Climate Forecast System Version 2 Model Research Articles

A hybrid statistical downscaling prediction model with timescale decomposition for summer precipitation over Hainan Island, China

AbstractBy observing well‐known sea surface temperature (SST) indices and outputs from Climate Forecast System version 2 (CFSv2) hindcasts and predictions, in this study, we developed a hybrid statistical downscaling prediction model (HSDPM) based on a timescale decomposition approach, which effectively improved the forecasting ability of summer (June–August) precipitation over Hainan Island (HNSP) in China; this precipitation presented multiple obvious timescale variabilities. We found that the interannual variability of the HNSP is closely related to the observed North Atlantic tripole SST anomalies in the preceding winter and the summer sea level pressure over the South China Sea by CFSv2 predicted in March. On the other hand, the interdecadal variability in the HNSP is linked to the prewinter Pacific Interdecadal Oscillation (PDO) according to the observations, the CFSv2 predictions of the summer surface air temperature over the tropical central‐east Pacific. On this basis, both the interannual and interdecadal components of the HNSP are effectively predicted using the corresponding predictors via multiple regression; thus, the HSDPM is ultimately established by combining the above two components. Compared with the original CFSv2 model, the HSDPM model achieves a considerable improvement in performance in predicting the HNSP. Specifically, the temporal correlation coefficient, ratio of root‐mean‐square error and anomaly sign consistency rate between the observed and HSDPM‐predicted HNSPs are 0.70, 17.4% and 80.5%, respectively, which are significantly greater than the above three forecasting index values of 0.23, 38.3% and 48.8%, respectively, obtained from the original CFSv2 predictions. The application of the HSDPM may be beneficial for drought and flood prevention and mitigation in Hainan Island.

Multi-model seasonal prediction of global surface temperature based on partial regression correction method

The increased climate change is having a huge impact on the world, with the climatic change sensitive and vulnerable regions at significant risk particularly. Effective understanding and integration of climate information are essential. It helps to reduce the risks associated with adverse weather conditions and to better adapt to the impacts of climate variability and change. Using the hindcast data from Japan Meteorological Agency/Meteorological Research Institute (JMA/MRI) coupled prediction system version 2 (JMA/MRI-CPS2), National Centers for Environmental Prediction (NCEP) Climate Forecast System model version 2 (CFSv2), and Canadian Centre for Climate Modeling and Analysis (CCCma) Coupled Climate Model, versions 3 (CanCM3) seasonal prediction model systems, the performance of seasonal prediction for global surface temperature in boreal summer and winter is comprehensively evaluated and compared for 1982–2011 from the perspective of deterministic and probabilistic forecast skills in this study, and a partial regression correction (PRC) method is introduced to correct seasonal predictions. The results show high prediction skills in the tropics, particularly in the equatorial Pacific, while poor skills on land. In general, JMA/MRI-CPS2 has slightly better prediction performance than CFSv2 and CanCM3 in the tropics. CFSv2 is generally superior to JMA/MRI-CPS2 and CanCM3 in the extratropical northern hemisphere and East Asia, especially for the abnormal low winter temperature prediction in East Asia. CanCM3 shows good deterministic forecast skills in extra-tropics but performs slightly worse in probabilistic forecasting. Based on the respective strengths of each seasonal prediction model, an ensemble forecast correction with observational constraint is implemented by partial regression, and the improvement of skills in ensemble predicting has been analyzed. Compared to the simple multi-model ensemble (MME), the correction improved the global-average temporal correlation coefficient and multi-year mean anomaly correlation coefficient by about 0.1 and 0.13, respectively. The validation tests indicate that the corrected ensemble forecast has higher ranked probability skill scores than that of the MME, which is improved by more than 0.06 in the tropics. Meanwhile, when the training period is sufficiently long, it may have the potential for future seasonal temperature predictions from the perspective of stable zonal partial regression coefficients.

New downscaling prediction models for spring drought in China

AbstractAccurate prediction of spring drought in China is helpful toward reducing associated agricultural losses. In this study, spring drought is defined as the three‐month Standardized Precipitation Evapotranspiration Index (SPEI) ending in May. Based on the year‐to‐year increment and downscaling method, three single‐predictor prediction models (P1 model, P2 model, and P3 model) and two multi‐predictor models for spring drought at 677 stations in China are developed for the period 1983–2020. As physical processes affecting spring drought in the China region, the tropical Pacific–Indian Ocean sea surface temperature (SST) in winter, the Davis Strait–Barents sea‐ice concentration (SIC) in winter, and the 500‐hPa spring vertical velocity, predicted by the Climate Forecast System, version 2 (CFSv2), are considered in the prediction models. The prediction skill of the downscaling models for the spring SPEI is measured by cross‐validation for the period 1983–2020. The CFSv2 model only shows convincing prediction skill for the spring SPEI at 35 of 677 stations. However, among the 677 stations, the temporal correlation coefficient between the observed and predicted spring SPEI at 621 stations for P1 model, 598 stations for P2 model, 545 stations for P3 model, 674 stations for the statistical downscaling model (SD model), and 675 stations for the hybrid downscaling dynamical–statistical prediction model (HD model) exceeds the 95% confidence level. Therefore, compared to the CFSv2 model, the prediction skill for spring drought is improved by the single‐predictor and multi‐predictor models. The prediction skill of HD model for spring drought, which combines preceding observational predictors and the simultaneous predictor of the CFSv2 model, is higher than that of SD model. In addition, the severe drought that occurred in Northeast and North China in spring 2017 can be successfully predicted by HD model.

Performance of probabilistic forecast of the onset of the rainy season over Java Island based on the application of Constructed Analogue (CA) method on Climate Forecast System Version 2 (CFSV2) model output

The onset of the rainy season is one of the forecast products that is issued regularly by the Indonesian Agency of Meteorology, Climatology, and Geophysics (BMKG), with deterministic information about the month of which the initial 10-days (dasarian) of the rainy season will occur in each a designated area. On the other hand, state-of-the-art of seasonal forecasting methods suggests that probabilistic forecast products are potentially better for decision making. The probabilistic forecast is also more suitable for Indonesia because of the large rainfall variability that adds up to uncertainty in climate model simulations, besides complex geographical factors. The research aims to determine the onset of rainy season and monsoon over Java Island based on rainfall prediction by Constructed Analogue statistical downscaling of CFSv2 (Climate Forecast System version 2) model output. This research attempted to develop a method to produce a probabilistic forecast of the onset of the rainy season, as well as monsoon onset, by utilizing the freely available seasonal model output of CFSv2 operated by the US National Oceanic and Atmospheric Administration (NOAA). In this case, the output of the global model is dynamically downscaled using the modified Constructed Analogue (CA) method with an observational rainfall database from 26 BMKG stations and TRMM 3B43 gridded dataset. This method was then applied to perform hindcast using CFS-R (re-forecast) for the 2011-2014 period. The results show that downscaled CFS predictions with initial data in September (lead-1) give sufficient accuracy, while that initialized in August (lead-2) have large errors for both onsets of the rainy season and monsoon. Further analysis of forecast skill using the Brier score indicates that the CA scheme used in this study showed good performance in predicting the onset of the rainy season with a skill score in the range of 0.2. The probabilistic skill scores indicate that the prediction for East Java is better than the West- and Central-Java regions. It is also found that the results of CA downscaling can capture year-to-year variations, including delays in the onset of the rainy season.

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.

Hydrodynamic and Waves Response during Storm Surges on the Southern Brazilian Coast: A Hindcast Study

The Southern Brazilian Coast is highly susceptible to storm surges that often lead to coastal flooding and erosive processes, significantly impacting coastal communities. In addition, climate change is expected to result in expressive increases in wave heights due to more intense and frequent storms, which, in conjunction with sea-level rise (SLR), has the potential to exacerbate the impact of storm surges on coastal communities. The ability to predict and simulate such events provides a powerful tool for coastal risk reduction and adaptation. In this context, this study aims to investigate how accurately storm surge events can be simulated in the Southwest Atlantic Ocean employing the coupled ADCIRC+SWAN hydrodynamic and phase-averaged wave numerical modeling framework given the significant data scarcity constraints of the region. The model’s total water level (TWL) and significant wave height (Hs) outputs, driven by different sources of meteorological forcing, i.e., the Fifth Generation of ECMWF Atmospheric Reanalysis (ERA 5), the Climate Forecast System Version 2 (CFSv2), and the Global Forecast System (GFS), were validated for three recent storm events that affected the coast (2016, 2017, and 2019). In order to assess the potentially increasing storm surge impacts due to sea-level rise, a case study was implemented to locally evaluate the modeling approach using the most accurate model setup for two 2100 SLR projections (RCP 4.5 and 8.5). Despite a TWL underestimation in all sets of simulations, the CFSv2 model stood out as the most consistent meteorological forcing for the hindcasting of the storm surge and waves in the numerical model, with an RMSE range varying from 0.19 m to 0.37 m, and an RMSE of 0.56 m for Hs during the most significant event. ERA5 was highlighted as the second most accurate meteorological forcing, while adequately simulating the peak timings. The SLR study case demonstrated a possible increase of up to 82% in the TWL during the same event. Despite the limitations imposed by the lack of continuous and densely distributed observational data, as well as up to date topobathymetric datasets, the proposed framework was capable of expanding TWL and Hs information, previously available for a handful of gauge stations, to a spatially distributed and temporally unlimited scale. This more comprehensive understanding of such extreme events represents valuable knowledge for the potential implementation of more adequate coastal management and engineering practices for the Brazilian coastal zone, especially under changing climate conditions.

An assessment of radiative flux biases in the climate forecast system model CFSv2

An extensive analysis of radiative flux biases in the Climate Forecast System Model Version 2 (CFSv2) is done. Annual mean and seasonal variations of biases at the surface and top of the atmosphere (TOA) are reported in the global domain. Large regional biases in shortwave (SW) and longwave (LW) radiation are observed over convectively active zones in the tropics. The relative contribution of various processes responsible for the reported biases is quantified. The poor simulation of clouds and inadequate representation of surface properties seem to be major contributors. Over certain regions, errors due to different processes add up, whereas, over other regions, errors tend to nullify each other. Surface and atmospheric variables taken as input parameters in the radiative transfer modules are compared with satellite-based observations. The maximum biases in SW and LW radiation are observed over the regions of persistent low clouds. The magnitude of the SW and LW biases at the TOA is in phase with the biases in cloud fraction by and large. However, the error in the radiative fluxes due to errors in surface radiative properties is of equal importance. The cold bias in near-surface air temperature reported in other studies may partly be attributed to an underestimation in the net SW radiation at the surface. In the present study, a plausible prescription is also provided to correct the source of the biases.

Distinct atmosphere-ocean coupling processes on the onset phase of Indian summer monsoon during 2017 and 2018 as revealed through SCATSAT-1 and its comparison with CFSv2

ABSTRACT Distinct atmosphere-ocean coupled processes determine the inter-annual variability in the onset processes of Indian Summer Monsoon. In this work, we have analysed the distinct characteristics of onset process during 2017 (MOK17) and 2018 (MOK18) using SCATSAT-1 observations. Two years of dedicated SCATSAT-1 analysed winds and other flux datasets are used to understand distinct-coupled processes and further compared with coupled model forecasts from National Centres for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). Air-sea coupled mechanism during 2017 and 2018 suggests that there is a delayed response (about one pentad) of atmosphere-ocean coupling over North Indian Ocean on the monsoon onset and its propagation during 2018. It is found that the influence of cyclone Mekunu formed over Western Arabian Sea disturbed the onset vortex formed over Eastern Arabian Sea during MOK18. The presence of cyclone Mekunu results in a sudden spike of East-west Sea Surface Temperature gradient, Latent Heat Flux and wind stress over Arabian Sea during one pentad before monsoon onset over Kerala. This created an onset like situation in the next pentad, and later the conditions are not favoured for the further progress of the monsoon. It leads to a weak onset followed by a sluggish and halted northward progression of monsoon during 2018. In the absence of a cyclonic system during the onset phase of MOK17, conventional onset and its further progress took place during 2017. Further comparison of SCATSAT-1 and other analysed data sets with the CFSv2 model show that the CFSv2 model is able to capture the atmosphere-ocean coupled processes during the onset phase up to four pentads in advance during MOK17. However, due to the presence of Mekunu cyclone before the monsoon onset limited the forecast skill of CFSv2 only up to two pentads in advance during MOK18. We found that the presence of strong convective instability due to strong synoptic-scale system can influence the skill of coupled model forecast in the extended range. The new SCATSAT-1 observations provide high-quality observations that can be used to quantify the errors and identify common biases in the coupled models.

Sensitivity of Dynamical Downscaling Seasonal Precipitation Forecasts to Convection and Land Surface Parameterization in a High-Resolution Regional Climate Model

Both convection and land surface parameterization influence seasonal precipitation forecasts. In this study, the sensitivity of dynamical downscaling seasonal precipitation forecasts to convection and land surface parameterization was investigated by nesting the Weather Research and Forecasting (WRF) model into the NCEP’s Climate Forecast System version 2 (CFSv2) retrospective forecasts with four convective schemes: Kain–Fritsch (KF), Betts–Miller–Janjic (BMJ), Grell–Freitas (GF), and new simplified Arakawa–Schubert (NSAS) schemes, and two land surface schemes: Noah and simplified Simple Biosphere (SSiB) schemes over the Han River basin. The CFSv2 model biases are reduced when the KF convective scheme is used in the wet summer season. However, negative biases still exist especially when the combination of BMJ and SSiB schemes is used. Compared with CFSv2 reforecasts and other combinations of schemes, the forecast skills of spatial patterns of precipitation anomalies are highest when the combination of KF and Noah schemes is used in summer. In contrast, the combination of BMJ and SSiB schemes shows lowest forecast skills in summer. To understand the causes of the differences in precipitation forecasts using different parameterization schemes, the simulated moisture flux convergence, thermodynamic parameters at different pressure levels, convective available potential energy (CAPE), convective inhibition (CIN), and heat fluxes are compared with the data in the ERA-5 reanalysis dataset. The WRF model-simulated moisture flux convergence is closer to that of the ERA-5 reanalysis compared with that of the CFSv2 reforecasts in summer. The vertical thermodynamic profiles also suggest that the combination of the KF and Noah schemes has caused a more unstable atmosphere, which is crucial for precipitation. In contrast, the combination of BMJ and SSiB schemes shows a less unstable atmospheric environment in summer, which explains the lower forecast skills compared with other schemes. The spatial patterns of CAPE are also improved when using the WRF model, which further enhances the precipitation forecast skills over the Han River basin.

Why is the North Atlantic Oscillation More Predictable in December?

The prediction skill of the Climate Forecast System, version 2 (CFSv2), for the North Atlantic Oscillation (NAO) is evaluated in three winter months (December, January, and February). The results show that the CFSv2 model can skillfully predict the December NAO one month in advance. There are two main contributors to NAO predictability in December. One is the predictability of the relationship between the North Atlantic sea surface temperature anomaly (SSTA) tripole and the NAO and the other is the second empirical orthogonal function (EOF) mode of the geopotential height at 50 hPa (Z50-EOF2). The relationship between the NAO and SSTA tripole index in December is the most significant in the three winter months. The significant monthly differences of surface heat fluxes in December over the whole North Atlantic are favorable for promoting the interaction between the NAO and North Atlantic SSTAs, in addition to improving the predictability of the December NAO. When the NAO is in a positive phase, easterly anomalies are located at the low and high latitudes and westerly anomalies prevail in the mid-latitudes of the troposphere. The correlation between the December Z50-EOF2 and zonal-mean zonal wind anomalies shows a similar spatial structure to that for the NAO. The possible reason why the CFSv2 model can predict the December NAO one month ahead is that it can reasonably reproduce the relationship between the December NAO and both the North Atlantic SST and stratospheric circulation.

Improving Arctic sea ice seasonal outlook by ensemble prediction using an ice-ocean model

An ensemble based Sea Ice Seasonal Prediction System (SISPS) is configured towards operationally predicting the Arctic summer sea ice conditions. SISPS runs as a pan-Arctic sea ice-ocean coupled model based on Massachusetts Institute of Technology general circulation model (MITgcm). A 4-month hindcast is carried out by SISPS starting from May 25, 2016. The sea ice-ocean initial fields for each ensemble member are from corresponding restart files from an ensemble data assimilation system that assimilates near-real-time Special Sensor Microwave Imager Sounder (SSMIS) sea ice concentration, Soil Moisture and Ocean Salinity (SMOS) and CryoSat-2 ice thickness. An ensemble of 11 time lagged operational atmospheric forcing from the National Center for Environmental Prediction (NCEP) climate forecast system model version 2 (CFSv2) is used to drive the ice-ocean model. Comparing with the satellite based sea ice observations and reanalysis data, the SISPS prediction shows good agreement in the evolution of sea ice extent and thickness, and performs much better than the CFSv2 operational sea ice prediction. This can be largely attributed to the initial conditions that we used in assimilating the SMOS and CryoSat-2 sea ice thickness data, thereafter reduces the initial model bias in the basin wide sea ice thickness, while in CFSv2 there is no sea ice thickness assimilation. Furthermore, comparisons with sea ice predictions driven by deterministic forcings demonstrate the importance of employing an ensemble approach to capture the large prediction uncertainty in Arctic summer. The sensitivity experiments also show that the sea ice thickness initialization that has a long-term memory plays a more important role than sea ice concentration and sea ice extent initialization on seasonal sea ice prediction. This study shows a good potential to implement Arctic sea ice seasonal prediction using the current configuration of ensemble system.

Sub-seasonal prediction skill for the stratospheric meridional mass circulation variability in CFSv2

This study evaluates the prediction skill for the stratospheric mass circulation variability in winter (November–March) in the Climate Forecast System, version 2 (CFSv2), from 2011 to 2018. Three stratospheric mass circulation indices measuring meridional mass transport into the polar stratosphere by the total flow (ST60N), wavenumber-1 (ST60N_W1), and wavenumber-2 waves (ST60N_W2) are considered. The variability of ST60N is mainly contributed by ST60N_W1 and ST60N_W2, and these indices are good indicators of the timing and locations of the continental-scale cold-air outbreaks (CAOs) at mid-latitudes. Thus, their potentially useful prediction skill can be utilized to make sub-seasonal forecasts of CAOs in a dynamical and statistical hybrid paradigm. Systematic forecast bias is found in both the 7-year averaged winter mean and seasonal cycle, which is tied to the overestimation of damping in amplitude and westward tilting variations of total waves and difficulties in forecasting the exact contributions from different spatial scales of waves. The intraseasonal variations of stratospheric mass circulation indices, with the systematic forecast bias corrected, can be modestly predicted at a forecast lead time of about 20 days, in terms of both the anomaly value and timing of negative and positive peak events. The sub-seasonal predictability of the ST60N value mainly comes from the ST60N_W1, while the predictability of the timing of positive and negative peaks comes from the ST60N_W2. The 20-day prediction limit of the stratospheric mass circulation indices is mainly due to the 2-week limit of the CFSv2 model in predicting the variability of anomalous wave tilt angle, whereas the prediction limit of the wave amplitude anomaly can exceed 50 days.

Skill Evaluation of Extended-Range Forecasts of Rainfall and Temperature over the Meteorological Subdivisions of India

Abstract Under the National Monsoon Mission Project initiated by the government of India’s Ministry of Earth Sciences, an indigenous dynamical ensemble prediction system (EPS) has been developed at the Indian Institute of Tropical Meteorology based on the state-of-the-art Climate Forecast System Model version 2 (CFSv2) coupled model, for extended-range (~15–20 days in advance) prediction. The forecasts are generated for the entire year covering the southwest monsoon, the northeast monsoon, and the summer and winter seasons. As the forecast of rainfall is important during the southwest and northeast monsoon seasons, along with that of the temperature during the summer and winter seasons, the present study documents the deterministic as well as probabilistic skill of the EPS in predicting the results in the respective seasons, over various meteorological subdivisions throughout India, on a pentad-lead time scale. The EPS is found to be skillful in predicting rainfall during the southwest and northeast monsoon seasons, as well as temperature during the summer and winter seasons, across different subdivisions of India. In addition, the EPS is noted to be skillful in predicting selected extremes in rainfall and temperature. This affirms the reliability and usefulness of the present EPS from an operational perspective.

Watershed-scale retrospective drought analysis and seasonal forecasting using multi-layer, high-resolution simulated soil moisture for Southeastern U.S

Drought assessment at local scales needs a reliable framework capturing both local landscape processes and the watershed-scale hydrological responses. Any such tool, implementable in a near real-time, semi-automated framework, is largely unavailable for the South Atlantic-Gulf (SAG) region in the Southeastern US (SEUS). In this study, we evaluate a drought monitoring and forecasting approach using multi-layer, high resolution, simulated soil moisture for 50 watersheds in the SEUS. Soil and Water Assessment Tool (SWAT) is integrated with meteorological drivers (precipitation and temperature) from Climate Forecast System Reanalysis data (CFSR) for retrospective simulations (January 1982–December 2013), and Climate forecast system model version-2 (CFSv2) models to obtain the near real-time estimates (January 2014 through mid-March 2017) and 9-month lead forecasts (mid-March through December 2017) of the hydrologic variables at 12-digit Hydrologic Unit Code (HUC-12) resolution. Drought assessment is carried out by combining the drought severity estimates of the weekly percentiles of the surface and total column (TC) soil moisture, aggregated to 4-week and 8-week respectively, following a different severity classification scheme for each layer. Several drought indices and observed drought maps from the U.S. Drought Monitor (USDM) are used to compare the agreement (or disagreement) of the proposed approach with the observed drought conditions in the region. The results show promising application of the proposed approach for near real-time estimation as the drought estimates show high (∼80–90%) Index of Agreement (IOA) with the Palmer Drought Severity Index (PDSI) for all drought categories (mild-exceptional). The retrospective assessment shows that TC soil moisture percentiles have high correlation (>0.7) with long-term drought indices. The surface soil moisture percentiles show high correlation (∼0.6) with Palmer Z Index (ZNDX) and 1-month Standardized Precipitation Index (SPI-1), while longer aggregations increase the association with the long-term drought indices. While the SWAT-CFSv2 based drought estimation is useful in near real-time mode, higher disagreement between the forecasted drought maps and the observed drought severity from the USDM is noted for a forecast window of greater than ∼ 4–6 weeks.

Simulation of synoptic features during summer monsoon onset over GWB, India, with CFSv2 coupled model: skill and bias assessment

The aim of this study is to examine the skill of climate forecast system version 2 (CFSv2) in simulating the synoptic features of Bay of Bengal (BOB) branch of summer monsoon (SM) during the onset over Gangetic West Bengal (GWB), India. Precise prediction of the onset time and the synoptic features associated with the onset is a major challenge in SM study. Better understanding of the synoptic and intra-seasonal variability during the propagation along with the mean simulation of monsoon features is crucial, especially for the operational models. The earlier studies focused mainly on the mean simulation of SM during June–September (JJAS) period. However, the main objective of the present study is to improve the understanding of CFSv2 model biases in simulating the synoptic features during the propagation of BOB branch of SM system till the onset over GWB. The skill of the coupled model is estimated for the years 2011 to 2015 with tropospheric temperature (TT), sea surface temperature (SST), mean sea level pressure (MSLP), winds at 850 and 500 hPa pressure levels, and rainfall rate (RR). The result shows that the observed characteristics are simulated, reasonably well, by CFSv2 model with quite high reliability unlike other coupled models. The CFSv2 has been able to simulate the position/variation during the onset; however, the model has not been able to estimate the intensity in some occasions. The gradients of pressure and SST have been slightly overestimated by the model. The model has not been able to simulate the winds at 850 and 500 hPa pressure levels in some occasions. The CFSv2 model in simulating the features during propagation of BOB branch of SM system shows disparity from observation in some occasions during 2011 to 2015. The result also reveals that the model biases remain unaltered during El Nino episode of 2011.

Sources of Bias in the Monthly CFSv2 Forecast Climatology

AbstractForecast climatologies are used to remove systematic errors from forecasts and to express forecasts as departures from normal. Forecast climatologies are computed from hindcasts by various averaging, smoothing, and interpolation procedures. Here the Climate Forecast System, version 2 (CFSv2), monthly forecast climatology provided by the NCEP Environmental Modeling Center (EMC) is shown to be biased in the sense of systematically differing from the hindcasts that are used to compute it. These biases, which are unexpected, are primarily due to fitting harmonics to hindcast data that have been organized in a particular format, which on careful inspection is seen to introduce discontinuities. Biases in the monthly near-surface temperature forecast climatology reach 2°C over North America for March targets and start times at the end of January. Biases in the monthly Niño-3.4 forecast climatology are also largest for start times near calendar-month boundaries. A further undesirable consequence of this fitting procedure is that the EMC forecast climatology varies discontinuously with lead time for fixed target month. Two alternative methods for computing the forecast climatology are proposed and illustrated. The proposed methods more accurately fit the hindcast data and provide a clearer representation of the CFSv2 model climate drift toward lower Niño-3.4 values for starts in March and April and toward higher Niño-3.4 values for starts in June, July, and August.

Evaluating the applicability of using daily forecasts from seasonal prediction systems (SPSs) for agriculture: a case study of Nepal’s Terai with the NCEP CFSv2

Ensemble forecasts from dynamic seasonal prediction systems (SPSs) have the potential to improve decision-making for crop management to help cope with interannual weather variability. Because the reliability of crop yield predictions based on seasonal weather forecasts depends on the quality of the forecasts, it is essential to evaluate forecasts prior to agricultural applications. This study analyses the potential of Climate Forecast System version 2 (CFSv2) in predicting the Indian summer monsoon (ISM) for producing meteorological variables relevant to crop modeling. The focus area was Nepal’s Terai region, and the local hindcasts were compared with weather station and reanalysis data. The results showed that the CFSv2 model accurately predicts monthly anomalies of daily maximum and minimum air temperature (Tmax and Tmin) as well as incoming total surface solar radiation (Srad). However, the daily climatologies of the respective CFSv2 hindcasts exhibit significant systematic biases compared to weather station data. The CFSv2 is less capable of predicting monthly precipitation anomalies and simulating the respective intra-seasonal variability over the growing season. Nevertheless, the observed daily climatologies of precipitation fall within the ensemble spread of the respective daily climatologies of CFSv2 hindcasts. These limitations in the CFSv2 seasonal forecasts, primarily in precipitation, restrict the potential application for predicting the interannual variability of crop yield associated with weather variability. Despite these limitations, ensemble averaging of the simulated yield using all CFSv2 members after applying bias correction may lead to satisfactory yield predictions.

A snow and ice melt seasonal prediction modelling system for Alpine reservoirs

Abstract. The timing and the volume of snow and ice melt in Alpine catchments are crucial for management operations of reservoirs and hydropower generation. Moreover, a sustainable reservoir operation through reservoir storage and flow control as part of flood risk management is important for downstream communities. Forecast systems typically provide predictions for a few days in advance. Reservoir operators would benefit if lead times could be extended in order to optimise the reservoir management. Current seasonal prediction products such as the NCEP (National Centers for Environmental Prediction) Climate Forecast System version 2 (CFSv2) enable seasonal forecasts up to nine months in advance, with of course decreasing accuracy as lead-time increases. We present a coupled seasonal prediction modelling system that runs at monthly time steps for a small catchment in the Austrian Alps (Gepatschalm). Meteorological forecasts are obtained from the CFSv2 model. Subsequently, these data are downscaled to the Alpine Water balance And Runoff Estimation model AWARE running at monthly time step. Initial conditions are obtained using the physically based, hydro-climatological snow model AMUNDSEN that predicts hourly fields of snow water equivalent and snowmelt at a regular grid with 50 m spacing. Reservoir inflow is calculated taking into account various runs of the CFSv2 model. These simulations are compared with observed inflow volumes for the melting and accumulation period 2015.

The IITM Earth System Model: Transformation of a Seasonal Prediction Model to a Long-Term Climate Model

AbstractWith the goal of building an Earth system model appropriate for detection, attribution, and projection of changes in the South Asian monsoon, a state-of-the-art seasonal prediction model, namely the Climate Forecast System version 2 (CFSv2) has been adapted to a climate model suitable for extended climate simulations at the Indian Institute of Tropical Meteorology (IITM), Pune, India. While the CFSv2 model has been skillful in predicting the Indian summer monsoon (ISM) on seasonal time scales, a century-long simulation with it shows biases in the ocean mixed layer, resulting in a 1.5°C cold bias in the global mean surface air temperature, a cold bias in the sea surface temperature (SST), and a cooler-than-observed troposphere. These biases limit the utility of CFSv2 to study climate change issues. To address biases, and to develop an Indian Earth System Model (IITM ESMv1), the ocean component in CFSv2 was replaced at IITM with an improved version, having better physics and interactive ocean biogeochemistry. A 100-yr simulation with the new coupled model (with biogeochemistry switched off) shows substantial improvements, particularly in global mean surface temperature, tropical SST, and mixed layer depth. The model demonstrates fidelity in capturing the dominant modes of climate variability such as the ENSO and Pacific decadal oscillation. The ENSO–ISM teleconnections and the seasonal leads and lags are also well simulated. The model, a successful result of Indo–U.S. collaboration, will contribute to the IPCC’s Sixth Assessment Report (AR6) simulations, a first for India.

Flood forecasting in the upper Uruguay River basin

Floods are common in the upper Uruguay River, and they may occur almost at any moment, because there are not defined rainfall seasonal patterns. Moreover, there is not an official model for flood forecasting in this basin. This study developed a methodology for 1-month flood forecasting in the upper region of the Uruguay River basin (≥3000 m3 s−1), from the headwaters to the Ita reservoir. The monthly version of the SMAP (Soil Moisture Accounting Procedure) model was presented and used to describe the transformation of rainfall into runoff, and the CFSv2 (Climate Forecast System version 2) model was used to provide rainfall forecasts. Twenty-five 1-month-lead rainfall forecasts were used to calculate 25 flow predictions for every month. Ensembles with different number of members were compared among them and with the official model currently used for 1-month flow forecasting in the upper Uruguay River. The best accuracy was achieved with the average of the first seven members, which showed a mean relative error of 10.8 % during the floods, while the official model presented 64.0 %, predicting remarkably lower flows. Furthermore, during the period assessed, the correlation between the natural flow and the first-seven-member ensemble was >0.77, while with the official model was 0.34. Thus, coupling SMAP and CFS is a valid approach that can be useful to anticipate mitigating actions to decrease the effects of severe floods in the upper Uruguay River and, probably, in other Brazilian basins.