Climate Forecast System Version Research Articles

The noise dose range of the X-59 estimated from propagation simulations

NASA’s X-59 aircraft will soon be flown in a series of low-boom community noise surveys across the USA. Community test experimental designs will include noise dose schedules, which require an estimate of the noise dose range that X-59 can produce. To determine the dose range, propagation simulations using NASA’s PCBoom code were completed using CFD-generated X-59 C612A near-field pressure data for an on-design condition (Mach 1.4 at 53 200 ft) and two off-design conditions (Mach 1.4 at 46 000 ft and Mach 1.3 at 43 000 ft). Near-field data at four cardinal aircraft headings were propagated through one year of realistic atmospheric profiles from the Climate Forecast System Version 2 database at several USA locations. Statistics of the mean and range of the Perceived Level (PL) of the booms in the inner 40 km of each carpet as well as the carpet width were computed. Results indicate the typical inner carpet mean PL is approximately 70–87dB depending on the aircraft condition. The typical PL range across the inner carpet is 6–8 dB (on-design) and 5–13 dB (off-designs). The carpet widths ranged from 32–55 km. Understanding these quantities for various flight and atmospheric conditions is important for planning X-59 community noise surveys.

Forecasting Summer Rainfall and Streamflow over the Yangtze River Valley Using Western Pacific Subtropical High Feature

The western Pacific subtropical high (WPSH) is one of the key systems affecting the summer rainfall over the Yangtze River Valley in China. In this study, the forecasting capacity of the WPSH for summer rainfall and streamflow is evaluated based on the WPSH index (WPSHI) derived from the NCEP/NCAR reanalysis dataset. It has been found that WPSHI can identify extreme flood years with a higher skill than normal wet years. Specifically, exceedance probability forecasting based on WPSHI has higher skills for higher thresholds of rainfall. For streamflow, adding WPSHI as a predictor only enhances the skill for higher thresholds of streamflow relative to models based on antecedent streamflow. Under the same framework, performances of two postprocessing approaches for dynamical forecasts, i.e., the model output statistics (MOS) approach and the reanalysis-based (RAN) approach are compared. Hindcasts from Climate Forecast System version 2 from the National Center for Environmental Prediction (CFSv2) are used to calculate WPSHI, which is used as the predictor for rainfall and streamflow. The result shows that the RAN approach performs better than the MOS approach. This study emphasizes the fact that the forecasting skill of exceedance probability would largely depend on the selected threshold of the predictand, and this fact should be noticed in future studies in the long-term forecasting field.

A five-parameter Gamma-Gaussian model to calibrate monthly and seasonal GCM precipitation forecasts

Calibration is necessary for improving raw forecasts generated by global climate models (GCMs) to fully utilize potential benefits of the forecasts in practical applications. Based on quantile mapping (QM), this paper proposes a five-parameter Gamma-Gaussian model to calibrate monthly and seasonal GCM precipitation forecasts. While QM directly maps forecasts to observations without accounting for the dependency relationship, the Gamma-Gaussian model employs the Gamma distribution to normalize precipitation forecasts and observations using normal quantile transform (NQT) and then formulates a bivariate Gaussian distribution to characterize the dependency relationship. A case study is devised to calibrate global precipitation forecasts generated by the Climate Forecast System version 2 (CFSv2) using both QM and Gamma-Gaussian models. The results show that both QM and Gamma-Gaussian models can effectively correct biases. While QM can improve forecast reliability to some degree by reducing biases, reliability is not always satisfactory. The Gamma-Gaussian model outperforms QM because it can ensure forecast reliability and coherence. To facilitate the selection of marginal distributions for the purpose of calibrating GCM precipitation forecasts, six alternative distributions, i.e., Gamma, lognormal, generalized extreme value, generalized logistic, Pearson type III and Kappa distributions, are employed to characterize the marginal distribution of forecasts (observations) in NQT. It is observed that the Gamma distribution is overall the most suitable and that the alternative distributions tend to fit sample-specific noises and get penalized under cross validation. Overall, the Gamma-Gaussian model can serve as an effective tool to calibrate raw GCM forecasts for hydrological modeling and water resources management.

Performance evaluation of ERA-5, JRA-55, MERRA-2, and CFS-2 reanalysis datasets, over diverse climate regions of Pakistan

Reanalysis precipitation products (RPPs) are frequently used for studying the water cycle changes from short to long-term scale globally. In the current study, ERA-5 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japanese 55-year Reanalysis (JRA-55), the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), and the Climate Forecast System version 2 (CFS-2) precipitation products were evaluated with the rain-gauge data as a reference during 1981–2019 over Pakistan. The performance was assessed using statistical error metrics on daily, monthly, and annual timescales. The reanalysis precipitation products (RPPs) captured the precipitation intensities and the extreme precipitation events (75th to 99th percentile) across climatic classes. On a daily scale, the ERA-5 follows rain-gauges very closely (RC: 0.67, R: 0.81, RMSE: 1.69 mm), consistently capturing the precipitation intensities (light to violent) and extreme precipitation events (95th percentile), followed by CFS-2. The MERRA-2 captured precipitation intensity but did not detect extreme precipitation events in some regions. The JRA-55 produced good results in the central area while overestimated the precipitation in the northern and southern parts of the study area. On a monthly time scale, ERA-5 performed well as compared to the rest of RPPs, with regression coefficient values of 0.91, correlation coefficient (0.96), and a lower value of RMSE (11.09 mm), followed by JRA-55, MERRA-2, and CFS-2. All the RPPs performed better in winter, pre-monsoon, and post-monsoon seasons with slight deviations/differences, but in monsoon season, the ERA-5 and JRA-55 (MERRA-2, CFS-2) overestimated (underestimated) precipitation mean. The findings can help the researchers select reliable datasets for bias correction of the projections and real-time application in flood, drought estimation, and prediction.

Evaluation of Mean State in NCEP Climate Forecast System (Version 2) Simulation Using a Stochastic Multicloud Model Calibrated With DYNAMO RADAR Data

AbstractStochastic parameterizations are continuously providing promising simulations of unresolved atmospheric processes for global climate models (GCMs). One of the stochastic multi‐cloud model (SMCM) features is to mimic the life cycle of the three most common cloud types (congestus, deep, and stratiform) in tropical convective systems. To better represent organized convection in the Climate Forecast System version 2 (CFSv2), the SMCM parameterization is adopted in CFSv2 (SMCM‐CTRL) in lieu of the pre‐existing revised simplified Arakawa–Schubert (RSAS) cumulus scheme and has shown essential improvements in different large‐scale features of tropical convection. But the sensitivity of the SMCM parameterization from the observations is yet to be ascertained. Radar data during the Dynamics of the Madden‐Julian Oscillation (DYNAMO) field campaign is used to tune the SMCM in the present manuscript. The DYNAMO radar observations have been used to calibrate the SMCM using a Bayesian inference procedure to generate key time scale parameters for the transition probabilities of the underlying Markov chains of the SMCM as implemented in CFS (hereafter SMCM‐DYNAMO). SMCM‐DYNAMO improves many aspects of the mean state climate compared to RSAS, and SMCM‐CTRL. Significant improvement is noted in the rainfall probability distribution function over the global tropics. The global distribution of different types of clouds, particularly low‐level clouds, is also improved. The convective and large‐scale rainfall simulations are investigated in detail.

Reanalysis profile downscaling with WRF model and sensitivity to PBL parameterization schemes over a subtropical station

Atmospheric profiles are essential in weather prediction, atmospheric research, and satellite data calibration. In this study, we assess the use of the Weather Research and Forecasting (WRF) model to downscaling NCEP Climate Forecast System Version 2 (CFSv2) reanalysis atmospheric profiles. Moreover, a sensitivity analysis was conducted to Yonsei University (YSU) and Mellor–Yamada–Janjic (MYJ) Planetary Boundary Layer (PBL) parameterization schemes. WRF simulations were carried out using two nested grids with horizontal resolutions of 12 and 3 km. Simulated profiles were evaluated against radiosonde observations in Southern Brazil. Results indicate that WRF can skillfully simulate vertical profiles of water vapor mixing ratio (q) and potential temperature (θ). The entire profile comparison yielded high correlation coefficients (mostly higher than 0.9), low moist and cold average biases, and overall RMSE (MAE) of 0.84 (0.44) g/Kg and 2.30 (1.61) K for q and θ, respectively. In the lower troposphere (0–3 km), errors are larger for q and lower for θ. Statistically, there is no significant difference in the retrieved profiles on increasing the horizontal resolution. The setting choice is a compromise between spatial detail needs and computational cost. Furthermore, the overall results did not indicate any particular PBL scheme that stands out in all case days. The performance depends on the meteorological conditions that prevail in the different cases. The WRF model and even data directly from NCEP CFSv2 reanalysis are useful to represent the vertical structure of the atmosphere when local radiosondes are not available.

Relationship between Prediction Skill of Surface Winds in Average of Weeks 1–4 and Interannual Variability over the Western Pacific and Indian Ocean

AbstractThis study examines the possible relationship between predictions of weekly and biweekly averages of 10-m winds at 3-week lead time and interannual variability over the western Pacific and Indian Ocean (WP-IO) using Climate Forecast System version 2 (CFSv2) reforecasts for period 1979–2008. There is a large temporal correlation between forecasts and reanalyses for zonal, meridional, and total wind magnitudes at 10 m over most of WP-IO for the average of weeks 1 and 2 (W1 and W2) in reforecasts initialized in January (JIR) and May (MIR). The model has some correlations that exceed 95% confidence in some portions of WP-IO in week 3 (W3) but no skill in week 4 (W4) over most of the region. The model depicts prediction skill in the 14-day average of weeks 3–4 (W3–4) over portions of WP-IO, similar to the level of skill in W3. The amplitude of interannual variability (IAV) for 10-m winds in W1 of JIR and MIR is close to that in reanalyses. As lead time increases, the amplitude of IAV of 10-m winds gradually decreases over WP-IO in reforecasts, in contrast to behavior in reanalyses. The amplitude of IAV of predicted 10-m winds in W3–4 over WP-IO is equivalent to that in W3 and W4 in reforecasts. In contrast, the amplitude of IAV in W3–4 in January and May of the reanalysis is much smaller than IAV of W3 and W4. Therefore, one of the possible causes for prediction skill in W3–4 over subregions of WP-IO is due to a reduction of IAV bias in W3–4 in comparison to IAV bias in W3 and W4.

Reducing Systematic Biases Over the Indian Region in CFS V2 by Dynamical Downscaling

AbstractThe usefulness of dynamical downscaling of seasonal reforecasts of Indian Monsoon is explored to address the seasonal mean biases in the reforecasts. Almost all the current generation global coupled models, including the Climate Forecast System version 2 (CFSv2, T126 ∼110 km), exhibit systematic mean dry bias over the central Indian region during the summer monsoon season. Cold sea surface temperature (SST) biases in the Indian Ocean and a weak monsoon circulation due to a colder tropospheric temperature contribute to this dry bias. Such systematic biases restrict the use of skillful forecasts from these models in climate applications (such as agriculture or hydrology). Dynamical downscaling of seasonal forecasts (∼110 km resolution) using the Weather Research and Forecasting (WRF) model coupled to a simple ocean mixed layer model (OML; WRFOML) at 38 km resolution significantly reduces the majority of the systematic biases reported earlier. The seasonal mean dry bias reduces to 16% in WRFOML as compared to 44% (33%) in the CFSv2‐T126 (WRFCTL) over the Indian land region. Warmer SSTs in the Indian Ocean and a more robust monsoon circulation emanating from a realistic simulation of the tropospheric temperature reduced the systematic biases in WRFOML compared to CFSv2‐T126 and WRFCTL. Additionally, category‐wise rainfall distributions are also improved drastically in the downscaled simulations (WRFOML). Downscaled reforecasts with reduced systematic biases have better suitability for climate applications.

Multi-Model Multi-Physics Ensemble: A Futuristic Way to Extended Range Prediction System

In an endeavor to design better forecasting tools for real-time prediction, the present work highlights the strength of the multi-model multi-physics ensemble over its operational predecessor version. The exiting operational extended range prediction system (ERPv1) combines the coupled, and its bias-corrected sea-surface temperature forced atmospheric model running at two resolutions with perturbed initial condition ensemble. This system had accomplished important goals on the sub-seasonal scale skillful forecast; however, the skill of the system is limited only up to 2 weeks. The next version of this ERP system is seamless in resolution and based on a multi-physics multi-model ensemble (MPMME). Similar to the earlier version, this system includes coupled climate forecast system version 2 (CFSv2) and atmospheric global forecast system forced with real-time bias-corrected sea-surface temperature from CFSv2. In the newer version, model integrations are performed six times in a month for real-time prediction, selecting the combination of convective and microphysics parameterization schemes. Additionally, more than 15 years hindcast are also generated for these initial conditions. The preliminary results from this system demonstrate appreciable improvements over its predecessor in predicting the large-scale low variability signal and weekly mean rainfall up to 3 weeks lead. The subdivision-wise skill analysis shows that MPMME performs better, especially in the northwest and central parts of India.

Skillful Subseasonal Prediction of United States Extreme Warm Days and Standardized Precipitation Index in Boreal Summer

AbstractSkillful subseasonal prediction of extreme heat and precipitation greatly benefits multiple sectors, including water management, public health, and agriculture, in mitigating the impact of extreme events. A statistical model is developed to predict the weekly frequency of extreme warm days and 14-day standardized precipitation index (SPI) during boreal summer in the United States (US). We use a leading principal component of US soil moisture and an index based on the North Pacific sea surface temperature (SST) as predictors. The model outperforms the NCEP’s Climate Forecast System version 2 (CFSv2) at weeks 3-4 in the eastern US. It is found that the North Pacific SST anomalies persist several weeks and are associated with a persistent wave train pattern (WTZ500), which leads to increased occurrences of blocking and extreme temperature over the eastern US. Extreme dry soil moisture conditions persist into week 4 and are associated with an increase in sensible heat flux and decrease in latent heat flux, which may help maintain the overlying anticyclone. The clear sky conditions associated with blocking anticyclones further decrease soil moisture conditions and increase the frequency of extreme warm days. This skillful statistical model has the potential to aid in irrigation scheduling, crop planning, reservoir operation, and provide mitigation of impacts from extreme heat events.

Role of autoconversion process in assessing the low-level clouds over the southern Indian Ocean in Climate Forecast System (CFS) version 2

The sensitivity of a modified realistic critical droplet radius concerning the rain autoconversion process on the simulation of low-level clouds over the southern Indian Ocean (SIO) has been investigated. Two simulations are performed with the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) model, one (CFSCR) with default physically based cloud scheme (WRF single-moment 6-class microphysics scheme (WSM6)) and the other with modified CFSCR (CFSCR-Mod). In CFSCR-Mod, the critical droplet radius as a function of the rain autoconversion process is revised. Keeping in mind the variability of cloud condensation nuclei (CCN) over the land and ocean, the critical droplet radius is put differently over land and ocean. The fidelity of both the models in simulating SIO low-level clouds is evaluated concerning different observational and re-analysis products. Observations exhibit the prominent occurrence of low-level clouds over the SIO in the 30–10°S latitudinal and 80–100°E longitudinal belt. The analysis reveals that CFSCR-Mod has a better fidelity to capturing low-level clouds due to its more realistic autoconversion process and realistic cloud microphysics.

An effective downscaling model for operational prediction of summer precipitation over China

A hybrid downscaling model for operational prediction of summer precipitation at 160 stations over China is proposed in this paper. The prediction model is conducted in February, and six predictors extending from the tropics to high latitudes with specific mechanisms are involved. Simultaneous sea level pressure over pan-East Asia and spring sea surface temperature (SST) over the tropical Atlantic Ocean are from the Climate Forecast System version 2 (CFSv2). The others are observational predictors including winter SST over the tropical Indian Ocean, sea ice concentration (SIC) over the Hudson Bay–Baffin Bay–Davis Strait in winter and the Barents–Kara Sea in autumn, and winter outgoing longwave radiation over the tropics. Different predictors impact the summer precipitation of different regions in China. Validations for schemes based on individual predictors (IP-schemes) and all predictors (HD-scheme) indicate that downscaling schemes outperform CFSv2 in predicting summer precipitation over China, especially the HD-scheme. The temporal and spatial anomaly correlation coefficient, root-mean-square errors, and ratio of the same sign (RSS) are estimated. For RSS (Anomalous RSS), the results are improved from 27.8% (23.8%) for CFSv2 to 77.8% (61.9%) for HD-scheme. Notably, the RSSs of IP-schemes based on SIC are equivalent to that of HD-scheme. This partly emphasizes the importance of preceding Arctic sea ice to summer rainfall in China. Moreover, two cases from hindcasts and two from the operational application are chosen for further validation of the prediction skill of HD-scheme. The case studies confirm that the hybrid downscaling model proposed in this paper not only outperforms CFSv2 but can also be applied well to real-time forecasting of summer precipitation over China.

Evaluation of Seasonal Forecasts for the Fire Season in Interior Alaska

AbstractIn this study, seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), are compared with station observations to assess their usefulness in producing accurate buildup index (BUI) forecasts for the fire season in Interior Alaska. These comparisons indicate that the CFSv2 June–July–August (JJA) climatology (1994–2017) produces negatively biased BUI forecasts because of negative temperature and positive precipitation biases. With quantile mapping (QM) correction, the temperature and precipitation forecasts better match the observations. The long-term JJA mean BUI improves from 12 to 42 when computed using the QM-corrected forecasts. Further postprocessing of the QM-corrected BUI forecasts using the quartile classification method shows anomalously high values for the 2004 fire season, which was the worst on record in terms of the area burned by wildfires. These results suggest that the QM-corrected CFSv2 forecasts can be used to predict extreme fire events. An assessment of the classified BUI ensemble members at the subseasonal scale shows that persistently occurring BUI forecasts exceeding 150 in the cumulative drought season can be used as an indicator that extreme fire events will occur during the upcoming season. This study demonstrates the ability of QM-corrected CFSv2 forecasts to predict the potential fire season in advance. This information could, therefore, assist fire managers in resource allocation and disaster response preparedness.

Revisit of a Case Study of Spilled Oil Slicks Caused by the Sanchi Accident (2018) in the East China Sea

Marine oil spills occur suddenly and pose a serious threat to ecosystems in coastal waters. Oil spills continuously affect the ocean environment for years. In this study, the oil spill caused by the accident of the Sanchi ship (2018) in the East China Sea was hindcast simulated using the oil particle-tracing method. Sea-surface winds from the European Centre for Medium-Range Weather Forecasts (ECMWF), currents simulated from the Finite-Volume Community Ocean Model (FVCOM), and waves simulated from the Simulating WAves Nearshore (SWAN) were employed as background marine dynamics fields. In particular, the oil spill simulation was compared with the detection from Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) images. The validation of the SWAN-simulated significant wave height (SWH) against measurements from the Jason-2 altimeter showed a 0.58 m root mean square error (RMSE) with a 0.93 correlation (COR). Further, the sea-surface current was compared with that from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2), yielding a 0.08 m/s RMSE and a 0.71 COR. Under these circumstances, we think the model-simulated sea-surface currents and waves are reliable for this work. A hindcast simulation of the tracks of oil slicks spilled from the Sanchi shipwreck was conducted during the period of 14–17 January 2018. It was found that the general track of the simulated oil slicks was consistent with the observations from the collected GF-3 SAR images. However, the details from the GF-3 SAR images were more obvious. The spatial coverage of oil slicks between the SAR-detected and simulated results was about 1 km2. In summary, we conclude that combining numerical simulation and SAR remote sensing is a promising technique for real-time oil spill monitoring and the prediction of oil spreading.

Worldwide continuous gap-filled MODIS land surface temperature dataset

Satellite land surface temperature (LST) is vital for climatological and environmental studies. However, LST datasets are not continuous in time and space mainly due to cloud cover. Here we combine LST with Climate Forecast System Version 2 (CFSv2) modeled temperatures to derive a continuous gap filled global LST dataset at a spatial resolution of 1 km. Temporal Fourier analysis is used to derive the seasonality (climatology) on a pixel-by-pixel basis, for LST and CFSv2 temperatures. Gaps are filled by adding the CFSv2 temperature anomaly to climatological LST. The accuracy is evaluated in nine regions across the globe using cloud-free LST (mean values: R2 = 0.93, Root Mean Square Error (RMSE) = 2.7 °C, Mean Absolute Error (MAE) = 2.1 °C). The provided dataset contains day, night, and daily mean LST for the Eastern Mediterranean. We provide a Google Earth Engine code and a web app that generates gap filled LST in any part of the world, alongside a pixel-based evaluation of the data in terms of MAE, RMSE and Pearson’s r.

Decadal prediction skill for spring and summer surface air-temperature over India and its association with SST patterns in CFSv2 and CNRM coupled models

In this study, we have assessed the skill of decadal prediction of boreal spring (March–May) and summer (June–September) Surface Air Temperature (SAT) over India and its relation with Sea Surface Temperature (SST) in the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) coupled model. The skill of CFSv2 is compared with CNRM (Centre National de Recherches Meteorologiques) coupled model, which is the best among the selected CMIP5 (Coupled Model Intercomparison Project 5) models for long-lead forecasts (6–9 years) of global SSTs (with high skill). It is found that both models show significant skill in predicting 4-year mean SAT over central and southern peninsular India at 1–5 year leads in spring. During summer, significant skills for SAT over the northwest and southeast India are seen in CFSv2, whereas CNRM displayed significant skills over the north and central India at 1–5 years lead. The first Empirical Orthogonal Function (EOF) mode of SAT variability over India indicates a country-wide warming/cooling pattern in both observations and models for spring and summer. The analysis reveals that the decadal variability of SAT (EOF-1) over India is highly related to SST variations over the Indo-Pacific and North Atlantic regions. The strong convergence of low-level winds over the equatorial Indian Ocean and maritime continent accompanied by warm SST anomalies drive the northerly dry winds over India and favour warm SAT in both spring and summer. Further, changes in shortwave radiation also contributed to SAT variability over India. In general, the SAT relationship with SST in different parts of tropical and sub-tropical regions is underestimated in CFSv2 compared to the observations and CNRM in both boreal spring and summer. The models are able to represent the changes in the atmospheric circulation and related Indo-Western Pacific SST patterns reasonably well at the 1–5 years lead with some discrepancy. However, both models showed relatively low skills in capturing the relationship between SAT over India and equatorial Pacific SSTs. This might limit the skills of models in predicting decadal variations of SAT over India.

A Hybrid Ensemble Canonical Correlation Prediction Model of the Winter Siberian High

AbstractAs one of the main components of the East Asian winter monsoon, the winter Siberian High (SH) plays an important role in the variability of East Asian climate. However, the Climate Forecast System, version 2 (CFSv2), shows limited prediction skill for the winter SH. To improve the prediction skill, a hybrid ensemble canonical correlation (ECC) prediction model is established for the winter SH and SH intensity index (SHI) basing the year‐to‐year increment method and an efficient downscaling approach. Hence, three preceding predictors from observation/reanalysis [sea‐ice concentration (SIC), snow‐cover extent (SCE), and sea surface temperature (SST)] and one integrated current predictor from CFSv2 [surface air temperature and sea level pressure (SAT&SLP)] are selected based on their fundamental physical roles. Considering these individual predictors, four separate downscaling schemes are constructed. The regional‐mean temporal anomaly correlation coefficient (ACC) of the winter SH for each scheme is 0.54 (SIC‐scheme‐SH), 0.50 (SCE‐scheme‐SH), 0.72 (SST‐scheme‐SH) and 0.42 (SAT&SLP‐scheme‐SH) [four schemes exceed significant at the 1% level]. However, the skill of each scheme differs in regional and temporal distribution. Thus, a hybrid ECC prediction model is proposed by employing multiple linear regression. The regional‐mean temporal ACC of the winter SH between the observed and predicted results increases from −0.12 (CFSv2; not significant at the 10% level) to 0.85 (significant at the 1% level). Besides, the correlation coefficient between the observation and hybrid ECC scheme for winter SHI is 0.90 (significant at the 1% level). Furthermore, the strongest winter SH in 2012 is reproduced well by ECC‐scheme‐SH.

A modelling framework for a better understanding of the tropically-forced component of the Indian monsoon variability

An experimental technique is introduced for controlling atmospheric diabatic heating in a fully-coupled climate model (NCEP Climate Forecast System version 2), with the motivation of better understanding the role of tropical forcing in modulating the Indian summer monsoon and contributing to global seasonal prediction skill. The ‘added heating’ mechanistic approach has the benefit of not interfering with any internal model feedbacks. The approach is applied in an iterative fashion to correct bias in the three-dimensional tropical heating over the Indo-Pacific, defined as the difference between the model ensemble mean heating and that estimated from ERA-Interim. The June through September seasonal mean, trend and parabolic fit in diabatic heating are corrected for each year separately over a 20-year set of seasonal re-forecasts, resulting in a 60–90% reduction in the mean-squared error of Indo-Pacific heating from an initial set of control re-forecasts, two-thirds of which is associated with the climatological bias. This results in higher skill in the climatological mean and inter-annual variability of local low-level winds and the underlying sea surface temperature (SST). Improvements are most significant over the equatorial Indian Ocean with a removal of the climatological low-level westerly bias and SST gradient bias along with substantially more skill in inter-annual variability. Although there is very little improvement in the model rainfall over India, there is improvement in the low-level circulation. The lack of significant improvement in rainfall prediction may be partly related to the representation of convection and/or the coarse resolution grid of the model.

Use of the dry-spell seasonal forecast in crop management decisions

Climate forecasts have shown the potential for improving the resilience of agriculture to climate change. The usefulness of climate forecasts for applications in agriculture can be enhanced if the forecasts are translated into agricultural outlooks, in which the information is targeted for decision-making. The sequence of dry periods is necessary for successful crop management decisions, especially in dry season planting. This paper investigates how well the Climate Forecast System version 2 (CFSv2) seasonal forecasts predict the dry spell (DS) over the Indonesia region. The seasonal forecasts were downscaled using the constructed analogue method and, in turn, were corrected with TRMM 3B42 rainfall data to match daily precipitation totals. The DS is defined as rainfall less than 5 mm day−1 for ≥10 consecutive days. Accuracy of the DS prediction was assessed using the Brier Score (BS) method for December-January-February (DJF) and June-July-August (JJA) periods. The results demonstrate that the highest accuracy of the DS forecast occurred in JJA in southern part Indonesia with a range of the BS value between 0-0.2 (>80%). The operational DS seasonal forecasts are needed to manage agriculture practices for the upcoming planting season such as the choice of a crop/variety, supplementary irrigation, and crop water requirement.

Evaluation of the ability of CFSv2 to forecast polar vortex displacements in the stratosphere of the Northern hemisphere

In this paper, the ability of the CFSv2 model to forecast the polar vortex displacements in the stratosphere of the Northern hemisphere is evaluated. We use the ERA-I reanalysis daily data of the potential vortex (pv) and the horizontal wind; vertical wind in the 430, 475, 530, and 600 K in the potential temperature field; and the temperature in the geopotential height at 50 hPa. And the 1–850 hPa isobaric surface temperature and u, v wind field daily data from the 9-month business forecast data of the Climate Forecast System Version 2 (CFSv2). The two polar vortex displacement events shift to Eurasia that occurred in Feb 2008 and Jan 2012. The results show that the polar vortex displacement events in February 2008 and January 2012 shifted toward the European region. During the polar vortex displacements, the westerly wind at 60°N latitude decreases, the temperature gradient reverses at the north polar and 60°N latitudes at 50 hPa, and a weaker stratospheric warming occurs in this process. For two of the polar vortex displacement events, the CFSv2 predictions of the start, largest, and end times of the polar vortex displacement are accurate at forecast lead times of 0 and 5 days, and the prediction results for the westerly winds and temperature gradients are also excellent. However, the CFSv2 prediction ability at forecast lead times exceeding 20 days is poor. For the polar vortex displacement event in February 2008, the CFSv2 predictions are good for the start and largest times but are not consistent with the actual situation for the end time at a forecast lead time of 10 days. The CFSv2 predictions are good for the end time but are not accurate for the start and largest times at a forecast lead time of 15 days. For the polar vortex displacement event in January 2012, the CFSv2 predictions are poor for the start, largest and end times and for the zonal wind, but are good for the temperature gradient during the displacement at forecast lead times of 10 days. The change in the polar vortex boundary and zonal wind and temperature gradients cannot be predicted at forecast lead times of 15 days.