different forecast uncertainties. The snowfall range derived from the Weather Prediction Center Probabilistic Winter Precipitation Forecast suite is found to be statistically reliable for the day 1 forecast during the 2013/14 season, providing confidence in the practical applicability of the technique.

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Cross-Seasonal Relationship between the Boreal Autumn SAM and Winter Precipitation in the Northern Hemisphere in CMIP5

Journal of Climate ◽

10.1175/jcli-d-15-0708.1 ◽

2016 ◽

Vol 29 (18) ◽

pp. 6617-6636 ◽

Cited By ~ 8

Author(s):

Ting Liu ◽

Jianping Li ◽

Juan Feng ◽

Xiaofan Wang ◽

Yang Li

Keyword(s):

Northern Hemisphere ◽

Climate Models ◽

Decisive Role ◽

Winter Precipitation ◽

Systematic Evaluation ◽

Cmip5 Models ◽

Type I ◽

Sst Variability ◽

Ocean Atmosphere ◽

The Cross

Abstract Recent work suggests that the boreal autumn Southern Hemisphere annular mode (SAM) favors a tripole pattern of winter precipitation anomalies in the Northern Hemisphere. This study focuses on the abilities of climate models that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) to reproduce the physical processes involved in this observed cross-seasonal connection. A systematic evaluation suggested that 16 out of 25 models were essentially capable of reproducing this cross-seasonal connection. Two categories of models were selected to explore the underlying reasons for these successful simulations. Models that successfully simulated the cross-seasonal relationship were placed in the type-I category, and these performed well in reproducing the related physical mechanism, known as the “coupled ocean–atmosphere bridge,” in terms of the SST variability associated with the SAM and response of the meridional circulation to these SST anomalies. In contrast, the type-II category of models showed poor performance in representing the related processes and associated feedbacks, and the model biases compromised the performance of the simulated cross-seasonal relationship. These results demonstrate that the capability of the CMIP5 models to reproduce SST variability associated with the boreal autumn SAM and related coupled ocean–atmosphere bridge process plays a decisive role in the successful simulation of the cross-seasonal relationship.

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Seasonal predictions of precipitation over Africa using coupled ocean-atmosphere general circulation models: skill of the ENSEMBLES project multimodel ensemble forecasts

Tellus A Dynamic Meteorology and Oceanography ◽

10.1111/j.1600-0870.2010.00493.x ◽

2011 ◽

Vol 63 (2) ◽

pp. 283-299 ◽

Cited By ~ 36

Author(s):

L. BattÉ ◽

M. DeÉQueÉ

Keyword(s):

General Circulation ◽

General Circulation Models ◽

Multimodel Ensemble ◽

Ensemble Forecasts ◽

Ocean Atmosphere ◽

Ensembles Project

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Multimodel Ensemble Forecasts of Precipitation Based on an Object-Based Diagnostic Evaluation

Monthly Weather Review ◽

10.1175/mwr-d-19-0266.1 ◽

2020 ◽

Vol 148 (6) ◽

pp. 2591-2606 ◽

Cited By ~ 1

Author(s):

Luying Ji ◽

Xiefei Zhi ◽

Clemens Simmer ◽

Shoupeng Zhu ◽

Yan Ji

Keyword(s):

Absolute Error ◽

Skill Score ◽

Diagnostic Evaluation ◽

Ensemble Prediction ◽

Multimodel Ensemble ◽

Ensemble Forecasts ◽

Object Based ◽

Forecast Technique ◽

Point To Point ◽

The Individual

Abstract We analyzed 24-h accumulated precipitation forecasts over the 4-month period from 1 May to 31 August 2013 over an area located in East Asia covering the region 15.05°–58.95°N, 70.15°–139.95°E generated with the ensemble prediction systems (EPS) from ECMWF, NCEP, UKMO, JMA, and CMA contained in the TIGGE dataset. The forecasts are first evaluated with the Method for Object-Based Diagnostic Evaluation (MODE). Then a multimodel ensemble (MME) forecast technique that is based on weights derived from object-based scores is investigated and compared with the equally weighted MME and the traditional gridpoint-based MME forecast using weights derived from the point-to-point metric, mean absolute error (MAE). The object-based evaluation revealed that attributes of objects derived from the ensemble members of the five individual EPS forecasts and the observations differ consistently. For instance, their predicted centroid location is more southwestward, their shape is more circular, and their orientation is more meridional than in the observations. The sensitivity of the number of objects and their attributes to methodological parameters is also investigated. An MME prediction technique that is based on weights computed from the object-based scores, median of maximum interest, and object-based threat score is explored and the results are compared with the ensemble forecasts of the individual EPS, the equally weighted MME forecast, and the traditional superensemble forecast. When using MODE statistics for the forecast evaluation, the object-based MME prediction outperforms all other predictions. This is mainly because of a better prediction of the objects’ centroid locations. When using the precipitation-based fractions skill score, which is not used in either of the weighted MME forecasts, the object-based MME forecasts are slightly better than the equally weighted MME forecasts but are inferior to the traditional superensemble forecast that is based on weights derived from the point-to-point metric MAE.

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Seasonal Forecasts of Canadian Winter Precipitation by Postprocessing GCM Integrations

Monthly Weather Review ◽

10.1175/2007mwr2232.1 ◽

2008 ◽

Vol 136 (3) ◽

pp. 769-783 ◽

Cited By ~ 17

Author(s):

Hai Lin ◽

Gilbert Brunet ◽

Jacques Derome

Keyword(s):

General Circulation ◽

Large Scale ◽

General Circulation Models ◽

Winter Precipitation ◽

Second Phase ◽

Seasonal Forecasts ◽

Large Area ◽

Ensemble Forecasts ◽

The North ◽

Ensemble Mean

Abstract In the second phase of the Canadian Historical Forecasting Project (HFP2), four global atmospheric general circulation models (GCMs) were used to perform seasonal forecasts over the period of 1969–2003. Little predictive skill was found from the uncalibrated GCM ensemble seasonal predictions for the Canadian winter precipitation. This study is an effort to improve the precipitation forecasts through a postprocessing approach. Canadian winter precipitation is significantly influenced by two of the most important atmospheric large-scale patterns: the Pacific–North American pattern (PNA) and the North Atlantic Oscillation (NAO). The time variations of these two patterns were found to be significantly correlated with those of the leading singular value decomposition (SVD) modes that relate the ensemble mean forecast 500-mb geopotential height over the Northern Hemisphere and the tropical Pacific SST in the previous month (November). A statistical approach to correct the ensemble forecasts was formulated based on the regression of the model’s leading forced SVD patterns and the observed seasonal mean precipitation. The performance of the corrected forecasts was assessed by comparing its cross-validated skill with that of the original GCM ensemble mean forecasts. The results show that the corrected forecasts predict the Canadian winter precipitation with statistically significant skill over the southern prairies and a large area of Québec–Ontario.

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Autumn Sea Ice Cover, Winter Northern Hemisphere Annular Mode, and Winter Precipitation in Eurasia

Journal of Climate ◽

10.1175/jcli-d-12-00380.1 ◽

2012 ◽

Vol 26 (11) ◽

pp. 3968-3981 ◽

Cited By ~ 59

Author(s):

Fei Li ◽

Huijun Wang

Keyword(s):

Sea Ice ◽

Surface Temperature ◽

Northern Hemisphere ◽

Ice Cover ◽

Winter Precipitation ◽

The Arctic ◽

Northern Eurasia ◽

Laptev Sea ◽

Annular Mode ◽

Northern Hemisphere Annular Mode

Abstract This paper examines the impacts of the previous autumn sea ice cover (SIC) on the winter Northern Hemisphere annular mode (NAM) and winter precipitation in Eurasia. The coherent variations among the Kara–Laptev autumn SIC, winter NAM, and Eurasian winter precipitation appear after the year 1982, which may prove useful for seasonal prediction of winter precipitation. From a physical point of view, the Kara–Laptev SIC and sea surface temperature (SST) anomalies develop in autumn and remain in winter. Given that winter NAM is characterized by an Arctic–midlatitude seesaw centered over the Barents Sea and Kara–Laptev Seas, it is closely linked to the Arctic forcing that corresponds to the Kara–Laptev sea ice increase (reduction) and the associated surface temperature cooling (warming). Moreover, based on both model simulations and observations, the diminishing Kara–Laptev sea ice does induce positive sea level pressure (SLP) anomalies over high-latitude Eurasia in winter, which is accompanied by a significant surface warming in northern Eurasia and cooling south of the Mediterranean. This surface air temperature (SAT) anomaly pattern facilitates increases of specific humidity in northern Eurasia with a major ridge extending southward along the East Asian coast. As a result, the anomalous Eurasian winter precipitation has a more zonal band structure.

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Quantifying the Uncertainty in Historical and Future Simulations of Northern Hemisphere Spring Snow Cover

Journal of Climate ◽

10.1175/jcli-d-16-0341.1 ◽

2016 ◽

Vol 29 (23) ◽

pp. 8647-8663 ◽

Cited By ~ 21

Author(s):

Chad W. Thackeray ◽

Christopher G. Fletcher ◽

Lawrence R. Mudryk ◽

Chris Derksen

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Climate Model ◽

Coupled Model ◽

Internal Variability ◽

Winter Precipitation ◽

First Century ◽

Internal Climate Variability ◽

Snow Cover Extent ◽

Twenty First Century

Abstract Projections of twenty-first-century Northern Hemisphere (NH) spring snow cover extent (SCE) from two climate model ensembles are analyzed to characterize their uncertainty. Phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble exhibits variability resulting from both model differences and internal climate variability, whereas spread generated from a Canadian Earth System Model–Large Ensemble (CanESM-LE) experiment is solely a result of internal variability. The analysis shows that simulated 1981–2010 spring SCE trends are slightly weaker than observed (using an ensemble of snow products). Spring SCE is projected to decrease by −3.7% ± 1.1% decade−1 within the CMIP5 ensemble over the twenty-first century. SCE loss is projected to accelerate for all spring months over the twenty-first century, with the exception of June (because most snow in this month has melted by the latter half of the twenty-first century). For 30-yr spring SCE trends over the twenty-first century, internal variability estimated from CanESM-LE is substantial, but smaller than intermodel spread from CMIP5. Additionally, internal variability in NH extratropical land warming trends can affect SCE trends in the near future (R2 = 0.45), while variability in winter precipitation can also have a significant (but lesser) impact on SCE trends. On the other hand, a majority of the intermodel spread is driven by differences in simulated warming (dominant in March–May) and snow cover available for melt (dominant in June). The strong temperature–SCE linkage suggests that model uncertainty in projections of SCE could be potentially reduced through improved simulation of spring season warming over land.

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