Predictability of extreme events in UFS at subseasonal time scale

2021 ◽  
Author(s):  
Christiana Stan
Keyword(s):  
Extreme Events ◽  
Time Scale ◽  
Initial Conditions ◽  
Coupled Model ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Forecast System ◽  
Cold Events ◽  
Second Category

<p>The predictability of extreme events over the continental United States (CONUS) in the Unified Forecast System (UFS) Couple Model is studied at subseasonal time scale. The benchmark runs of UFS (GFSv15), a coupled model consisting of atmospheric component (FV3GFS) with 28 km resolution and ocean (MOM6) and sea ice (CICE5) components with global 0.25° resolution, for the period April 2011–December 2017 have been assessed. The model’s month-long forecasts initiated on the first and fifteenth of each month are used to examine the predictability of extreme events in precipitation and 2m temperature. The atmospheric and ice initial conditions are from CFSR data, and the ocean initial conditions are from 3Dvar CPC. The errors in the week 1–4 predictions and the corresponding spatial correlation between model and observation over CONUS are presented. The differences in the predictability of the extreme events between the boreal summer and winter are discussed. Two categories of extreme events are evaluated: 95<sup>th</sup> and 99<sup>th</sup> percentile, respectively. The forecast skill of extreme events in the 95<sup>th</sup> percentile is higher than the forecast skill of events in the second category. The forecast skill of warm and cold events in the 95<sup>th</sup> percentile shows seasonal dependence and is higher during the boreal winter.</p>

Eastern Pacific Intraseasonal Variability: A Predictability Perspective

2014 ◽  
Vol 27 (23) ◽  
pp. 8869-8883 ◽  
Author(s):  
J. M. Neena ◽  
Xianan Jiang ◽  
Duane Waliser ◽  
June-Yi Lee ◽  
Bin Wang
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Initial Conditions ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Eastern Pacific ◽  
Prediction Skill ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Weather And Climate

Abstract The eastern Pacific (EPAC) warm pool is a region of strong intraseasonal variability (ISV) during boreal summer. While the EPAC ISV is known to have large-scale impacts that shape the weather and climate in the region (e.g., tropical cyclones and local monsoon), simulating the EPAC ISV is still a great challenge for present-day global weather and climate models. In the present study, the predictive skill and predictability of the EPAC ISV are explored in eight coupled model hindcasts from the Intraseasonal Variability Hindcast Experiment (ISVHE). Relative to the prediction skill for the boreal winter Madden–Julian oscillation (MJO) in the ISVHE (~15–25 days), the skill for the EPAC ISV is considerably lower in most models, with an average skill around 10 days. On the other hand, while the MJO exhibits a predictability of 35–45 days, the predictability estimate for the EPAC ISV is 20–30 days. The prediction skill was found to be higher when the hindcasts were initialized from the convective phase of the EPAC ISV as opposed to the subsidence phase. Higher prediction skill was also found to be associated with active MJO initial conditions over the western Pacific (evident in four out of eight models), signaling the importance of exploring the dynamic link between the MJO and the EPAC ISV. The results illustrate the possibility and need for improving dynamical prediction systems to facilitate more accurate and longer-lead predictions of the EPAC ISV and associated weather and short-term climate variability.

Dependence of Indian summer monsoon rainfall forecast skill of CFSv2 on initial conditions and the role of bias in SST boundary forcing

2021 ◽  
Author(s):  
Stella Jes Varghese ◽  
Kavirajan Rajendran ◽  
Sajani Surendran ◽  
Arindam Chakraborty
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Lead Time ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Initial Conditions ◽  
Summer Monsoon Rainfall ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Central Pacific

<p>Indian summer monsoon seasonal reforecasts by CFSv2, initiated from January (4-month lead time, L4) through May (0-month lead time, L0) initial conditions (ICs), are analysed to investigate causes for the highest Indian summer monsoon rainfall (ISMR) forecast skill of CFSv2 with February (3-month lead time, L3) ICs. Although theory suggests forecast skill should degrade with increase in lead-time, CFSv2 shows highest skill with L3, due to its forecasting of ISMR excess of 1983 which other ICs failed to forecast. In contrast to observation, in CFSv2, ISMR extremes are largely decided by sea surface temperature (SST) variation over central Pacific (NINO3.4) associated with El Niño-Southern Oscillation (ENSO), where ISMR excess (deficit) is associated with La Niña (El Niño) or cooling (warming) over NINO3.4. In 1983, CFSv2 with L3 ICs forecasted strong La Niña during summer, which resulted in 1983 ISMR excess. In contrast, in observation, near normal SSTs prevailed over NINO3.4 and ISMR excess was due to variation of convection over equatorial Indian Ocean, which CFSv2 fails to capture with all ICs. CFSv2 reforecasts with late-April/early-May ICs are found to have highest deterministic ISMR forecast skill, if 1983 is excluded and Indian monsoon seasonal biases are also reduced. During the transitional ENSO in Boreal summer of 1983, faster and intense cooling of NINO3.4 SSTs in L3, could be due to larger dynamical drift with longer lead time of forecasting, compared to L0. Boreal summer ENSO forecast skill is also found to be lowest for L3 which gradually decreases from June to September. Rainfall occurrence with strong cold bias over NINO3.4, is because of the existence of stronger ocean-atmosphere coupling in CFSv2, but with a shift of the SST-rainfall relationship pattern to slightly colder SSTs than the observed. Our analysis suggests the need for a systematic approach to minimize bias in SST boundary forcing in CFSv2, to achieve improved ISMR forecasts.</p>

scholarly journals Tendency Bias Correction in Coupled and Uncoupled Global Climate Models with a Focus on Impacts over North America

2019 ◽  
Vol 32 (2) ◽  
pp. 639-661 ◽  
Author(s):  
Y. Chang ◽  
S. D. Schubert ◽  
R. D. Koster ◽  
A. M. Molod ◽  
H. Wang
Keyword(s):  
North America ◽  
Bias Correction ◽  
Climate Models ◽  
Global Climate ◽  
Storm Track ◽  
Coupled Model ◽  
Global Climate Models ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Transient Wave

Abstract We revisit the bias correction problem in current climate models, taking advantage of state-of-the-art atmospheric reanalysis data and new data assimilation tools that simplify the estimation of short-term (6 hourly) atmospheric tendency errors. The focus is on the extent to which correcting biases in atmospheric tendencies improves the model’s climatology, variability, and ultimately forecast skill at subseasonal and seasonal time scales. Results are presented for the NASA GMAO GEOS model in both uncoupled (atmosphere only) and coupled (atmosphere–ocean) modes. For the uncoupled model, the focus is on correcting a stunted North Pacific jet and a dry bias over the central United States during boreal summer—long-standing errors that are indeed common to many current AGCMs. The results show that the tendency bias correction (TBC) eliminates the jet bias and substantially increases the precipitation over the Great Plains. These changes are accompanied by much improved (increased) storm-track activity throughout the northern midlatitudes. For the coupled model, the atmospheric TBCs produce substantial improvements in the simulated mean climate and its variability, including a much reduced SST warm bias, more realistic ENSO-related SST variability and teleconnections, and much improved subtropical jets and related submonthly transient wave activity. Despite these improvements, the improvement in subseasonal and seasonal forecast skill over North America is only modest at best. The reasons for this, which are presumably relevant to any forecast system, involve the competing influences of predictability loss with time and the time it takes for climate drift to first have a significant impact on forecast skill.

scholarly journals The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2)

2020 ◽  
Vol 35 (4) ◽  
pp. 1317-1343 ◽  
Author(s):  
Hai Lin ◽  
William J. Merryfield ◽  
Ryan Muncaster ◽  
Gregory C. Smith ◽  
Marko Markovic ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Southern Oscillation ◽  
Weather Prediction ◽  
Coupled Model ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Boreal Winter ◽  
Prediction System ◽  
Sea Ice Concentration

AbstractThe second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale.

Assessment and Comparison of Subseasonal Bias and Forecast Skill in the Unified Forecast System (UFS) Benchmarks 3 and 5

2021 ◽  
Author(s):  
Keri Kodama ◽  
David Straus ◽  
James Kinter
Keyword(s):  
Systematic Error ◽  
Lead Time ◽  
Diabatic Heating ◽  
Model Development ◽  
Forecast Skill ◽  
Boreal Winter ◽  
Forecast System ◽  
Climate Forecast System ◽  
Temperature And Precipitation ◽  
Marked Diminution

<p>A series of reforecasts have been generated with prototype versions of the coupled Unified Forecast System (UFS) to evaluate progress in the model development. The forecast skill and biases of the UFS Prototypes 3 and 5 reforecast sets—called Benchmark 3 and Benchmark 5, respectively—are analyzed and compared with the NCEP Climate Forecast System version 2 (CFSv2) reforecasts from the Subseasonal Prediction Experiment (SubX). The evaluation focuses on surface variables typically provided in the subseasonal outlooks at weekly-averaged timescales, namely 2-meter air temperature, precipitation rate, and sea surface temperature. Additional assessment of the structure of the systematic error in total diabatic heating over three broad layers of the atmosphere (850-650 hPa, 650-450 hPa and 450-50 hPa) has been performed as a function of season and forecast lead. In terms of forecast skill, all models still experience a skill drop-off of varying degree by week 3. In general, however, the UFS prototypes considerably reduce the marked diminution of variability with lead time displayed in their predecessor, CFSv2. Moreover, the prototypes have reduced systematic error compared to CFSv2, particularly for 2-meter temperature and precipitation. A systematic overestimate of diabatic cooling is noted in the upper atmosphere (diabatic heating too negative compare to ERA-5 estimates) during boreal winter. </p>

scholarly journals Tropical Atlantic SST Prediction with Coupled Ocean–Atmosphere GCMs

2006 ◽  
Vol 19 (23) ◽  
pp. 6047-6061 ◽  
Author(s):  
Timothy N. Stockdale ◽  
Magdalena A. Balmaseda ◽  
Arthur Vidard
Keyword(s):  
Initial Conditions ◽  
Coupled Model ◽  
Boreal Summer ◽  
Tropical Atlantic ◽  
Equatorial Atlantic ◽  
Seasonal Forecasts ◽  
Coupled Models ◽  
Equatorial Ocean ◽  
Sst Anomalies

Abstract Variations in tropical Atlantic SST are an important factor in seasonal forecasts in the region and beyond. An analysis is given of the capabilities of the latest generation of coupled GCM seasonal forecast systems to predict tropical Atlantic SST anomalies. Skill above that of persistence is demonstrated in both the northern tropical and equatorial Atlantic, but not farther south. The inability of the coupled models to correctly represent the mean seasonal cycle is a major problem in attempts to forecast equatorial SST anomalies in the boreal summer. Even when forced with observed SST, atmosphere models have significant failings in this area. The quality of ocean initial conditions for coupled model forecasts is also a cause for concern, and the adequacy of the near-equatorial ocean observing system is in doubt. A multimodel approach improves forecast skill only modestly, and large errors remain in the southern tropical Atlantic. There is still much scope for improving forecasts of tropical Atlantic SST.

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 Mid-Twenty-First-Century Changes in Extreme Events over Northern and Tropical Africa

2012 ◽  
Vol 25 (17) ◽  
pp. 5748-5767 ◽  
Author(s):  
Edward K. Vizy ◽  
Kerry H. Cook
Keyword(s):  
West Africa ◽  
Heat Wave ◽  
Extreme Events ◽  
Rainfall Intensity ◽  
Global Climate Models ◽  
Congo Basin ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Diurnal Temperature ◽  
Temperature Ranges

Abstract Changes in rainfall and temperature extremes are predicted by many global climate models as a response to greenhouse gas increases, and such changes will have significant environmental and social impacts. A regional climate model is used to predict changes in extremes across tropical and northern Africa for 2041–60 under a midline emissions forcing scenario. Six indicators are examined, including annual extreme and daily diurnal temperature ranges, heat wave days, number of dry days, number of extreme wet days, and extreme wet day rainfall intensity. Confidence in the projections is evaluated by examining the ensemble spread and the validation of extreme events in the twentieth-century simulation. Despite an increase in both daily minimum and maximum temperatures, diurnal temperature ranges decrease from West Africa to Ethiopia during spring and fall, over the Sahel during summer, and over the Congo basin during winter and spring. Diurnal temperature ranges increase over the Horn of Africa during boreal winter and over Kenya and Tanzania during boreal summer. The number of heat wave days increases north of 8°N with the largest increase (60–120 days) over the western Sahel. The number of dry days decreases over the Congo and the central Sahel but increases over East Africa, the latter associated with a reduction in the springtime long rains. The number of extreme wet rainfall days is projected to increase over West Africa, the Sahel, and the Ethiopian Highlands but decrease over the Congo. The predicted changes in extreme wet rainfall intensity are highly regional.

scholarly journals The Global Influence of the Madden–Julian Oscillation on Extreme Temperature Events*

2015 ◽  
Vol 28 (10) ◽  
pp. 4141-4151 ◽  
Author(s):  
Satoko Matsueda ◽  
Yuhei Takaya
Keyword(s):  
Extreme Events ◽  
Time Lag ◽  
Extreme Temperature ◽  
Forecast Skill ◽  
Japan Meteorological Agency ◽  
Ensemble Prediction ◽  
Boreal Winter ◽  
Madden Julian Oscillation ◽  
Ensemble Prediction System ◽  
Extreme Temperature Events

Abstract The authors investigated the influence of the Madden–Julian oscillation (MJO) on extreme warm and cold events, which may have large social and economic impacts. The frequencies of extreme temperature events were analyzed and compared between active and inactive MJO periods by using the 7-day running average of the 850-hPa temperature during the extended boreal winter (November–April). The results show that the frequency of extreme events is significantly modulated (i.e., increased by a factor of more than 2) by the MJO with a time lag over some areas in the extratropics as well as in the tropics. In the extratropics, the modulation of the frequency of the extreme events is roughly associated with midlatitude wave responses to tropical forcing and anomalous lower-level circulation due to the MJO. The relationship between the MJO and forecast skill of extreme temperature events was also investigated by using a suite of hindcasts made with the operational one-month ensemble prediction system of the Japan Meteorological Agency. Forecast skill of extreme events occurring after active MJO periods tend to be better over some areas, compared with after inactive MJO periods. These results suggest that a realistic representation of the MJO and of the atmospheric response to the MJO in forecast models is important for providing reliable early warning information about extreme events.

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