Climate Forecast System Research Articles

Deep learning-based bias correction of ISMR simulated by GCM

Simulated data of atmospheric variables like precipitation is very important for climate science research, especially for understanding future scenarios. Such data is generated by global or regional climate models (GCM/RCM), for both past and future periods under different initial conditions. Unfortunately, these models frequently display biases in simulated precipitation data compared to ground observations, due to their inability to incorporate the entire physics of the process accurately. It is imperative to mitigate such biases using contemporary techniques to make the simulated data a valuable end product. Our aim in this research is to correct seasonal (from June to September) Indian Summer Monsoon Rainfall (ISMR) data as simulated by the Climate Forecast System (CFS) over the Indian subcontinent, with the Global Precipitation Climatology Project (GPCP) data serving as the ground truth reference. This period is important as more than one-third of India's annual rainfall occurs during these months. This study presents a novel Deep Learning (DL) based architecture known as the Convolutional Neural Network for Bias Correction (CNNBC) that aims to calibrate the model-simulated data with past observations, to address these biases while preserving the statistical properties and spatial correlations relationships among grid points and enhancing the spatial mean of precipitation estimates. To evaluate the effectiveness of our proposed method, we compare its performance to that of three other statistical and DL techniques: quantile mapping (QM), quantile delta mapping (QDM), and the SRDRN model. The comparative analysis demonstrates that the CNNBC model outperforms the other in terms of our task.

Assessing Multi-Scale Atmospheric Circulation Patterns for Improvements in Sub-Seasonal Precipitation Predictability in the Northern Great Plains

This study leverages the relationships between the Great Plains low-level jet (GP-LLJ) and the circumglobal teleconnection (CGT) to assess the enhancement of 30-day rainfall forecast in the Northern Great Plains (NGP). The assessment of 30-day simulated precipitation using the Climate Forecast System (CFS) is contrasted with the North American Regional Reanalysis, searching for sources of precipitation predictability associated with extended wet and drought events. We analyze the 30-day sources of precipitation predictability using (1) the characterization of dominant statistical modes of variability of 900 mb winds associated with the GP-LLJ, (2) the large-scale atmospheric patterns based on 200 mb geopotential height (HGT), and (3) the use of GP-LLJ and CGT conditional probability distributions using a continuous correlation threshold approach to identify when and where the forecast of NGP precipitation occurs. Two factors contributing to the predictability of precipitation in the NGP are documented. We found that the association between GP-LLJ and CGT occurs at two different scales—the interdiurnal and the sub-seasonal, respectively. The CFS reforecast suggests that the ability to forecast sub-seasonal precipitation improves in response to the enhanced simulation of the GP-LLJ and CGT. Using these modes of climate variability could improve predictive frameworks for water resources management, governance, and water supply for agriculture.

Assessment of Subseasonal-to-Seasonal (S2S) Precipitation Forecast Skill for Reservoir Operation in the Yaque Del Norte River, Dominican Republic

Operational forecasters desire information about how their reservoir and riverine systems will evolve over monthly to seasonal timescales. Seasonal traces of hydrometeorological variables at a daily or sub-daily resolution are needed to drive hydrological models at this timescale. Operationally available models such as the Climate Forecast System (CFS) provide seasonal precipitation forecasts, but their coarse spatial scale requires further processing for use in local or regional hydrologic models. We focus on three methods to generate such forecasts: (1) a bias-adjustment method, in which the CFS forecasts are bias-corrected by ground-based observations; (2) a weather generator (WG) method, in which historical precipitation data, conditioned on an index of the El Niño–Southern Oscillation, are used to generate synthetic daily precipitation time series; and (3) a historical analog method, in which the CFS forecasts are used to condition the selection of historical satellite-based mean areal precipitation (MAP) traces. The Yaque del Norte River basin in the Dominican Republic is presented herein as a case study, using an independent dataset of rainfall and reservoir inflows to assess the relative performance of the methods. The methods showed seasonal variations in skill, with the MAP historical analog method having the strongest overall performance, but the CFS and WG methods also exhibited strong performance during certain seasons. These results indicate that the strengths of each method may be combined to produce an ensemble forecast product.

Climatology of the terms and variables of transformed Eulerian-mean (TEM) equations from multiple reanalyses: MERRA-2, JRA-55, ERA-Interim, and CFSR

Abstract. A 30-year (1980–2010) climatology of the major variables and terms of the transformed Eulerian-mean (TEM) momentum and thermodynamic equations is constructed by using four global atmospheric reanalyses: the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2); the Japanese 55-year Reanalysis (JRA-55); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim); and the Climate Forecast System Reanalysis (CFSR). Both the reanalysis ensemble mean (REM) and the differences in each reanalysis from the REM are investigated in the latitude–pressure domain for December–January–February and for June–July–August. For the REM investigation, two residual vertical velocities (the original one and one evaluated from residual meridional velocity) and two mass streamfunctions (from meridional and vertical velocities) are compared. Longwave (LW) radiative heating and shortwave (SW) radiative heating are also shown and discussed. For the TEM equations, the residual terms are also calculated and investigated for their potential usefulness, as the residual term for the momentum equation should include the effects of parameterized processes such as gravity waves, while that for the thermodynamic equation should indicate the analysis increment. Inter-reanalysis differences are investigated for the mass streamfunction, LW and SW heating, the two major terms of the TEM momentum equation (the Coriolis term and the Eliassen–Palm flux divergence term), and the two major terms of the TEM thermodynamic equation (the vertical temperature advection term and the total diabatic heating term). The spread among reanalysis TEM momentum balance terms is around 10 % in Northern Hemisphere winter and up to 50 % in Southern Hemisphere winter. The largest uncertainties in the thermodynamic equation (about 50 %) are found in the vertical advection, for which the structure is inconsistent with the differences in heating. The results shown in this paper provide basic information on the degree of agreement among recent reanalyses in the stratosphere and upper troposphere in the TEM framework.

Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization

Abstract. The Los Alamos Community Ice CodE (CICE) sea ice model is being tested in standalone mode to identify biases that limit its suitability for seasonal prediction, where it is driven by atmospheric forcings from the NCEP Climate Forecast System Reanalysis (CFSR) and a built-in mixed-layer ocean model in CICE. The initial conditions for the sea ice and mixed-layer ocean are also from CFSR in the control experiments. The simulated sea ice extent agrees well with observations during the warm season at all lead times up to 12 months, in both the Arctic and the Antarctic. This suggests that CICE is able to provide useful sea ice edge information for seasonal prediction. However, the model's initial conditions have ice that is too thick in the Beaufort Sea, resulting in excessive ice extent in the Arctic at 6-month lead forecasts and errors in ice volume at all lead times when compared to available observations. To address this limitation, additional CS2_IC experiments were conducted, where the Arctic ice thickness was initialized using CryoSat-2 satellite observations while keeping all other initial fields the same as in the control experiments. This reduced the positive bias in the ice thickness in the initial conditions, leading to improvements in both the simulated ice edge and the ice thickness at the seasonal timescale. This indicates that CICE has the potential to improve its seasonal forecast skill and provide more accurate predictions of sea ice extent and thickness when initialized with a more realistic sea ice thickness. This study highlights that the suitability of CICE for seasonal prediction depends on various factors, including initial conditions such as sea ice thickness, in addition to sea ice coverage, as well as oceanic and atmospheric conditions.

КЛИМАТИЧЕСКИЕ ПРОГНОЗЫ. ЧАСТЬ I. СОВРЕМЕННОЕ СОСТОЯНИЕ И ПЕРСПЕКТИВЫ РАЗВИТИЯ

The physical background and main types of climate forecasts issued by the world meteorological centers are considered. It is emphasized that modern forecasting systems have an integrated nature and combine not only data assimilation systems and global numerical atmosphere-ocean-land models, but also support infrastructure for providing forecast products to users. The features of the forecast system of the Hydrometcenter of Russia/North Eurasia Climate Centre (NEACC) and other world meteorological centers are compared. It is noted that, unlike other centers, the forecast system of the Hydrometeorological Center of Russia/NEACC is based on the integrated use of synoptic, statistical, and hydrodynamic methods. It has been revealed that new trends and directions in the development of the forecast system are associated with the emergence of a new version of the SL-AV global semi-Lagrangian finite-difference atmosphere model of the Hydrometcenter of Russia and the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (INM RAS) with an expanded (up to 61 members) forecast ensemble, as well as with using the INR RAS climate model (INM-CM5) and additional applications designed for interaction with various economic sectors. The findings may be useful in determining a vector of future research aimed at developing the Russian climate prediction system.

Efficiency of monsoon mission climate forecast system (CFSv2-T382) to predict the large-scale and regional short-term drought over India during boreal summer and its possible controlling factors

Efficiency of monsoon mission climate forecast system (CFSv2-T382) to predict the large-scale and regional short-term drought over India during boreal summer and its possible controlling factors

Iterative Ensemble Smoothers for Data Assimilation in Coupled Nonlinear Multiscale Models

Abstract This paper identifies and explains particular differences and properties of adjoint-free iterative ensemble methods initially developed for parameter estimation in petroleum models. The aim is to demonstrate the methods’ potential for sequential data assimilation in coupled and multiscale unstable dynamical systems. For this study, we have introduced a new nonlinear and coupled multiscale model based on two Kuramoto–Sivashinsky equations operating on different scales where a coupling term relaxes the two model variables toward each other. This model provides a convenient testbed for studying data assimilation in highly nonlinear and coupled multiscale systems. We show that the model coupling leads to cross covariance between the two models’ variables, allowing for a combined update of both models. The measurements of one model’s variable will also influence the other and contribute to a more consistent estimate. Second, the new model allows us to examine the properties of iterative ensemble smoothers and assimilation updates over finite-length assimilation windows. We discuss the impact of varying the assimilation windows’ length relative to the model’s predictability time scale. Furthermore, we show that iterative ensemble smoothers significantly improve the solution’s accuracy compared to the standard ensemble Kalman filter update. Results and discussion provide an enhanced understanding of the ensemble methods’ potential implementation and use in operational weather- and climate-prediction systems.

Impact of Satellite Wind on Improving Simulation of the Upper Ocean Response to Tropical Cyclones

Accurate modeling of the ocean response to tropical cyclones (TCs) requires high-quality wind fields to force ocean models. In this study, blended wind fields are generated using multi-source satellite data and the Climate Forecast System Reanalysis (CFSR) wind data. We utilize the hybrid wind fields to drive the Regional Ocean Modeling System (ROMS) for simulating oceanic dynamic and thermodynamic parameters. The model’s simulated ocean surface and sub-surface temperatures, as well as current speeds, are generally consistent with satellite and in situ observations collected during TC Winston and Freddy. The results are significantly better than those simulated by ROMS using wind forcing from CFSR alone. These results suggest that incorporating satellite wind data into the atmospheric forcing has the potential to enhance vertical mixing and improve simulations of the upper ocean response to TCs.

Spatial and Temporal Variation of Subseasonal-to-Seasonal (S2S) Precipitation Reforecast Skill across the CONUS

Abstract Precipitation forecasts, particularly at subseasonal-to-seasonal (S2S) time scale, are essential for informed and proactive water resource management. Although S2S precipitation forecasts have been evaluated, no systematic decomposition of the skill, Nash–Sutcliffe efficiency (NSE) coefficient, has been analyzed toward understanding the forecast accuracy. We decompose the NSE of S2S precipitation forecast into its three components—correlation, conditional bias, and unconditional bias—by four seasons, three lead times (1–12, 1–22, and 1–32 days), and three models, European Centre of Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction’s (NCEP) Climate Forecast System (CFS) model, and Environment and Climate Change Canada (ECCC), over the conterminous United States (CONUS). Application of a dry threshold, removal of grid cells with seasonal climatological precipitation means below 0.01 in. per day, is important as the NSE and correlations are lower across all seasons after masking areas with low precipitation values. Further, a west-to-east gradient in S2S forecast skill exists, and forecast skill was better during the winter months and for areas closer to the coast. Overall, ECMWF’s model performance was stronger than both ECCC and NCEP CFS’s performance, mainly for the forecasts issued during the fall and winter months. However, ECCC and NCEP CFS performed better for the forecast issued during the spring months and for areas further from the coast. Postprocessing using simple model output statistics could reduce both unconditional and conditional biases to zero, thereby offering better skill for regimes with high correlation. Our decomposition results show that efforts should focus on improving model parameterization and initialization schemes for climate regimes with low correlation.

Development of Indian summer monsoon precipitation biases in two seasonal forecasting systems and their response to large-scale drivers

Abstract. The Met Office Global Coupled Model (GC) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) are both widely used for predicting and simulating the Indian summer monsoon (ISM), and previous studies have demonstrated similarities in the biases in both systems at a range of timescales from weather forecasting to climate simulation. In this study, ISM biases are studied in seasonal forecasting setups of the two systems in order to provide insight into how they develop across timescales. Similarities are found in the development of the biases between the two systems, with an initial reduction in precipitation followed by a recovery associated with an increasingly cyclonic wind field to the north-east of India. However, this occurs on longer timescales in CFSv2, with a much stronger recovery followed by a second reduction associated with sea surface temperature (SST) biases so that the bias at longer lead times is of a similar magnitude to that in GC. In GC, the precipitation bias is almost fully developed within a lead time of just 8 d, suggesting that carrying out simulations with short time integrations may be sufficient for obtaining substantial insight into the biases in much longer simulations. The relationship between the precipitation and SST biases in GC seems to be more complex than in CFSv2 and differs between the early part of the monsoon season and the later part of the monsoon season. The relationship of the bias with large-scale drivers is also investigated, using the boreal summer intraseasonal oscillation (BSISO) index as a measure of whether the large-scale dynamics favour increasing, active, decreasing or break monsoon conditions. Both models simulate decreasing conditions the best and increasing conditions the worst, in agreement with previous studies and extending these previous results to include CFSv2 and multiple BSISO cycles.

Risk transference between climate variability and financial derivatives: Implications for global food security

We investigate the impacts of the El Niño-Southern Oscillation (ENSO) on global food security by using information embedded in financial derivatives. Using a state-of-the-art general circulation model (GCM) climate forecasting system that generates observations on the phase and magnitude of ENSO, we create novel indices that track its uncertainty throughout time. Together with the option implied volatilities of core food inputs (wheat, maize, rice, and soybean), we model the time-varying volatility spillover from ENSO to each commodity, capturing the direction and intensity of risk transference. Simulating Gaussian and non-Gaussian impulse responses that mimic an increase in climate variability show a persistent impact on the price uncertainty of the commodities lasting up to three months. We also show that shocks are contingent on the phase of ENSO, with warmer conditions in the Eastern Pacific (i.e., the El Niño phase) increasing risk transference for soybean and rice. In contrast, the La Niña phase has a material influence on the uncertainty of wheat and maize. In all, we reveal a volatility transmission channel of climate variability that affects financial markets, suggesting valuable inferences about the impact of climate shocks can be derived from the rich information embedded in commodity-linked derivatives.

Seasonal climate forecast-an important tool in managing the risk of extreme weather events in Australia's wheat industry

This study aims to investigate the benefits of using the Australian Community Climate and Earth-System Simulator-Seasonal Version 2 (ACCESS-S2), a state-of-the-art seasonal climate forecast (SCF) system, to improve contingent decision-making in the Australian wheat industry. Six locations in eastern Australia, three cultivars with varying maturities, and 17 times of sowing (TOS) were considered. Seasonal hindcasts from ACCESS-S2, initialized on the 1st May for the period 1981–2018 were linked with the Agricultural Production System sIMulator (APSIM)-Wheat model (v7.10) to (1) assess the hindcast skill of ACCESS-S2 in predicting the occurrence of extreme weather events and in predicting above/below long-term median wheat yield; (2) identify optimal management strategies in mitigating the impact of extreme weather events; and (3) quantify the yield benefit of using ACCESS-S2 at the key decision-making point of sowing against a baseline management situation (i.e., without the use of SCF). Research results showed that (1) ACCESS-S2 has varying skills in predicting extreme climate indices and wheat yield across the six locations at a seasonal lead time; (2) 68 % of cases had a yield gain using SCF information; (3) across all cases, there was an average yield gain of 281 kg/ha representing an increase of 13 %; (4) benefits of using SCF were seen across predicted wet years, neutral years and dry years accounting for 69 %, 65 % and 72 % respectively, and (5) the identified optimal management options varied from location to location with earlier sowing associated with either suntop or gregory or both being the optimal strategies at most of the locations considered. Overall, there is a demonstrated benefit in utilizing ACCESS-S2 forecasts in the Australian wheat industry to improve farm management decision-making. The benefit can occur in any climate state but with dry years being more likely and significant (with an average yield increase of 97 % across the locations).

Midsummer precipitation prediction over eastern China by the dynamic downscaling method

AbstractThis study assesses the midsummer precipitation prediction over eastern China by the dynamic downscaling method. Based on the Climate Forecast System version 2 of the National Centers for Environmental Prediction and the Weather Research and Forecasting Model, the prediction performance of global and regional models on the July precipitation over eastern China is further analyzed by hindcast experiments from 1982 to 2010 and prediction experiments from 2011 to 2021. The results suggest that the regional model forced by the global model can noticeably improve the prediction skill for precipitation in eastern China, especially in the region from the South of North China to the Yangtze River Basin, referred as the northern China in this paper. In addition, we perform a diagnostic analysis of the reason for the improvement of the model prediction skill. The results indicate that the high resolution of the regional model and the refinement of physical process parameterizations contribute to improving the simulation ability for the East Asian atmospheric circulation pattern, heat flux, especially for the meridional teleconnection pattern in East Asia and the sensible heat flux in the northern China, thus further improving precipitation prediction.

Synoptic Analysis and Subseasonal Predictability of an Early Heatwave in the Eastern Mediterranean

Greece and the surrounding areas experienced an early warm spell with characteristics of a typical summer Mediterranean heatwave in mid-May 2020. The maximum 2 m temperature at Kalamata (southern Greece) reached 40 °C on 16 May and at Aydin (Turkey), it was 42.6 °C on 17 May. There was a 10-standard deviation positive temperature anomaly (relative to the 1975–2005 climatology) at 850 hPa, with a southwesterly flow and warm advection over Greece and western Turkey from 11 to 20 May. At 500 hPa, a ridge was located over the Eastern Mediterranean, resulting in subsidence. The aims of this study were (a) to investigate the prevailing synoptic conditions during this event in order to document its occurrence and (b) to assess whether this out-of-season heatwave was predictable on subseasonal timescales. The subseasonal predictability is not a well-researched scientific topic in the Eastern Mediterranean Sea. The ensemble global forecasts from six international meteorological centres (European Centre for Medium-Range Weather Forecasts—ECMWF, United Kingdom Met Office—UKMO, China Meteorological Administration—CMA, Korea Meteorological Administration—KMA, National Centers for Environmental Prediction—NCEP and Hydrometeorological Centre of Russia—HMCR) and limited area forecasts using the Weather Research and Forecasting model with the Advanced Research dynamic solver (WRF) forced by Climate Forecast System version 2 (CFSv.2; NCEP) forecasts were evaluated for lead times ranging from two to six weeks using statistical scores. WRF was integrated using two telescoping nests covering Europe, the Mediterranean basin and large part of the Atlantic Ocean, with a grid spacing of 25 km, and Greece–western Turkey at 5 km. The results showed that there were some accurate forecasts initiated two weeks before the event’s onset. There was no systematic benefit from the increase of the WRF model’s resolution from 25 km to 5 km for forecasting the 850 hPa temperature, but regarding the prediction of maximum air temperature near the surface, the high resolution (5 km) nest of WRF produced a marginally better performance than the coarser resolution domain (25 km).

Evaluating climate change scenarios in the white volta basin: A statistical bias-correction approach

This study provides a critical assessment of future climate scenarios in the White Volta Basin (WVB), an area heavily reliant on groundwater resources. With monthly model results for two Shared Socioeconomic Pathway Scenarios (SSP2-4.5 and 5–8.5), seven (7) Coupled Model Intercomparison Project Phase 6 (CMIP6) models with spatial resolution ranging from 1.125° × 2.8° were assessed. The study also considered, three 30-year time intervals, 1971–2013 for the Baseline (historical) climate, the 2020s (2020–2040) 2050s (2041–2070), and the 2080s (2071–2075) for the future climate scenarios. The Climate Change for Watershed Modeling (CMhyd) software was used for bias correction of these models, with observational data sourced from the National Centers for Environmental Prediction's Climate Forecast System Reanalysis (NCEP CFSR) and 12 gridded climate stations. The bias-corrected models validated using R2, NSE, RMSE, PBIAS, and additional metrics, demonstrated good calibration results compared to observed data. Precipitation ensembles showed 97–99% R2, 94–99% NSE, 70–485 mm RMSE, and -9-5% PBIAS. Maximum and minimum bias corrected temperatures performance varied from 95 to 99% R2, 92–99% NSE, 0.01–0.07 RMSE, and 0.01–0.23 PBIAS, Overall, precipitation levels are expected to decline for SSP2-4.5, while under SSP5-8.5 are expected to increase until the 2080s across all scenarios in comparison to the baseline period. Maximum temperature will be considerably high under SSP5-8.5, with an estimated increase of around 4.3 °C/year in comparison to the reference period. The monthly average variation in the maximum temperature ranges from 0.62 to 2.43 0Cunder the SSP5-8.5 scenario. These findings reveal the potential impacts of climate change on agricultural productivity, groundwater recharge, and crucial information for the development of Ghana's National Determined Contributions (NDCs) and National Adaptation Plans (NAPs). Thus, emphasizing the need for comprehensive adaptation strategies to mitigate the climate change impact on water resources and ecosystem services.

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.

Assessing the suitability of CFSR data for SWAT model hydrologic simulation of Kunthipuzha river basin, Kerala, India

Among the different inputs for the hydrological model, well distributed and precise precipitation datahas a crucial role in accurately simulating the various processes in a watershed. Poor distribution network of rain gauges and lack of precise precipitation data is one of the most important problems involved in many Indian watersheds. This study investigates the potential of using an alternate source of data for hydrologic modelling. The Climate Forecast System Reanalysis (CFSR) data is a global, high resolution, coupled atmosphere-ocean-land surface-sea ice system. Ithas been reported as an alternative option for solving the data deficiency of certain watersheds. The suitability of the CFSR to model the stream flow of Kunthipuzha river, flowing through the famous Silent Valley National Park in Kerala was assessed. The Soil and Water Assessment Tool (SWAT) model was made use of for the simulation of hydrologic process. The model was simulated using calibrated parameters in which CN2, ALPHA_BF and ESCO are the major factors affecting runoff.The developed model was run with observed and predicted meteorological data (CFSR) and the simulated results of stream flow were compared using Nash Sutcliffe Efficiency (NSE), Coefficient of determination (R2) and Root mean Square Error (RMSE). The NSE, R2 and RMSE obtained when observed data was usedfor modelling were 0.82, 0.85 and 29.25 respectively, whereas with CFSR data, the values were 0.70, 0.72 and 37.18 respectively. The streamflow modelled with SWAT using observed meteorological data wascloser to the measured streamflow as compared with that using CFSR data. The NSE and R2 obtained with CFSR data (0.7 & 0.72) indicates that gridded data (CFSR data) can perhaps be utilized in data scare regions with reasonable accuracy.

Effects of Arctic sea-ice concentration on turbulent surface fluxes in four atmospheric reanalyses

Abstract. A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the turbulent surface fluxes of sensible and latent heat in the Arctic. We analyse these effects utilising four global atmospheric reanalyses, ERA5, JRA-55, MERRA-2, and NCEP/CFSR (including both the NCEP Climate Forecast System Reanalysis (CFSR) and the NCEP Climate Forecast System Version 2 (CFSv2)), and evaluate their uncertainties arising from inter-reanalysis differences in SIC and in the sensitivity of the turbulent surface fluxes to SIC. The magnitude of the differences in SIC is up to 0.15 but typically around 0.05 in most of the Arctic over all four seasons. Orthogonal-distance regression and ordinary-least-squares regression analyses indicate that the greatest sensitivity of both the latent and the sensible heat flux to SIC occurs in the cold season, November to April. For these months, using daily means of data, the average sensitivity is 400 W m−2 for the latent heat flux and over 800 W m−2 for the sensible heat flux per unit of SIC (change in SIC from 0 to 1), with differences between reanalyses that are as large as 300 W m−2 for the latent heat flux and 600 W m−2 for the sensible heat flux per unit of SIC. The sensitivity is highest for the NCEP/CFSR reanalysis. Comparing the periods 1980–2000 and 2001–2021, we find that the effect of SIC on turbulent surface fluxes has weakened owing to the increasing surface temperature of sea ice and sea-ice decline. The results also indicate signs of a decadal-scale improvement in the mutual agreement between reanalyses. The effect of SIC on turbulent surface fluxes arises mostly via the effect of SIC on atmosphere–surface differences in temperature and specific humidity, whereas the effect of SIC on wind speed (via surface roughness and atmospheric-boundary-layer stratification) partly cancels out in the turbulent surface fluxes, as the wind speed increases the magnitudes of both upward and downward fluxes.

Multimodel comparison and assessment of short to medium range precipitation and temperature forecasts over India: Implications towards forecasting of meteorological indices in India

AbstractA comprehensive assessment of the forecast skill of various meteorological indices over the Indian region from different numerical weather prediction (NWP) models is lacking in the literature. In this study the performance of four NWP models, namely Global Ensemble Forecast System (GEFSv12), European Center for Medium Range Forecasting (ECMWF), Climate Forecast System (CFSv2) and Indian Institute of Tropical Meteorology (IITM) towards forecasting of precipitation, temperature and associated meteorological indices, is evaluated at short to medium timescales across the Indian region. Further, the effect of ocean atmospheric (OA) oscillations on the precipitation/temperature forecast skill from the different NWP models is also assessed. Results show that the NWP models are better in predicting the meteorological indices than the quantitative forecasts. The ECMWF model was found to be the best for PCP forecasting with CFSv2 performing poorly. For temperature the GEFSv12 model performance was the lowest, compared to rest of the models. The models show poor skill in forecasting monsoon season precipitation compared to non‐monsoon and the temperature forecasts from the NWP models are particularly poor for the northern basins. Skilful temperature forecasts are observed in the Northwestern, Indo‐Gangetic and Central basins for the CFSv2 and ECMWF models. The forecast skill of precipitation indices are higher in the northwestern, central and Indo‐Gangetic basins compared to the rest. The skill of precipitation indices, namely rainy days, extreme rainy days and consecutive wet days, is higher during the monsoon seasons while the prediction skill of consecutive dry days is higher during the non‐monsoon season. OA analysis revealed that the ENSO phases have dominant effect on the forecast skill of the precipitation only. The temperature and meteorological indices forecasts are not affected significantly by the OA phases. The outcomes of this study have implications towards irrigation scheduling and water resources management decision making in India.