The Nature and Predictability of the East Asian Extreme Cold Events of 2020/21

Three extreme cold events invaded China during the early winter period between December 2020 to mid-January 2021 and caused drastic temperature drops,setting new low-temperature records at many stations during 6−8 January 2021.These cold events occurred under background conditions of low Arctic sea ice extent and a La Niña event.This is somewhat expected since the coupled effect of large Arctic sea ice loss in autumn and sea surface temperature cooling in the tropical Pacific usually favors cold event occurrences in Eurasia.Further diagnosis reveals that the first cold event is related to the southward movement of the polar vortex and the second one is related to a continent-wide ridge,while both the southward polar vortex and the Asian blocking are crucial for the third event.Here,we evaluate the forecast skill for these three events utilizing the operational forecasts from the ECMWF model.We find that the third event had the highest predictability since it achieves the best skill in forecasting the East Asian cooling among the three events.Therefore,the predictability of these cold events,as well as their relationships with the atmospheric initial conditions,Arctic sea ice,and La Niña deserve further investigation.

Influence of the Arctic on the Predictability of Eurasian Winter Extreme Weather Events

The linkage between the Arctic and midlatitudes has received much attention recently due to the rapidly changing climate.Many investigations have been conducted to reveal the relationship between the Arctic and Eurasian extreme events from the perspective of climatological statistics.As a prediction source for extreme events in Eurasia,Arctic conditions are crucial for extreme event predictions.Therefore,it is urgent to explore the Arctic influence on the predictability of Eurasian extreme events due to the large uncertainties in Arctic conditions.Considering the sensitivity and nonlinearity of the atmospheric circulations in midlatitude to Arctic conditions,it is necessary to investigate the Arctic influences on Eurasian extreme weather events in case studies at weather time scales.Previous studies indicate that only perturbations in specific patterns have fast growth.Thus,the conditional nonlinear optimal perturbation approach is recommended for exploring the uncertainties in Arctic initial and boundary conditions and their synergistic effect on Eurasian extreme events.Moreover,the mechanism for extreme event formation may differ in different cases.Therefore,more extreme cases should be investigated to reach robust conclusions.

A Nonlinear Representation of Model Uncertainty in a Convective-Scale Ensemble Prediction System

How to accurately address model uncertainties with consideration of the rapid nonlinear error growth characteristics in a convection-allowing system is a crucial issue for performing convection-scale ensemble forecasts.In this study,a new nonlinear model perturbation technique for convective-scale ensemble forecasts is developed to consider a nonlinear representation of model errors in the Global and Regional Assimilation and Prediction Enhanced System(GRAPES)Convection-Allowing Ensemble Prediction System(CAEPS).The nonlinear forcing singular vector(NFSV)approach,that is,conditional nonlinear optimal perturbation-forcing(CNOP-F),is applied in this study,to construct a nonlinear model perturbation method for GRAPES-CAEPS.Three experiments are performed:One of them is the CTL experiment,without adding any model perturbation;the other two are NFSV-perturbed experiments,which are perturbed by NFSV with two different groups of constraint radii to test the sensitivity of the perturbation magnitude constraint.Verification results show that the NFSV-perturbed experiments achieve an overall improvement and produce more skillful forecasts compared to the CTL experiment,which indicates that the nonlinear NFSV-perturbed method can be used as an effective model perturbation method for convection-scale ensemble forecasts.Additionally,the NFSV-L experiment with large perturbation constraints generally performs better than the NFSV-S experiment with small perturbation constraints in the verification for upper-air and surface weather variables.But for precipitation verification,the NFSV-S experiment performs better in forecasts for light precipitation,and the NFSV-L experiment performs better in forecasts for heavier precipitation,indicating that for different precipitation events,the perturbation magnitude constraint must be carefully selected.All the findings above lay a foundation for the design of nonlinear model perturbation methods for future CAEPSs.

Evaluation of the Forecast Performance for North Atlantic Oscillation Onset

By utilizing operational forecast products from TIGGE(The International Grand Global Ensemble) during 2006 to 2015,the forecasting performances of the European Centre for Medium-Range Weather Forecasts(ECMWF), National Centers for Environmental Prediction(NCEP), Japan Meteorology Agency(JMA) and China Meteorological Administration(CMA) for the onset of North Atlantic Oscillation(NAO) events are assessed against daily NCEP–NCAR reanalysis data. Twenty-two positive NAO(NAO+) and nine negative NAO(NAO-) events are identified during this time period. For these NAO events,control forecasts, one member of the ensemble that utilizes the currently most proper estimate of the analysis field and the best description of the model physics, are able to predict their onsets three to five days in advance. Moreover, the failure proportion for the prediction of NAO-onset is higher than that for NAO+ onset, which indicates that NAO-onset is harder to forecast. Among these four operational centers, ECMWF has performs best in predicting NAO onset, followed by NCEP,JMA, and then CMA.The forecasting performance of the ensemble mean is also investigated. It is found that, compared with the control forecast, the ensemble mean does not improve the forecasting skill with respect to the onset time of NAO events. Therefore,a confident forecast of NAO onset can only be achieved three to five days in advance.

The Influence of Arctic Sea Ice Concentration Perturbations on Subseasonal Predictions of North Atlantic Oscillation Events

The influence of Arctic sea ice concentration (SIC) on the subseasonal prediction of the North Atlantic Oscillation (NAO) event is investigated by utilizing the Community Atmospheric Model version 4. The optimal Arctic SIC perturbations which exert the greatest influence on the onset of an NAO event from a lead of three pentads (15 days) are obtained with a conditional nonlinear optimal perturbation approach. Numerical results show that there are two types of optimal Arctic SIC perturbations for each NAO event, with one weakening event (marked as type-1) and another strengthening event (marked as type-2). For positive NAO events, type-1 optimal SIC perturbations mainly show positive SIC anomalies in the Greenland, Barents, and Okhotsk Seas, while type-2 perturbations mainly feature negative SIC anomalies in these regions. For negative NAO events, the optimal SIC perturbations have almost opposite patterns to those in positive events, although there are some differences among these SIC perturbations due to different atmospheric initial conditions. Further diagnosis reveals that the optimal Arctic SIC perturbations first modify the surface turbulent heat flux and the temperature in the lower troposphere via diabatic processes. Afterward, the temperature in the low troposphere is mainly affected by dynamic advection. Finally, potential vorticity advection plays a crucial role in the 500-hPa geopotential height prediction in the northern North Atlantic sector during pentad 4, which influences NAO event prediction. These results highlight the importance of Arctic SIC on NAO event prediction and the spatial characteristics of the SIC perturbations may provide scientific support for target observations of SIC in improving NAO subseasonal predictions.

Influence of Arctic Sea-ice Concentration on Extended-range Forecasting of Cold Events in East Asia

Utilizing the Community Atmosphere Model,version 4,the influence of Arctic sea-ice concentration(SIC)on the extended-range prediction of three simulated cold events(CEs)in East Asia is investigated.Numerical results show that the Arctic SIC is crucial for the extended-range prediction of CEs in East Asia.The conditional nonlinear optimal perturbation approach is adopted to identify the optimal Arctic SIC perturbations with the largest influence on CE prediction on the extended-range time scale.It shows that the optimal SIC perturbations are more inclined to weaken the CEs and cause large prediction errors in the fourth pentad,as compared with random SIC perturbations under the same constraint.Further diagnosis reveals that the optimal SIC perturbations first modulate the local temperature through the diabatic process,and then influence the remote temperature by horizontal advection and vertical convection terms.Consequently,the optimal SIC perturbations trigger a warming center in East Asia through the propagation of Rossby wave trains,leading to the largest prediction uncertainty of the CEs in the fourth pentad.These results may provide scientific support for targeted observation of Arctic SIC to improve the extended-range CE prediction skill.

A commentary of"Artificial intelligence models bring new breakthroughs in global accurate weather forecasting":Top 10 Scientific Advances of 2023,China

Skillful weather forecasts have the potential to save lives,support emergency management,and mitigate economic and social losses,which capture the public's attention.The present framework of numerical weather prediction(NWP)can trace its origins back to the 1950s,by solving partial differential equations(PDEs)that describe atmospheric motion,to infer future atmospheric states.Such forecasts typically require several hours on a supercomputer with hundreds of nodes for the upcoming days.Among the various numerical models used by operational centers,the Integrated Forecasting System(IFS)from the European Centre for Medium-Range Weather Forecasts(ECMWF)stands out for its superior skills in medium-range weather forecasts.The quiet revolution of NwP has also been evaluated by the World Meteorological Organization as one of the most significant scientific,technological and social advances in the twentieth century[1].

Targeted Observations for Improving Prediction of the NAO Onset

Based on the viewpoint that the North Atlantic Oscillation(NAO)has an intrinsic timescale of approximate two weeks and can be treated as an initial value problem,targeted observations for improving the prediction of the onset of NAO events are investigated by using the conditional nonlinear optimal perturbation(CNOP)method with a quasigeostrophic model.The results show that flow-dependent sensitive areas for the prediction of NAO onset are mainly located over North Atlantic and its upstream regions.Targeted observations over the main sensitive areas could improve NAO onset prediction in most cases(approximately 75%)due to reduced errors in anomalous eddy vorticity forcing(EVF)projection in the typical NAO mode.Moreover,a flow-independent sensitive area is determined based on the winter climatological flow,which is located over North America and its adjacent ocean.The NAO onset prediction can also be improved by targeted observations over the flow-independent sensitive area,but the skill improvement is somewhat lower than that derived from observations over the flow-dependent sensitive area.The above results indicate that targeted observations over sensitive areas identified by the CNOP method can help to improve the onset prediction of NAO events.