Climate Forecast System Version Research Articles

The impact of rain to observed signal from Chinese Gaofen-3 synthetic aperture radar in typhoons

Gaofen-3 (GF-3), a Chinese civil synthetic aperture radar (SAR) at C-band, has operated since August 2016. Remarkably, several typhoons have been captured by GF-3 around the China Seas over its last two-year mission. In this study, six images acquired in Global Observation (GLO) and Wide ScanSAR (WSC) modes at vertical-vertical (VV) polarization channel are discussed. This work focuses on investigating the observation of rainfall using GF-3 SAR. These images were collocated with winds from the European Centre for Medium-Range Weather Forecasts (ECMWF), significant wave height simulated from the WAVEWATCH-III (WW3) model, sea surface currents from climate forecast system version 2 (CFSv2) of the National Centers for Environmental Prediction (NCEP) and rain rate data from the Tropical Rainfall Measuring Mission (TRMM) satellite. Sea surface roughness, was compared with the normalized radar cross section (NRCS) from SAR observations, and indicated a 0.8 correlation (COR). We analyzed the dependences of the difference between model-simulated NRCS and SAR-measured NRCS on the TRMM rain rate and WW3-simulated significant wave height. It was found that the effects of rain on SAR damps the radar signal at incidence angles ranging from 15° to 30°, while it enhances the radar signal at incidence angles ranging from 30° to 45° and incidence angles smaller than 10°. This behavior is consistent with previous studies and an algorithm for rain rate retrieval is anticipated for GF-3 SAR.

Seasonal Climate Prediction Models for the Number of Landfalling Tropical Cyclones in China

Two prediction models are developed to predict the number of landfalling tropical cyclones (LTCs) in China during June–August (JJA). One is a statistical model using preceding predictors from the observation, and the other is a hybrid model using both the aforementioned preceding predictors and concurrent summer large-scale environmental conditions from the NCEP Climate Forecast System version 2 (CFSv2). (1) For the statistical model, the year-to-year increment method is adopted to analyze the predictors and their physical processes, and the JJA number of LTCs in China is then predicted by using the previous boreal summer sea surface temperature (SST) in Southwest Indonesia, preceding October South Australia sea level pressure, and winter SST in the Sea of Japan. The temporal correlation coefficient between the observed and predicted number of LTCs during 1983–2017 is 0.63. (2) For the hybrid prediction model, the prediction skill of CFSv2 initiated each month from February to May in capturing the relationships between summer environmental conditions (denoted by seven potential factors: three steering factors and four gene-sis factors) and the JJA number of LTCs is firstly evaluated. For the 2- and 1-month leads, CFSv2 has successfully reproduced these relationships. For the 4-, 3-, and 2-month leads, the predictor of geopotential height at 500 hPa over the western North Pacific (WNP) shows the worst forecasting skill among these factors. In general, the summer relative vorticity at 850 hPa over the WNP is a modest predictor, with stable and good forecasting skills at all lead times.

Variability of Intraseasonal Oscillations and Synoptic Signals in Sea Surface Salinity in the Bay of Bengal

Abstract Intraseasonal oscillations (ISOs) significantly impact southwest monsoon precipitation and Bay of Bengal (BoB) variability. The response of ISOs in sea surface salinity (SSS) to those in the atmosphere is investigated in the BoB from 2005 to 2017. The three intraseasonal processes examined in this study are the 30–90-day and 10–20-day ISOs and 3–7-day synoptic weather signals. A variety of salinity data from NASA’s Soil Moisture Active Passive (SMAP) and the European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity (SMOS) satellite missions and from reanalysis using the Hybrid Coordinate Ocean Model (HYCOM) and operational analysis of Climate Forecast System version 2 (CFSv2) were utilized for the study. It is found that the 30–90-day ISO salinity signal propagates northward following the northward propagation of convection and precipitation ISOs. The 10–20-day ISO in SSS and precipitation deviate largely in the northern BoB wherein the river runoff largely impacts the SSS. The weather systems strongly impact the 3–7-day signal in SSS prior to and after the southwest monsoon. Overall, we find that satellite salinity products captured better the SSS signal of ISO due to inherent inclusion of river runoff and mixed layer processes. CFSv2, in particular, underestimates the SSS signal due to the misrepresentation of river runoff in the model. This study highlights the need to include realistic riverine freshwater influx for better model simulations, as accurate salinity simulation is mandatory for the representation of air–sea coupling in models.

Severe Marine Weather Systems During Freeze-Up in the Chukchi Sea: Cold-Air Outbreak and Mesocyclone Case Studies From Satellite Multisensor Measurements and Reanalysis Datasets

In the present article, cold-air outbreak (CAO) and polar low (PL) events were examined over the Chukchi Sea during low sea ice extent of the basin in the November–December period, 2015. An analysis of weather events was performed using multisensor satellite measurements, the NCEP Climate Forecast System, version 2 (CFSv2), and Arctic System Reanalysis, version 2 (ASR2). The CFSv2 data showed that the most intense cold advection occurred at the 1000–900 hPa boundary layer. It was shown that CAO had been developing from November 30 to December 6 and was accompanied by the sea surface wind speed exceeding 20 m/s and rapid formation and drift of sea ice. Analysis of the sea ice response to the CAO showed fast freezing of the Chukchi Sea for seven days indicating excessive heat loss of the water basin. In addition, significant increase in the open and very open ice areas was observed. These areas could be exposed to excessive cold atmosphere, releasing heat into the atmosphere. PL events have been identified during intensive mesocyclogenesis over the sea during 15–17 November. Weather conditions were characterized by the values of wind speed reaching 15–17 m/s and dry air with total water vapor content (5–7 kg/m2). Analysis of the synthetic aperture radar image revealed the position and horizontal size of the meso-α-cyclone, the fine structure of the mesoscale frontal system of the PL forming meso-γ-vortices, and the wave-like line of the horizontal wind shear in the local convergence zone. The representation of PL events in sea level pressure and near-surface wind speed fields from the ASR2 and CFSv2 datasets was ambiguous. However, the satellite-based and CFSv2 wind speeds better agreed for high-wind conditions.

Evaluation of 2-m temperature and precipitation products of the Climate Forecast System version 2 over Iran

Using a continuous multi-decadal simulations over the period 1981–2010, subseasonal to seasonal simulations of the Climate Forecast System version 2 (CFSv2) over Iran against the Climatic Research Unit (CRU) dataset are evaluated. CFSv2 shows cold biases over northern hillsides of the Alborz Mountains with the Mediterranean climate and warm biases over northern regions of the Persian Gulf and the Oman Sea with a dry climate. Magnitude of the model bias for 2-m temperature over different regions of Iran varies by season, with the least bias in temperate seasons of spring and autumn, and the largest bias in summer. The model bias decreases as temporal averaging period increases from seasonal to annual. The forecast generally produces dry and wet biases over dry and wet regions of Iran, respectively. In general, 2-m temperature over Iran is better captured than precipitation, but the prediction skill of precipitation is generally high over western Iran. Averaged over Iran, observations indicated that 2-m temperature has been gradually increasing during the studied period, with a rate of approximately 0.5 °C per decade, and the upward trend is well simulated by CFSv2. Averaged over Iran, both observations and simulation results indicated that precipitation has been decreasing in spring, with averaged decreasing trends of 0.8 mm (observed) and 1.7 mm (simulated) per season each year during the period 1981–2010. Observations indicated that the maximum increasing trend of 2-m temperature has occurred over western Iran (nearly 0.7 °C per decade), while the maximum decreasing trend of annual precipitation has occurred over western and parts of southern Iran (nearly 45 to 50 mm per decade).

Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts

AbstractDespite recent progress in seasonal forecast systems, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic‐dynamical model (SDM) to predict the IOD using seasonally modulated El Niño–Southern Oscillation (ENSO) forcing together with a seasonally modulated Indian Ocean coupled ocean‐atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System version 2 and the Scale Interaction Experiment–Frontier system. The improvements mainly originate from better prediction of ENSO‐dependent IOD events and from reducing false alarms. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal‐to‐noise ratio of the system. Therefore, potential future ENSO improvements in models should translate to more skillful IOD predictions.

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.

The Influence of Summer Deep Soil Temperature on Early Winter Snow Conditions in Eurasia in the NCEP CFSv2 Simulation

AbstractThe National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) has a large cold bias in the model's deep soil temperature during summer. This study explores the potential triggering effect of that bias on excessive Eurasian snow cover in early winter. Snow cover appears erroneously early in the fall, especially in western Eurasia, in long simulations with CFSv2. The seasonal transition may be too early because the model land surface temperature (LST) reaches its freezing point earlier than observed, so that new snow cannot melt. This process initiates snow‐albedo feedback too early. The early cooling of LST is partially influenced by a seasonal resurfacing of the cold bias in the deep soil layer. From winter to early spring, a cold bias prevails in LST and upper soil temperature as snow cover remains. During this season, the temperature in the deep soil is generally warmer than in the upper soil and has relatively little bias. From spring to summer, the cold bias in the upper soil becomes smaller as it warms up in response to solar heating. On the other hand, the deep soil temperature has a noticeably smaller seasonal change than observed, resulting in a severe cold bias during summer. As the solar radiation declines quickly in early fall, the cold deep soil temperature causes additional cooling in the upper soil layer and helps to bring LST to the freezing point early in the western Eurasia, which leads to enhanced bias in the snow properties.

Coupling of a physically based lake model into the climate forecast system to improve winter climate forecasts for the Great Lakes region

In this study, we coupled the two-layer one-dimensional Freshwater lake (FLake) model into the Climate Forecast System (CFS) version 2 to improve simulations of the effects of the Great Lakes on winter climate forecasts in that region. We first incorporated global lake fraction and depth data into the coupled CFS-FLake through a subgridding system. An interface between the CFS and FLake was then developed to link the two models for energy and water flux exchanges. The lake scheme was triggered only if the lake fraction was more than 10% in one CFS grid cell. We conducted ensemble retrospective forecasts with CFS-FLake for the period of 1997 through 2016 with 9 monthly leads. These forecasts were assessed to obtain a better understanding of the role of the Great Lakes in the climate system for winter, when significant lake-effect precipitation often occurs. Our results indicate that forecasts of lake surface temperature (LST), lake ice coverage (LIC), and precipitation with CFS-FLake were consistently better than those with CFS. The major improvements resulted from changes in land use type for the Great Lakes, from ocean and land in CFS to lakes in CFS-FLake. With the change from ocean to lake, LST mostly decreased with increasing LIC, resulting in lower surface heat and water fluxes to the atmosphere during the winter. However, with the change from land to lake, LST increased, leading to higher heat and water fluxes. In the meantime, precipitation predicted by CFS-FLake was reduced quite significantly over the Great Lakes compared to that by CFS. This reduction was caused by suppressed rising motion due to increased stability in the lower atmosphere as a result of lowered surface heat and water fluxes. The results from this study indicate that local and mesoscale surface and atmospheric processes significantly affect regional climate forecasts, and the coupled CFS-FLake model will have a broader impact on climate and hydrology research and forecasts.

Impact of convective parameterization on the seasonal prediction skill of Indian summer monsoon

The sensitivity of seasonal predictions of the Indian summer monsoon (ISM) to convection parameterization schemes (CPS) is studied using 37 years of hindcast experiments. The predictions are quite sensitive to changes in these schemes and improve the skill by 18–28%. Though the mean state circulation and rainfall over India improves, the sea surface temperature (SST) biases increase in the sensitivity experiments compared to the control run. The ability of the model to realistically capture the teleconnections associated with monsoon such as the El-Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) also appears to change with different CPS. It is found that the suitability of a CPS for ISM in the Climate Forecast System version 2 (CFSv2) stems from its ability to capture cloud fractions realistically and keep the SST biases to a minimum. The revised Simplified-Arakawa–Schubert (SAS2, Han and Pan in Weather Forecast 26:520–533. https://doi.org/10.1175/waf-d-10-05038.1 , 2011) scheme gives better prediction skill for ISM compared to the skill score obtained from SAS2 with shallow convection (SAS2sc) primarily because it simulates realistic clouds, without aggravating the SST biases, particularly in the tropical Pacific Ocean, and captures the Indian Ocean teleconnections realistically. SAS2sc significantly under-estimates the low-level clouds over global equatorial region, despite simulating better mid and high-level clouds, higher Nino 3.4 skill, and better inter-annual variability of ISM. The cold SST bias in the tropical basins is large in SAS2sc. Therefore, to exploit the merits of SAS2sc, unrealistic suppression of low clouds needs to be addressed, and the cold SST biases need to be minimized.

Seasonal hydroclimatic ensemble forecasts anticipate nutrient and suspended sediment loads using a dynamical-statistical approach

Subseasonal-to-seasonal (S2S) water quantity and quality forecasts are needed to support decision and policy making in multiple sectors, e.g. hydropower, agriculture, water supply, and flood control. Traditionally, S2S climate forecasts for hydroclimatic variables (e.g. precipitation) have been characterized by low predictability. Since recent next-generation S2S climate forecasts are generated using improved capabilities (e.g. model physics, assimilation techniques, and spatial resolution), they have the potential to enhance hydroclimatic predictions. Here, this is tested by building and implementing a new dynamical-statistical hydroclimatic ensemble prediction system. Dynamical modeling is used to generate S2S flow predictions, which are then combined with quantile regression to generate water quality forecasts. The system is forced with the latest S2S climate forecasts from the National Oceanic and Atmospheric Administration’s Climate Forecast System version 2 to generate biweekly flow, and monthly total nitrogen, total phosphorus, and total suspended sediment loads. By implementing the system along a major tributary of the Chesapeake Bay, the largest estuary in the US, we demonstrate that the dynamical-statistical approach generates skillful flow, nutrient load, and suspended sediment load forecasts at lead times of 1–3 months. Through the dynamical-statistical approach, the system comprises a cost and time effective solution to operational S2S water quality prediction.

Simulations of Monsoon Intraseasonal Oscillation Using Climate Forecast System Version 2: Insight for Horizontal Resolution and Moist Processes Parameterization

In the present study, we analyze the Climate Forecast System version 2 (CFSv2) model in three resolutions, T62, T126, and T382. We evaluated the performance of all three resolutions of CFSv2 in simulating the Monsoon Intraseasonal Oscillation (MISO) of the Indian summer monsoon (ISM) by analyzing a suite of dynamic and thermodynamic parameters. Results reveal a slower northward propagation of MISO in all models with the characteristic northwest–southeast tilted rain band missing over India. The anomalous moisture convergence and vorticity were collocated with the convection center instead of being northwards. This affected the northward propagation of MISO. The easterly shear to the north of the equator was better simulated by the coarser resolution models than CFS T382. The low level specific humidity showed improvement only in CFS T382 until ~15° N. The analyses of the vertical profiles of moisture and its relation to rainfall revealed that all CFSv2 resolutions had a lower level of moisture in the lower level (< 850 hPa) and a drier level above. This eventually hampered the growth of deep convection in the model. These model shortcomings indicate a possible need of improvement in moist process parameterization in the model in tune with the increase in resolution.

High resolution modeling of western Alaskan tides and storm surge under varying sea ice conditions

A depth-integrated high resolution (down to 25–50 m) tide and storm surge model has been developed for the Alaska region with a focus on the coasts of western Alaska. The model uses the ADCIRC basin-to-channel scale unstructured grid circulation code forced with Climate Forecast System version 2 reanalysis meteorology. The tidal solution has been validated at 121 shelf and nearshore stations, with lower overall errors than a data assimilated tidal model (FES2012). The ADCIRC model shows considerable skill at simulating tide and coastal surge, and resulting overland flooding on the Yukon–Kuskokwim Delta, during both summer and winter storms. The root-mean-square-error of the model does not exceed 20 cm at any station, outperforming the Global Ocean Forecasting System (GOFS 3.1), a 1/12∘ data assimilated ocean general circulation model which is coupled to sea ice physics. The ADCIRC model incorporates the effect of sea ice through parameterizations of the wind drag coefficient, modifying the air–sea momentum transfer under ice coverage. Three large winter storms with distinctly different ice coverages along the coasts of western Alaska were chosen to exhibit the variable effect of sea ice on the resulting storm surge. Under forming coastal ice coverage, local increases in water levels due to ice is seen in coastal areas, and under predominantly dense pack ice, an increase in momentum transfer in the marginal sea ice at the shelf break leads to an increase in sea levels across the entire Bering Sea, both commensurate with observations. Under the most variable ice fields, results are mixed, in part due to uncertainties in the air–sea–ice drag parameterization such as related to the assumption that sea ice drift and wind velocities are always well correlated. Including a description of sea ice drift and current speeds explicitly in the air–sea–ice drag formulation and considering wave–surge–ice interaction should improve the description of momentum transferred to the water column and resulting simulation of surge.

The evaluation of drought indices: Standard Precipitation Index, Standard Precipitation Evapotranspiration Index, and Palmer Drought Severity Index in Cilacap-Central Java

The Cilacap Regency-Central Java, is the largest agricultural area with a high level of drought vulnerability. This study aims to evaluate three indices in quantifying a drought condition in Cilacap Regency and its application in forecasting. The indices that were used in this study are Standard Precipitation Index (SPI), Standard Precipitation Evapotranspiration Index (SPEI), and Palmer Drought Severity Index (PDSI). The comparison between SPI and SPEI shows that there is no significant difference in terms of determining the drought severity. SPEI should be used when there is a temperature difference more than 2°C in 30 years. PDSI may provide more frequency of drought event and a good result in indicating the effects of drought on agricultural productivity. We used Climate Forecast System Version 2 (CFSv2) to predict drought severity by SPI. The result of model prediction shows that there is no significant improvement in accuracy before and after statistical bias correction. The prediction can be done on three months (lead3) before initial planting.

The Impact of Modified Fractional Cloud Condensate to Precipitation Conversion Parameter in Revised Simplified Arakawa‐Schubert Convection Parameterization Scheme on the Simulation of Indian Summer Monsoon and Its Forecast Application on an Extreme Rainfall Event Over Mumbai

AbstractThe impact of modified fractional conversion parameter (from cloud condensate to precipitation) in the revised simplified Arakawa‐Schubert (RSAS) convection scheme in Climate Forecast System version 2 on the simulation of Indian summer monsoon (ISM) is examined. While the default fractional conversion parameter is constant in RSAS, the modified parameter has the form of an exponential function of temperature above the freezing level, whereas below the freezing, level it is kept constant. The model simulation indicates RSAS with modified conversion parameter (RSAS_mod) shows a better fidelity in capturing the mean monsoon features over the ISM region. The spatial distribution of precipitation shows notable improvement over the ISM region. Most of the global general circulation models has a tendency to grossly overestimate (underestimate) the convective (large‐scale) rainfall over the ISM region, which has somewhat improved in RSAS_mod simulation. It is suggested that reduced rate of conversion of cloud condensate to convective precipitation above the freezing level leads to suppression of convective precipitation, which further increases the detrained moisture from the upper‐level, resulting enhancement in large‐scale precipitation. Further, improvement has been noted in outgoing longwave radiation, wind circulation, total cloud fraction, and dynamical and thermodynamical processes in RSAS_mod simulation. The modified conversion parameter helps in improving the feedback between moisture and convective processes through better lower tropospheric moistening. In addition to mean summer monsoon, the RSAS_mod indicates its potential in predicting an extreme rainfall event over Mumbai in high‐resolution global forecast system at T1534 horizontal resolution. However, its fidelity needs to be further tested for more number of heavy rainfall events.

An evaluation of East Asian summer monsoon forecast with the North American Multimodel Ensemble hindcast data

AbstractThe abilities of three models (Climate Forecast System version 2 [CFSv2], Canadian Coupled Climate Model version 3 [CanCM3] and Canadian Coupled Climate Model version 4 [CanCM4]) in the North American Multimodel Ensemble for the East Asian summer monsoon (EASM) forecast were evaluated with their 29‐year hindcast data (1982–2010). Many EASM features including monsoon precipitation centres, large‐scale monsoon circulations and monsoon onset and retreat are generally captured by the three models and their ensemble mean, and the multimodel ensemble has the best performance. Since the East Asian domain includes the tropical western North Pacific summer monsoon (WNPSM) and the subtropical continent monsoon, two well‐known monsoon indices, the WNPSM index (WNPSMI) and EASM index (EASMI), and their associated low‐level winds and precipitation anomalies are well forecasted by the models. However, the forecast performance generally decreases as the leads increase, and the performance of EASMI is not as good as that of WNPSMI. CFSv2 forecasts well at leads up to 6 months whereas the skill of CanCM3 (CanCM4) decreases rapidly when the lead increases to 2 months (3 months). The failure of CanCM3 is mainly attributed to the poor forecast of the relationship of EASMI with the El Niño‐Southern Oscillation and Northern Indian Ocean‐western North Pacific (WNP) sea surface temperature anomaly. However, the causes of the poor forecast of CanCM4 for EASMI require further investigation. Sources of the forecast error (FE), which is the difference between model and observation for monsoon precipitation, are more significant than those of the predictability error (PE), which originates from the initial condition error, indicating that model deficiency plays a dominant role in limiting the EASM precipitation forecast. However, the PE cannot be neglected over the tropical western Pacific in CFSv2, over the WNP in CanCM3 and over the Tibetan Plateau in CanCM4. As the lead time increases, the FE does not remarkably change whereas the PE decreases significantly.

Developing Subseasonal to Seasonal Climate Forecast Products for Hydrology and Water Management

AbstractWe describe a new effort to enhance climate forecast relevance and usability through the development of a system for evaluating and displaying real‐time subseasonal to seasonal (S2S) climate forecasts on a watershed scale. Water managers may not use climate forecasts to their full potential due to perceived low skill, mismatched spatial and temporal resolutions, or lack of knowledge or tools to ingest data. Most forecasts are disseminated as large‐domain maps or gridded datasets and may be systematically biased relative to watershed climatologies. Forecasts presented on a watershed scale allow water managers to view forecasts for their specific basins, thereby increasing the usability and relevance of climate forecasts. This paper describes the formulation of S2S climate forecast products based on the Climate Forecast System version 2 (CFSv2) and the North American Multi‐Model Ensemble (NMME). Forecast products include bi‐weekly CFSv2 forecasts, and monthly and seasonal NMME forecasts. Precipitation and temperature forecasts are aggregated spatially to a United States Geological Survey (USGS) hydrologic unit code 4 (HUC‐4) watershed scale. Forecast verification reveals appreciable skill in the first two bi‐weekly periods (Weeks 1–2 and 2–3) from CFSv2, and usable skill in NMME Month 1 forecast with varying skills at longer lead times dependent on the season. Application of a bias‐correction technique (quantile mapping) eliminates forecast bias in the CFSv2 reforecasts, without adding significantly to correlation skill.

Climatic characteristics of drift ice in the northern Barents Sea

Abstract In this paper, a preliminary climatic description of the long-term offshore drift ice characteristics in the northern Barents Sea has been investigated from 1987 to 2016 based on the satellite ice motion datasets from National Snow and Ice Data Center (NSIDC) and reanalysis ice thickness datasets from National Centers for Environmental Prediction (NCEP)-Climate Forecast System Reanalysis (CFSR) and Climate Forecast System Version 2 (CFSv2). Both the ice velocity and thickness conditions have been studied at the three fixed locations from west to east. Annual and monthly drift ice roses indicate that the directions from WSW to SE are primarily prevailing, particularly in winter months. Besides, the annual ice speed extremums exceeding 40 cm s–1 mostly occur in the southerly directions from November to April. For the ice thickness, results reveal that it is prominently distributed in a thicker interval between 70 and 120 cm, and a thinner interval between 20 and 70 cm. The annual thickness maxima approximately range from 90 to 170 cm, primarily occurring from May to June, and demonstrate a light decreasing trend.

Credibility of Convection-Permitting Modeling to Improve Seasonal Precipitation Forecasting in the Southwestern United States

Sub-seasonal to seasonal (S2S) forecasts are useful for critical planning and management decisions in multiple sectors. Presently, in the United States, the primary source for real time seasonal climate forecast comes from the Climate Prediction Center within the National Center for climate Prediction (NCEP) which uses its model forecast component Climate Forecast System Version 2 (CFSv2) of North American Multi-Model Ensemble (NMME). In comparison to the cool season, the level of skill in warm season seasonal forecasts of precipitation produced by the NMME is much lower due to the poor climatological representation of warm season convective precipitation. This study shows that dynamical downscaling using a regional climate model at a convective permitting scale driven by boundary conditions from global reanalysis of CFS (CFSR) enhances regional forecast skill by better resolving the regional forcings and processes that generate the regional response from the large-scale circulation anomaly. Improvement in S2S forecast skill needs combination of skillful forecast of the large-scale circulation anomaly as well as its regional response in temperature and precipitation, the latter specially where it is modulated primarily by regional forcings like topography or land cover. To fully realize the potential in improving warm season seasonal forecasts using a dynamical modeling approach, we performed dynamically downscaled simulations with Weather Research and Forecasting model over the Upper and Lower Colorado basin at 12 km and 3 km grid spacing for 2000-2011. The additional convective-permitting nested domain of 3km resolution significantly reduces the bias in mean (~2mm/day) and extreme (~4 mm/day) summer precipitation when compared to coarser domain of 12 km resolution. The convective permitting modeling product also better represents eastward propagation of the moist convective summer systems, which are major source of summer rainfall in the southwest United Sates. Our findings provide important insights for S2S prediction using convective permitting model.

Effects of a multilayer snow scheme on the global teleconnections of the Indian summer monsoon

Eurasian snow is one of the slowly varying boundary forcings known to have significant influences on the mean and variability of the Indian summer monsoon rainfall (ISMR). A multilayer complex snow scheme, incorporated into the state‐of‐the‐art coupled Climate Forecast System version 2 (CFSv2) showed significant improvements in the simulation of mean ISMR, snow, and Northern Hemisphere surface and tropospheric temperature. Here we show that a realistic simulation of high‐latitude snow decreases the north–south temperature gradient, which in turn decreases the meridional transport of energy from the Equator to the Pole, consequently affecting the tropical sea surface temperature (SST) and air–sea interactions. The global teleconnections of the ISMR with SST and 2 m temperature over land are also improved considerably in association with improved simulation of the oceanic natural modes of variability. Our findings provide new insights for the relationship between the winter Eurasian snow and the following ISMR, namely that the same relationship may be understood through a framework of meridional atmospheric energy transport and its effects on the tropical air–sea interactions. The improvements in the global teleconnection in the modified version of CFSv2 may have implications in the ISMR predictability and prediction skill.