scholarly journals Seasonal Climate Predictability in a Coupled OAGCM Using a Different Approach for Ensemble Forecasts

2005 ◽  
Vol 18 (21) ◽  
pp. 4474-4497 ◽  
Author(s):  
Jing-Jia Luo ◽  
Sebastien Masson ◽  
Swadhin Behera ◽  
Satoru Shingu ◽  
Toshio Yamagata
Keyword(s):  
Indian Ocean ◽  
Lead Time ◽  
Initial Conditions ◽  
Surface Wind ◽  
Lead Times ◽  
Western Coast ◽  
Easterly Wind ◽  
Skill Scores ◽  
Wind Bursts ◽  
Sst Anomalies

Abstract Predictabilities of tropical climate signals are investigated using a relatively high resolution Scale Interaction Experiment–Frontier Research Center for Global Change (FRCGC) coupled GCM (SINTEX-F). Five ensemble forecast members are generated by perturbing the model’s coupling physics, which accounts for the uncertainties of both initial conditions and model physics. Because of the model’s good performance in simulating the climatology and ENSO in the tropical Pacific, a simple coupled SST-nudging scheme generates realistic thermocline and surface wind variations in the equatorial Pacific. Several westerly and easterly wind bursts in the western Pacific are also captured. Hindcast results for the period 1982–2001 show a high predictability of ENSO. All past El Niño and La Niña events, including the strongest 1997/98 warm episode, are successfully predicted with the anomaly correlation coefficient (ACC) skill scores above 0.7 at the 12-month lead time. The predicted signals of some particular events, however, become weak with a delay in the phase at mid and long lead times. This is found to be related to the intraseasonal wind bursts that are unpredicted beyond a few months of lead time. The model forecasts also show a “spring prediction barrier” similar to that in observations. Spatial SST anomalies, teleconnection, and global drought/flood during three different phases of ENSO are successfully predicted at 9–12-month lead times. In the tropical North Atlantic and southwestern Indian Ocean, where ENSO has predominant influences, the model shows skillful predictions at the 7–12-month lead times. The distinct signal of the Indian Ocean dipole (IOD) event in 1994 is predicted at the 6-month lead time. SST anomalies near the western coast of Australia are also predicted beyond the 12-month lead time because of pronounced decadal signals there.

scholarly journals Moist Dynamics of Severe Monsoons over South Asia: Role of the Tropical SST*

2012 ◽  
Vol 69 (1) ◽  
pp. 97-115 ◽  
Author(s):  
Prasanth A. Pillai ◽  
H. Annamalai
Keyword(s):  
Indian Ocean ◽  
South Asia ◽  
El Niño ◽  
El Nino ◽  
Coupled Model ◽  
Moisture Distribution ◽  
Northern Indian Ocean ◽  
Easterly Wind ◽  
Rainfall Anomalies ◽  
Sst Anomalies

Abstract Diagnostics from observations and multicentury integrations of a coupled model [Geophysical Fluid Dynamics Laboratory (GFDL) coupled model version 2.1 (CM2.1)] indicate that about 65% of the severe monsoons (rainfall > 1.5 standard deviations of its long-term mean) over South Asia are associated with sea surface temperature (SST) anomalies over the equatorial Pacific during the developing phase of ENSO, and another 30% are associated with SST variations over the tropical Indo-Pacific warm pool. The present research aims to identify the moist processes that initiate the dryness (wetness) and provide a precursor for rainfall anomalies over South Asia in spring during El Niño (La Niña). The hypothesis in this paper, based on CM2.1 composites, is that at low levels El Niño–forced equatorial easterly wind anomalies over the Indian Ocean, resulting from Ekman pumping, promote anticyclonic vorticity over the northern Indian Ocean, whose poleward flank advects dry air from northern latitudes to South Asia. This is tested by performing ensemble simulations with the atmospheric component of CM2.1 (AM2.1) and applying moisture and moist static energy budgets. During El Niño, AM2.1 solutions capture the anticyclonic vorticity formation over the northern Indian Ocean 20–25 days earlier than organized negative rainfall anomalies over South Asia, and the advection of climatological air of lower moisture content by these anomalous winds initiates the dryness over South Asia from April onward. This long lead time embodied in this precursor signal can be exploited for predicting severe monsoons. During ENSO neutral conditions, the amplitude of regional SST anomalies during spring is insufficient to produce such a precursor signal. The dominance of the term warrants monitoring the three-dimensional moisture distribution for better understanding, modeling, and predicting of severe monsoons.

scholarly journals Global meteorological drought – Part 2: Seasonal forecasts

2014 ◽  
Vol 18 (7) ◽  
pp. 2669-2678 ◽  
Author(s):  
E. Dutra ◽  
W. Pozzi ◽  
F. Wetterhall ◽  
F. Di Giuseppe ◽  
L. Magnusson ◽  
...  
Keyword(s):  
Lead Time ◽  
Initial Conditions ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Added Value ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Global Precipitation Climatology Centre ◽  
The World

Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two data sets as initial conditions: the Global Precipitation Climatology Centre (GPCC) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation, the most recent ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast evaluation focuses on the periods where precipitation deficits are likely to have higher drought impacts, and the results were summarized over different regions in the world. The verification of the forecasts with lead time indicated that generally for all regions the least reduction on skill was found for (i) long lead times using ERAI or GPCC for monitoring and (ii) short lead times using ECMWF or climatological seasonal forecasts. The memory effect of initial conditions was found to be 1 month of lead time for the SPI-3, 4 months for the SPI-6 and 6 (or more) months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value with skills at least equal to and often above that of climatological forecasts. Furthermore, it is very difficult to improve on the use of climatological forecasts for long lead times. Our results also support recent questions of whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

scholarly journals High Chlorophyll-a Areas along the Western Coast of South Sulawesi-Indonesia during the Rainy Season Revealed by Satellite Data

2021 ◽  
Vol 13 (23) ◽  
pp. 4833
Author(s):  
Anindya Wirasatriya ◽  
Raden Dwi Susanto ◽  
Joga Dharma Setiawan ◽  
Fatwa Ramdani ◽  
Iskhaq Iskandar ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Primary Productivity ◽  
Rainy Season ◽  
Surface Wind ◽  
Western Coast ◽  
Southern Coast ◽  
Easterly Wind ◽  
Chl A ◽  
South Sulawesi ◽  
Upwelling Area

The southern coast of South Sulawesi-Indonesia is known as an upwelling area occurring during dry season, which peaks in August. This upwelling area is indicated by high chlorophyll-a (Chl-a) concentrations due to a strong easterly wind-induced upwelling. However, the investigation of Chl-a variability is less studied along the western coast of South Sulawesi. By taking advantages of remote sensing data of Chl-a, sea surface temperature, surface wind, and precipitation, the present study firstly shows that along the western coast of South Sulawesi, there are two areas, which have high primary productivity occurring during the rainy season. The first area is at 119.0° E–119.5° E; 3.5° S–4.0° S, while the second area is at 119.0° E–119.5° E; 3.5° S–4.0° S. The maximum primary productivity in the first (second) area occurs in April (January). The generating mechanism of the high primary productivity along the western coast of South Sulawesi is different from its southern coast. The presence of river runoff in these two areas may bring anthropogenic organic compounds during the peak of rainy season, resulting in increased Chl-a concentration.

scholarly journals Global meteorological drought – Part 2: Seasonal forecasts

2014 ◽  
Vol 11 (1) ◽  
pp. 919-944 ◽  
Author(s):  
E. Dutra ◽  
W. Pozzi ◽  
F. Wetterhall ◽  
F. Di Giuseppe ◽  
L. Magnusson ◽  
...  
Keyword(s):  
Lead Time ◽  
Initial Conditions ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Added Value ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
The World ◽  
The Standardized Precipitation Index

Abstract. Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two datasets as initial conditions: the Global Precipitation Climatology Center (GPCC) and the ECMWF ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation: the most current ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast skill is concentrated on verification months where precipitation deficits are likely to have higher drought impacts and grouped over different regions in the world. Verification of the forecasts as a function of lead time revealed a reduced impact on skill for: (i) long lead times using different initial conditions, and (ii) short lead times using different precipitation forecasts. The memory effect of initial conditions was found to be 1 month lead time for the SPI-3, 3 to 4 months for the SPI-6 and 5 months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value, a skill similar or better than climatological forecasts. In some cases, particularly for long SPI time scales, it is very difficult to improve on the use of climatological forecasts. Our results also support recent questions whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.

scholarly journals Benchmarking ensemble streamflow prediction skill in the UK

2018 ◽  
Vol 22 (3) ◽  
pp. 2023-2039 ◽  
Author(s):  
Shaun Harrigan ◽  
Christel Prudhomme ◽  
Simon Parry ◽  
Katie Smith ◽  
Maliko Tanguy
Keyword(s):  
Soil Moisture ◽  
Lead Time ◽  
Base Flow ◽  
Ensemble Prediction ◽  
Flow Index ◽  
Lead Times ◽  
Streamflow Prediction ◽  
Skill Scores ◽  
Ensemble Streamflow Prediction ◽  
The Uk

Abstract. Skilful hydrological forecasts at sub-seasonal to seasonal lead times would be extremely beneficial for decision-making in water resources management, hydropower operations, and agriculture, especially during drought conditions. Ensemble streamflow prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in the absence of skilful future meteorological predictions, instead using initial hydrologic conditions (IHCs), such as soil moisture, groundwater, and snow, as the source of skill. We benchmark when and where the ESP method is skilful across a diverse sample of 314 catchments in the UK and explore the relationship between catchment storage and ESP skill. The GR4J hydrological model was forced with historic climate sequences to produce a 51-member ensemble of streamflow hindcasts. We evaluated forecast skill seamlessly from lead times of 1 day to 12 months initialized at the first of each month over a 50-year hindcast period from 1965 to 2015. Results showed ESP was skilful against a climatology benchmark forecast in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialization month, and individual catchment location and storage properties. UK-wide mean ESP skill decayed exponentially as a function of lead time with continuous ranked probability skill scores across the year of 0.75, 0.20, and 0.11 for 1-day, 1-month, and 3-month lead times, respectively. However, skill was not uniform across all initialization months. For lead times up to 1 month, ESP skill was higher than average when initialized in summer and lower in winter months, whereas for longer seasonal and annual lead times skill was higher when initialized in autumn and winter months and lowest in spring. ESP was most skilful in the south and east of the UK, where slower responding catchments with higher soil moisture and groundwater storage are mainly located; correlation between catchment base flow index (BFI) and ESP skill was very strong (Spearman's rank correlation coefficient =0.90 at 1-month lead time). This was in contrast to the more highly responsive catchments in the north and west which were generally not skilful at seasonal lead times. Overall, this work provides scientific justification for when and where use of such a relatively simple forecasting approach is appropriate in the UK. This study, furthermore, creates a low cost benchmark against which potential skill improvements from more sophisticated hydro-meteorological ensemble prediction systems can be judged.

scholarly journals Use of scatterometer based surface vorticity fields in forecasting genesis of tropical cyclones over the north Indian Ocean

MAUSAM ◽  
2021 ◽  
Vol 62 (1) ◽  
pp. 61-72
Author(s):  
O. P. SINGH ◽  
HARVIR SINGH
Keyword(s):  
Indian Ocean ◽  
Tropical Cyclones ◽  
Lead Time ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
North Indian Ocean ◽  
The North ◽  
Peak Intensity ◽  
Ocean Surface Wind ◽  
Vorticity Anomaly

. Utilizing surface vorticity fields computed with the ocean surface wind speed and direction dataobtained from QuikSCAT, a study has been undertaken to investigate the increase in surface vorticity during the genesisphase of tropical cyclones over the north Indian Ocean. Six named tropical cyclones; Agni, Hibaru, Mala, Akash, Nargisand Phyan which formed over the region during 2004-2009 have been selected for this purpose. It has been found thatthere was a steep rise in scatterometer based surface vorticity before the formation of a cyclone in the cyclogenesisregion. The peak surface vorticity in the genesis region was observed on the day of intensification of the vortex to thedepression stage or a day earlier. However, the rising trend in the genesis region begins a few days before the formationof the system. Thus, the surface vorticity fields derived on the basis of scatterometer data can provide predictiveindication of the genesis of tropical cyclones over the Bay of Bengal and Arabian Sea with a lead time of 2-3 days. Usingthis technique it is possible to increase the lead time of pre-cyclone watch period over the north Indian Ocean. No relationship was found between the peak surface vorticity anomaly during the genesis phase and the surfacevorticity anomaly at the time of peak intensity of the system during its life cycle. In other words, the peak surfacevorticity anomaly during genesis phase does not provide any indication of future maximum intensity of the cyclone.

scholarly journals Dynamical Ocean Forcing of the Madden–Julian Oscillation at Lead Times of up to Five Months

2012 ◽  
Vol 25 (8) ◽  
pp. 2824-2842 ◽  
Author(s):  
Benjamin G. M. Webber ◽  
David P. Stevens ◽  
Adrian J. Matthews ◽  
Karen J. Heywood
Keyword(s):  
Indian Ocean ◽  
Rossby Waves ◽  
Surface Wind ◽  
Wind Forcing ◽  
Global Ocean ◽  
Dynamical Response ◽  
Madden Julian Oscillation ◽  
Easterly Wind ◽  
Ocean Response ◽  
Equatorial Rossby Waves

Abstract The authors show that a simple three-dimensional ocean model linearized about a resting basic state can accurately simulate the dynamical ocean response to wind forcing by the Madden–Julian oscillation (MJO). This includes the propagation of equatorial waves in the Indian Ocean, from the generation of oceanic equatorial Kelvin waves to the arrival of downwelling oceanic equatorial Rossby waves in the western Indian Ocean, where they have been shown to trigger MJO convective activity. Simulations with idealized wind forcing suggest that the latitudinal width of this forcing plays a crucial role in determining the potential for such feedbacks. Forcing the model with composite MJO winds accurately captures the global ocean response, demonstrating that the observed ocean dynamical response to the MJO can be interpreted as a linear response to surface wind forcing. The model is then applied to study “primary” Madden–Julian events, which are not immediately preceded by any MJO activity or by any apparent atmospheric triggers, but have been shown to coincide with the arrival of downwelling oceanic equatorial Rossby waves. Case study simulations show how this oceanic equatorial Rossby wave activity is partly forced by reflection of an oceanic equatorial Kelvin wave triggered by a westerly wind burst 140 days previously, and partly directly forced by easterly wind stress anomalies around 40 days prior to the event. This suggests predictability for primary Madden–Julian events on times scales of up to five months, following the reemergence of oceanic anomalies forced by winds almost half a year earlier.

Evaluation of S2S forecasts over India for renewable energy applications

2021 ◽  
Author(s):  
Aheli Das ◽  
Somnath Baidya Roy
Keyword(s):  
Renewable Energy ◽  
Relative Humidity ◽  
Radiative Forcing ◽  
Lead Time ◽  
Surface Fluxes ◽  
Shortwave Radiation ◽  
Lead Times ◽  
Western Coast ◽  
Western Himalayas ◽  
Probability Score

<p>This study evaluates S2S forecasts of meteorological variables relevant for the renewable energy sector from six global coupled forecast models: ECMWF-SEAS5, DWD- GCFS 2.0, Météo-France’s System 6, NCEP-CFSv2, UKMO- GloSea5-GC2-LI, and CMCC-SPS3. The variables include 10m wind speed, incoming shortwave radiation, 2 m temperature, and relative humidity because these variables are critical for estimating the supply and demand of renewable energy. The study is conducted over seven homogenous climate regions of India for 1994-2016 April and May when energy demand peaks throughout the country. The evaluation is done by comparing the forecasts at 1, 2, 3, 4, and 5-months lead-times with ERA5 reanalysis data. In order to assess the forecast quality, deterministic metrics such as bias and correlation and probabilistic metrics such as Ranked Probability Score (RPS) and Continuous Ranked Probability Score (CRPS) are calculated by spatially averaging the forecasts and reanalyses over each region. The tercile limits for each variable are determined separately for each homogenous region from the ERA5 reanalysis using leave-one-out cross-validation. The forecasts show the highest skill at 1-month lead-time and the skill reduces with the increase in lead-time. However, deviations from this pattern are observed in some cases. For example, the 2 m temperature forecasts tend to perform better at longer lead-times over the western Himalayas perhaps because the slowly-varying snow dynamics aids in long-term predictability. The 2 m temperature and relative humidity forecasts generally show high correlations with observations over the western coast of the Indian peninsula in May at all lead-times, indicating the ability of the models to simulate presence of moisture prior to monsoon onset. Results show that the model performance depends on time-period of initialisation, better representation of surface fluxes, interaction between radiation and microphysics schemes, land-surface processes and factors governing radiative forcing such as greenhouse gases and aerosols. Overall, the SEAS5 model performs better than other models, although the Météo-France’s System 6 and UKMO- GloSea5-GC2-LI models also perform well in some regions.</p>

Decadal predictability in the North Atlantic as seen by EC-Earth3.

2021 ◽  
Author(s):  
Bo Christiansen ◽  
Shuting Yang ◽  
Dominic Matte
Keyword(s):  
North Atlantic ◽  
Lead Time ◽  
Initial Conditions ◽  
Statistical Significance ◽  
Lead Times ◽  
The North ◽  
Weak Evidence ◽  
The North Atlantic ◽  
The Impact ◽  
Decadal Predictability

<p>We study the decadal predictability in the North Atlantic region using  ensembles of historical and decadal prediction experiments with EC-Earth3  and other CMIP models. In particular, the focus is on the NAO and the sub-polar gyre region. In general the impact of initialization is weak  for lead-times larger than one to two years and we investigate different ways to isolate and estimate the statistical significance of this impact. For the sub-polar gyre region the prediction skill is found to be mainly due to an abrupt change in the late 90ies and models disagree on whether this skill is due to forcing or initial conditions. Also the predictability of the NAO is weak and varies with lead-time and length of the predicted period. We only see weak evidence of the 'signal-to-noise paradox'. The importance of the ensemble size is also studied.                                                              </p>

scholarly journals Eastern U.S. Verification of Ensemble Precipitation Forecasts

2017 ◽  
Vol 32 (1) ◽  
pp. 117-139 ◽  
Author(s):  
Sanjib Sharma ◽  
Ridwan Siddique ◽  
Nicholas Balderas ◽  
Jose D. Fuentes ◽  
Seann Reed ◽  
...  
Keyword(s):  
United States ◽  
Lead Time ◽  
Weather Prediction ◽  
The United States ◽  
Ensemble Forecast ◽  
Spatial Aggregation ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Eastern United States ◽  
Skill Scores

Abstract The quality of ensemble precipitation forecasts across the eastern United States is investigated, specifically, version 2 of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System Reforecast (GEFSRv2) and Short Range Ensemble Forecast (SREF) system, as well as NCEP’s Weather Prediction Center probabilistic quantitative precipitation forecast (WPC-PQPF) guidance. The forecasts are verified using multisensor precipitation estimates and various metrics conditioned upon seasonality, precipitation threshold, lead time, and spatial aggregation scale. The forecasts are verified, over the geographic domain of each of the four eastern River Forecasts Centers (RFCs) in the United States, by considering first 1) the three systems or guidance, using a common period of analysis (2012–13) for lead times from 1 to 3 days, and then 2) GEFSRv2 alone, using a longer period (2004–13) and lead times from 1 to 16 days. The verification results indicate that, across the eastern United States, precipitation forecast bias decreases and the skill and reliability improve as the spatial aggregation scale increases; however, all the forecasts exhibit some underforecasting bias. The skill of the forecasts is appreciably better in the cool season than in the warm one. The WPC-PQPFs tend to be superior, in terms of the correlation coefficient, relative mean error, reliability, and forecast skill scores, than both GEFSRv2 and SREF, but the performance varies with the RFC and lead time. Based on GEFSRv2, medium-range precipitation forecasts tend to have skill up to approximately day 7 relative to sampled climatology.

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