Climate Forecast System Version Research Articles

Prediction of Intermonth Modes of Winter Air Temperature over China

Abstract The features and causes of the leading intermonth modes of winter surface air temperature anomalies (SATA) over China are investigated, and associated prediction models are developed. The first three intermonth modes of winter SATA over China are obtained by extended empirical orthogonal function (i.e., EEOF1–3) analysis. The results show that EEOF1 represents consistent variations in the whole winter, with a variance contribution of 32.3%, whereas EEOF2 and EEOF3 show spatiotemporally inconsistent changes, and their variance contributions are 16.9% and 12.5%, respectively. EEOF2 has out-of-phase variations between December and January–February, and EEOF3 exhibits a temporal warm–cold alternating pattern, with spatially reversing changes over northwestern and southern China. However, the Climate Forecast System, version 2 (CFSv2) presents a limited prediction skill for winter SATA over China and their intermonth modes. Further investigations indicate that the September sea ice over the Barents–Laptev Seas, the November snow cover over western Europe and East Asia, and the November northern Atlantic sea surface temperature can be, respectively, adopted to develop prediction schemes for the consistent mode (EEOF1 scheme) and two inconsistent modes (EEOF2 and EEOF3 schemes) based on specific mechanisms. These schemes show effective performances in predicting both individual modes and the reconstruction field of SATA over China. The temporal correlation coefficients (TCCs) between cross-validation results and observations are 0.48, 0.51, and 0.31 for the EEOF1–3 modes, respectively (the 90% confidence level is 0.27). For the reconstruction field, the TCCs are 0.40, 0.27, and 0.45 in December, January, and February, respectively, which are much higher than those of the CFSv2 outputs (0.23, −0.16, and −0.09).

Abrupt sub-seasonal rainfall variability over India during summer monsoon 2021: Interaction between midlatitude and tropical circulation

The Indian subcontinent witnessed abnormal subseasonal rainfall variations during the summer monsoon season 2021 with abrupt change (54%) in rainfall from August to September. According to India Meteorological Department, in August Indian landmass received 81% rainfall of its long period average (LPA), which is lowest since 2002. On the other hand, anomalously high rainfall (+ 35% of its LPA) was received in September. The physical mechanisms responsible for such unusual changes in monthly/subseasonal rainfall are unravelled in this study. In August 2021, the southward displacement of Asian jet (SWDAJ) corroborated by westward and northwestward extension of the Western north Pacific (WNP) anticyclone induced moisture divergence over the monsoon trough region and northwestern parts of India are mainly responsible for the deficit rainfall. In the East Asian region, strong low-level westerly wind anomalies in the northern flank of WNP anticyclone couples with the upper-level westerlies associated with SWDAJ and the resulting interaction due to the barotropic–baroclinic coupling maintains WNP circulation. Apart from WNP anticyclone, anomalous Sea Surface Temperature warming over the southeast equatorial Indian Ocean also contributed to the suppressed rainfall over India. In contrast to August, during September the northward displacement of the Asian jet and associated anomalous upper-level anticyclone over northwest India linked to the mid-latitude wave train emanating from the northeast Atlantic contributed to enhanced rainfall over northwest India. Anomalous upper-level high over central Asia/northwest of India increased the moist baroclinic instability over northwest India, and enhanced rainfall there. The Climate Forecast System version 2 (CFSv2) model based on May initial conditions is some extent predicted rainfall change from August and September 2021 over India with much lesser magnitude. Inability of the model in capturing the changes in Asian jet, midlatitude wave train and associated teleconnections/interactions to Indian Summer Monsoon (ISM) rainfall limits its prediction skill. Further, the North American Multi Model Ensemble (NMME) models also could predict the correct sign of ISM rainfall anomaly in August and September, with May initial conditions, but failed to capture the spatial patterns, which needs further investigation.

Improving the subseasonal variability of the Indian summer monsoon in a climate model

AbstractMany global climate models, including the Climate Forecast System version 2 (CFSv2), have a biased representation of subseasonal modes of variability of the Indian summer monsoon. For instance, they simulate a weaker summer mean monsoon low‐pressure systems (LPS) climatology, faster than observed northward propagation of monsoon intraseasonal oscillations (MISOs), and a systematic dry bias over Indian landmass. The Bay of Bengal (BoB), with its shallow mixed layers and unique thermal stratification, significantly modulates the convective activity in this region at subseasonal‐to‐seasonal timescales through modulation of sea surface temperature. The highly stratified upper ocean in the BoB is due to the enormous freshwater it receives from rains and rivers. A river routing model is coupled to the CFSv2 to account for the riverine freshwater and the improvements in modelling the upper‐ocean structure are analysed. Model simulations indicate that inclusion of temporally varying riverine freshwater improves the upper‐ocean state in the BoB and the observed mixed‐layer temperature gradients in the Bay are simulated reasonably after incorporating the time varying river runoff. This resulted in increased LPS lifetime and track density, and enhanced rainfall over central India. Better representation of the upper‐ocean stratification in the model leads to larger post‐convection shoaling of mixed layers at intraseasonal timescales, thereby forming thick barrier layers. Enhanced air–sea interactions restricted to the shallow mixed layer are associated with stronger vorticity, specific humidity and low‐level convergence to the north of the intraseasonal convection band. This enhanced low‐level moisture convergence north of the convection centre results in realistic northward propagation of MISO and aids LPS activity. It is demonstrated that better simulation of the upper‐ocean structure in coupled climate models can improve the representation of subseasonal modes of monsoon variability. These results bear important implications for operational forecasting.

Estimating the noise dose range of the NASA X-59 aircraft in supersonic cruise using PCBoom propagation simulations.

PCBoom propagation simulations of the NASA X-59 low-boom in supersonic cruise were conducted using near-field CFD pressure waveforms as inputs to estimate the noise dose range. Low-booms were propagated through realistic atmospheric profiles from the Climate Forecast System Version 2 across the USA, and loudness statistics of the low-booms are presented. The near-field waveforms correspond to aircraft configurations expected to produce minimum- and maximum-loudness levels on the ground. The simulations show that the practical Perceived Level noise dose range is 72 to 86.7 dB. Dose ranges for other single event and cumulative sonic boom metrics of interest are also provided.

Evaluation of downscaling seasonal climate forecasts for crop yield forecasting in Zimbabwe

Meteorology and weather forecasting are crucial for water-limited agriculture. We evaluate the added value of downscaling seven-months global deterministic seasonal forecasts from the Climate Forecast System version 2 (CFSv2) using the Weather, Research and Forecasting (WRF) model over Zimbabwe for ten growing seasons (2011–2021). Downscaling reduces the area of significant differences between forecasted and observed total seasonal rainfall. Downscaling also improves the score for droughts as measured through the standardized precipitation index and 3-class method. Yield forecasts by the WOrld FOod STudies (WOFOST) model reveal that downscaling improves the estimated growing season evolution and maize yield in all studied regions across the country. For the main maize production region Karoi, the bias, root mean square error, mean absolute error and mean absolute percentage error reduce by 33% (0.2 ton/ha), 27% (0.4 ton/ha), 31% (0.4 ton/ha) and 27% (8.3 %) respectively by downscaling. Hence we illustrate that downscaling the deterministic seasonal forecasts may assist in food security in a crucial area in southern Africa.

Advances in Seasonal Predictions of Arctic Sea Ice With NOAA UFS

AbstractThe Unified Forecast System (UFS) is the next generation modeling infrastructure under development for NOAA's operational numerical weather/climate predictions. This study is the first attempt with UFS for seasonal predictions application. In particular, 9‐month hindcasts are performed starting from every month during 2007–2020. The UFS performance in predicting Arctic sea ice was compared to hindcasts by (a) the current operational Climate Forecast System version 2 (CFSv2) and (b) an experimental sea ice prediction system (CFSm5) at NOAA. Evaluations indicate that UFS demonstrates consistently improved skills than both CFSv2 and CFSm5 in seasonal sea ice predictions, together with more realistic climatological sea ice distributions. Diagnostics suggest that the better climatological sea ice distributions in UFS is related to its atmospheric states simulated with FV3, the atmospheric component of UFS, and reinforced by associated ocean circulations. In addition, applications of the multi‐model ensemble strategy do not present skill improvements over the UFS forecasts alone.

Study of the Optimal Grid Resolution and Effect of Wave–Wave Interaction during Simulation of Extreme Waves Induced by Three Ensuing Typhoons

Three typhoons, Meranti, Malakas, and Megi, occurred successively in eastern Taiwanese waters in September 2016, causing extreme waves (significant wave height > 10.0 m), and these events were selected to investigate the effect of model grid resolution and wave–wave interaction on simulating typhoon-driven waves. The WAVEWATCH III (WW3) model, with 0.50 deg, 0.25 deg, 0.20 deg, 0.10 deg, and 0.05 deg grid resolutions, and two reanalysis wind fields were adopted to simulate ocean waves during these three typhoons. The results indicated that the exertion of the Climate Forecast System version 2 (CFSv2) winds over the WW3 model with 0.10 deg grid resolution yielded optimum simulations of typhoon waves in a compromise between accuracy and elapsed time. In the present study, the WW3 model modeled nonlinear wave–wave interactions using discrete interaction approximation (DIA). The numerical experiments revealed that the underestimations of typhoon waves were significant when the WW3 model excluded nonlinear wave–wave interactions, especially when employing a higher grid resolution. This study also found that the WW3 model is superior to the Wind Wave Model III (WWM-III) using the CFSv2 winds because the WWM-III tended to overestimate the extreme waves in all three of these eastern Taiwan typhoon events that occurred in September 2016.

Turbulence effects on shaped booms: Central composite design of modeled atmospheric turbulence parameters for sonic boom propagation

Propagation of sonic booms through turbulence reduces mean sonic boom perception metric levels and also causes considerable variability. NASA's PCBoom suite of sonic boom acoustic propagation modules includes an approximate method for accounting for the effects of turbulence on traditional N-wave sonic booms. The current implementation is ineffective for shaped sonic booms or low-booms, and it also has limited values for turbulence and ambient input parameters. NASA's future X-59 low-boom community noise surveys require an accurate estimate of the effects of turbulence in regions across the USA, so the module must be improved. This work presents the methods of selecting which ambient and turbulence parameters should be included in an improved PCBoom turbulence module. Turbulence and ambient data were collected from two atmospheric model databases, the Climate Forecast System Version 2 and European Centre for Medium-Range Weather Forecast Reanalysis Version 5 (ERA5), hourly from 7 AM to 7 PM local time for 10 years at 19 locations across the USA. A fully-factorial propagation analysis using these parameters would be exceedingly computationally expensive. Instead, a central composite design was chosen resulting in 45 combinations of ambient and turbulence parameters. These 45 cases effectively sample the space balancing computational burden.

Downscaled subseasonal fire danger forecast skill across the contiguous United States

AbstractThe increasing complexity and impacts of fire seasons in the United States have prompted efforts to improve early warning systems for wildland fire management. Outlooks of potential fire activity at lead‐times of several weeks can help in wildland fire resource allocation as well as complement short‐term meteorological forecasts for ongoing fire events. Here, we describe an experimental system for developing downscaled ensemble‐based subseasonal forecasts for the contiguous US using NCEP's operational Climate Forecast System version 2 model. These forecasts are used to calculate forecasted fire danger indices from the United States (US) National Fire Danger Rating System in addition to forecasts of evaporative demand. We further illustrate the skill of subseasonal forecasts on weekly timescales using hindcasts from 2011 to 2021. Results show that while forecast skill degrades with time, statistically significant week 3 correlative skill was found for 76% and 30% of the contiguous US for Energy Release Component and evaporative demand, respectively. These results highlight the potential value of experimental subseasonal forecasts in complementing existing information streams in weekly‐to‐monthly fire business decision making for suppression‐based decisions and geographic reallocation of resources during the fire season, as well for proactive fire management actions outside of the core fire season.

U-Net: A deep-learning method for improving summer precipitation forecasts in China

A deep-learning method named U-Net was applied to improve the skill in forecasting summer (June–August) precipitation for at a one-month lead during the period 1981–2020 in China. The variables of geopotential height, soil moisture, sea level pressure, sea surface temperature, ocean salinity, and snow were considered as the model input to revise the seasonal prediction of the Climate Forecast System, version 2 (CFSv2). Results showed that on average U-Net reduced the root-mean-square error of the original CFSv2 prediction by 49.7% and 42.7% for the validation and testing set, respectively. The most improved areas were Northwest, Southwest, and Southeast China. The anomaly same sign percentages and temporal and spatial correlation coefficients did not present significant improvement but maintained the comparable performances of CFSv2. Sensitivity experiments showed that soil moisture is the most crucial factor in predicting summer rainfall in China, followed by geopotential height. Due to its advantages in handling small training dataset sizes, U-Net is a promising deep-learning method for seasonal rainfall prediction.摘要本研究应用了名为U-Net的深度学习方法来提高中国夏季 (6–8月) 降水的预报技能, 预报时段为1981–2020年, 预报提前期为一个月. 将位势高度场, 土壤湿度, 海平面气压, 海表面温度, 海洋盐度和青藏高原积雪等变量作为模型输入, 本文对美国NCAR气候预报系统第2版 (CFSv2) 的季节性预报结果进行了修正. 结果显示, 在验证集和测试集上, U-Net平均将原CFSv2预测的均方根误差分别减少了49.7%和42.7%. 预报结果改善最大的地区是中国的西北,西南和东南地区. 然而, 同号率和时空相关系数没有得到明显改善, 但仍与CFSv2的预测技巧持平. 敏感性实验表明, 土壤湿度是预测中国夏季降雨的最关键因素, 其次是位势高度场. 本研究显示了U-Net模型在训练小样本数据集方面的优势, 为我国汛期季节性降雨预测提供了一种有效的深度学习方法.

Two deep learning-based bias-correction pathways improve summer precipitation prediction over China

As most global climate models (GCM) suffer from large biases in simulating/predicting summer precipitation over China, it is of great importance to develop suitable bias-correction methods. This study proposes two pathways of bias-correction with deep learning (DL) models incorporated. One is the deterministic pathway (DP), in which the bias correction is directly applied to the precipitation forecasts. The other one, namely the probability pathway (PP), corrects the forecasted precipitation anomalies using a conditional probability method before being added to the observational climatology. These two pathways have been applied to correct the precipitation forecasts based on a GCM prediction system Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0). The applications of DL models in the both pathways yield higher resolution of corrected predictions than the uncorrected ones. Both pathways improve summer precipitation predictions at 4-month lead. Moreover, the DP correction shows a better performance in predicting extreme precipitation, while the PP is proficient in correcting the spatial pattern of precipitation anomalies over China. The present results highlight the importance of the application of appropriate correction strategy for different prediction purposes.

Offshore reanalysis wind speed assessment across the wind turbine rotor layer off the United States Pacific coast

Abstract. The California Pacific coast is characterized by considerable wind resource and areas of dense population, propelling interest in offshore wind energy as the United States moves toward a sustainable and decarbonized energy future. Reanalysis models continue to serve the wind energy community in a multitude of ways, and the need for validation in locations where observations have been historically limited, such as offshore environments, is strong. The U.S. Department of Energy (DOE) owns two lidar buoys that collect wind speed observations across the wind turbine rotor layer along with meteorological and oceanographic data near the surface to characterize the wind resource. Lidar buoy data collected from recent deployments off the northern California coast near Humboldt County and the central California coast near Morro Bay allow for validation of commonly used reanalysis products. In this article, wind speeds from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP) are validated at heights within the wind turbine rotor layer ranging from 50 to 100 m. The validation results offer guidance on the performance and uncertainty associated with utilizing reanalyses for offshore wind resource characterization, providing the offshore wind energy community with information on the conditions that lead to reanalysis error. At both California coast locations, the reanalyses tend to underestimate the observed rotor-level wind resource. Occasions of large reanalysis error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production (> 10 m s−1), and mischaracterization of the diurnal wind speed cycle in summer months.

Developing and evaluating week 2 and weeks 3-4 outlook tools for extratropical storminess

Extratropical cyclones give rise to most of the high impact weather in the mid-to high-latitudes during the cool seasons, including heavy precipitation and strong winds. Thus it is important for stakeholders to be informed of approaching periods of increased or decreased cyclone activity. While individual cyclone tracks can be predicted out to about a week or so, from week 2 on, statistics summarizing cyclone activity, or storminess, are more useful. Storminess can be defined based on Lagrangian cyclone tracking or by Eulerian variance statistics. The outlook includes a combination of both methods. Lagrangian cyclone tracks provide information about where cyclones pass through and are more intuitive to users, while Eulerian variance statistics have been shown to be highly correlated with cyclone-related weather and are expected to be more predictable given that they are not as noisy. In this paper, we evaluate a storminess outlook tool developed based on dynamical model forecasts in the week-2 and weeks 3-4 time ranges. The outlook uses two 6-hourly subseasonal ensemble forecasts–the Global Ensemble Forecast System version 12 (GEFSv12), and the coupled Climate Forecast System version 2 (CFSv2). Hindcasts and operational forecasts from 1999–2016 are used to assess the prediction skill. Our results show that the GEFSv12 and CFSv2 combined ensemble has higher skill than either individual ensemble. The combined ensemble shows some skill in predicting cyclone amplitude and frequency out to weeks 3-4, with highest skill in winter, and lowest skill in summer. Models also show some skill in predicting the statistics of deep cyclones for week 2. The prediction skills for an Eulerian sea level pressure variance storminess metric is significantly higher than those for Lagrangian track statistics. Our results also show that GEFSv12 performs better than its predecessor GEFSv11. Correlations between the storminess indices and surface weather, including precipitation and high winds, are examined. A publicly accessible web page has been developed to display the subseasonal predictions in real time. The web page also contains information on climatology and forecast verification to enable users to make more informed use of the outlook.

Updated noise dose range of NASA’s X-59 aircraft estimated from propagation simulations

A follow-on study to Doebler and Loubeau [“The noise dose range of the X-59 estimated from propagation simulations,” JASA 150(4), A208 (2021)] is described. In the previous work, propagation simulations of NASA’s X-59 sonic thump were conducted using two near-field pressure solutions from NASA’s Cart3D computational fluid dynamics (CFD) code. The near-fields were propagated through realistic atmospheric profiles from the Climate Forecast System Version 2 database across the USA, and perceived level statistics of the thumps were presented. The current work repeats the previous analyses, instead using near-field CFD solutions from NASA’s Fully Unstructured Navier-Stokes (FUN3D) code for the same aircraft conditions (Mach 1.4 at 53 200 feet and Mach 1.3 at 43 000 feet), which achieve the minimum and maximum cruise loudness. NASA and contractors in the X-59 development and community noise testing project mutually agreed upon using FUN3D for generating near-field pressures. Sonic thump loudness statistics using the FUN3D near-fields are similar to those of Cart3D. The min loudness condition is about 1 dB (PL) greater when using the FUN3D solution, and the max loudness condition results are nearly identical. Understanding the X-59’s dose range for various flight and atmospheric conditions is important for planning X-59 community noise surveys.

Deep Learning Provides Substantial Improvements to County‐Level Fire Weather Forecasting Over the Western United States

AbstractThe recent wildfires in the western United States during 2018 and 2020 caused record‐breaking fire damage and casualties. Despite remarkable advances in fire modeling and weather forecasting, it remains challenging to anticipate catastrophic wildfire events and associated damage. One key missing component is a fire weather prediction system with sufficiently long lead time capable of providing useful regional details. Here, we develop a hybrid prediction model of wildfire danger called CFS with super resolution (CFS‐SR) as a proof of concept to fill that void. The CFS‐SR model is constructed by integrating the Climate Forecast System version 2 with a deep learning (DL) technique from Single Image Super Resolution, a method widely used in enhancing image resolution. We show that for the 2018–2019 fire season, the CFS‐SR model significantly improves accuracy in forecasting fire weather at lead times of up to 7 days with an enhanced spatial resolution up to 4 km. This level of high resolution provides county‐level fire weather forecast, making it more practical for allocating resources to mitigate wildfire danger. Our study demonstrates that a proper combination of ensemble climate predictions with DL techniques can boost predictability at finer spatial scales, increasing the utility of fire weather forecasts for practical applications.

Simulation and Regression Modeling of NASA’s X-59 Low-Boom Carpets Across America

NASA’s X-59 aircraft is predicted to produce a significantly quieter cruise sonic boom than traditional -wave-producing aircraft. A propagation simulation study was undertaken to quantify loudness levels, exposure size, and variability of the X-59’s low-boom carpet using realistic atmospheric profiles across the contiguous United States of America (CONUS). Near-field pressure data of the X-59 in supersonic cruise from NASA’s fully unstructured Navier–Stokes three-dimensional (known as FUN3D) computational fluid dynamics code were propagated using NASA’s PCBoom code, which solves an enhanced Burgers equation along acoustic rays. Atmospheric profiles from the National Oceanic and Atmospheric Administration’s Climate Forecast System Version 2 database were used for propagation at 138 locations across the CONUS. Carpets at each location were generated for aircraft headings in the four cardinal directions. Over one million X-59 carpets were generated in total. The effects of the heading, season, geography, and climate zone on boom levels and exposure size are presented. Multiple linear regression models were developed to estimate carpet width and loudness metrics across the CONUS. These results inform regulators and mission planners on expected variations in boom levels and carpet extent from atmospheric variations. Understanding potential carpet variability is important when planning community noise surveys using the X-59.

Physical–empirical prediction model for the dominant mode of extreme high temperature events in eastern China during summer

The dominant mode of extreme high temperature events in eastern China during summer shows a dipole pattern with opposite anomalies over southern and northeastern China, which explains 25% of the variance. We document the limited prediction skill of the National Centers for Environmental Prediction Climate Forecast System, version 2 (CFSv2) for the dominant mode of these events. Based on the mechanisms related to the dominant mode, a physical–empirical model was established that improved the prediction of extreme high temperature events in eastern China, which will help in disaster prediction and mitigation. The physical connection between the dominant mode and the summer sea surface temperature (SST) over the western tropical and subtropical Pacific Ocean was predicted well by the CFSv2 hindcasts, and thus the areal mean CFSv2-hindcast SSTs over the western tropical and subtropical Pacific Ocean served as a predictor in the physical–empirical model. In the observations, the soil moisture over southern China in the preceding December (soil_Dec) combined the signal of the air–sea interactions over the tropical and northern extratropical Pacific Ocean, which induced anomalous SSTs in the Indian and western tropical Pacific oceans in the following summer and contributed to the dominant mode. Soil_Dec was therefore closely correlated with the dominant mode and was used as a predictor. The results of the one-year-leave cross-validation and independent hindcast showed that the time series of the dominant mode was better predicted by the physical–empirical model than by the CFSv2 hindcasts, with an improved correlation coefficient from insignificant to about 0.8, a root-mean-square error reduced by about 50% and an increased rate of same signs. The physical–empirical model showed advantages in the prediction of the dominant mode of summer extreme high temperature events over eastern China, which may be used in the prediction of other climate variables.

A Two‐Stage Framework for Bias and Reliability Tests of Ensemble Hydroclimatic Forecasts

AbstractThe popular probability integral transform (PIT) uniform plot presents informative empirical illustrations of five types of ensemble forecasts, that is, reliable, under‐confident, over‐confident, negatively biased and positively biased. This paper has built a novel two‐stage framework upon the PIT uniform plot to quantitatively examine the forecast attributes of bias and reliability. The first stage utilizes the test statistic on bias to examine whether the mean of PIT values is equal to the theoretical mean of standard uniform distribution. Then, the second stage uses the test statistic on reliability to examine whether the mean squared deviation from the theoretical mean is equal to the theoretical variance of standard uniform distribution. Therefore, by using the two‐tailed bootstrap hypothesis testing, the first stage identifies unbiased ensemble forecasts, negatively biased forecasts and positively biased forecasts; the second stage focuses on unbiased ensemble forecasts to furthermore identify reliable forecasts, under‐confident forecasts and over‐confident forecasts. Numerical experiments are devised for the National Centers for Environmental Prediction's Climate Forecast System version 2 ensemble forecasts of global precipitation. The results highlight the existence of various shapes of the PIT uniform plots. Due to extreme values of observed precipitation, the PIT uniform plots in some cases can substantially deviate from the 1:1 line even though the mean and variance of ensemble forecasts are respectively in accordance with the mean and variance of observations. Nevertheless, the two‐stage framework along with the two test statistics serves as a robust tool for the verification of ensemble hydroclimatic forecasts.

Quantifying overlapping and differing information of global precipitation for GCM forecasts and El Niño–Southern Oscillation

Abstract. While El Niño–Southern Oscillation (ENSO) teleconnection has long been used in statistical precipitation forecasting, global climate models (GCMs) provide increasingly available dynamical precipitation forecasts for hydrological modeling and water resources management. It is not yet known to what extent dynamical GCM forecasts provide new information compared to statistical teleconnection. This paper develops a novel set operations of coefficients of determination (SOCD) method to explicitly quantify the overlapping and differing information for GCM forecasts and ENSO teleconnection. Specifically, the intersection operation of the coefficient of determination derives the overlapping information for GCM forecasts and the Niño3.4 index, and then the difference operation determines the differing information in GCM forecasts (Niño3.4 index) from the Niño3.4 index (GCM forecasts). A case study is devised for the Climate Forecast System version 2 (CFSv2) seasonal forecasts of global precipitation in December–January–February. The results show that the overlapping information for GCM forecasts and the Niño3.4 index is significant for 34.94 % of the global land grid cells, that the differing information in GCM forecasts from the Niño3.4 index is significant for 31.18 % of the grid cells and that the differing information in the Niño3.4 index from GCM forecasts is significant for 11.37 % of the grid cells. These results confirm the effectiveness of GCMs in capturing the ENSO-related variability of global precipitation and illustrate where there is room for improvement of GCM forecasts. Furthermore, the bootstrapping significance tests of the three types of information facilitate in total eight patterns to disentangle the close but divergent associations of GCM forecast correlation skill with ENSO teleconnection.

Investigation of Ocean Sub-Surface Processes in Tropical Cyclone Phailin Using a Coupled Modeling Framework: Sensitivity to Ocean Conditions

The present study is aimed to investigate sub-surface ocean processes and their contribution to the intensification of a tropical cyclone (TC) from a coupled-modeling perspective. The Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model was employed to simulate TC Phailin, which originated over the Bay of Bengal and made landfall on the eastern coast of India in October 2013. Three sub-surface ocean condition datasets—viz., (a) the European Centre for Medium-Range Weather Forecast (ECMWF) Ocean Reanalysis, (b) the Climate Forecast System Version 2 (CFSV2) Operational Analysis, and (c) the Hybrid Coordinate Ocean Model (HYCOM) Reanalysis datasets—were used for the initial and boundary conditions for the oceanic component of the coupled model in three different simulations of TC Phailin. All the simulations showed a delay in intensification compared to the observation, and ECMWF simulated the most intensified TC. CFSV2 simulated a deeper mixed layer (ML) and higher mixing, which hindered the intensification. Furthermore, higher entrainment of cold water in the ML led to cold water reaching the surface and, consequently, decreased sea surface temperature, which acted as negative feedback in the intensification of the storm in the cases of CFSV2 and HYCOM. ECMWF realistically simulated the interactions of the TC with a cold-core eddy before landfall. A sudden increase in ML heat content, the addition of heat in the ML due to entrainment, and the prevention of cold water reaching the surface were indicative of the breaking of the barrier layer (BL) in ECMWF, which was further corroborated by the spatial distribution of BL thickness in the simulation. This acted as positive feedback in the intensification of the TC. The findings of this study strongly suggest that not only the incorporation of physical oceanic sub-surface processes in the modeling of TCs but also the proper representation of prevailing mesoscale features and ocean sub-surface temperature, salinity, and current profiles in datasets is essential for realistic simulations of TCs.