Atmospheric Model Outputs Research Articles

An intelligent deep learning-based approach for downscaling atmospheric general circulation model outputs

An intelligent deep learning-based approach for downscaling atmospheric general circulation model outputs

Modelling snowpack on ice surfaces with the ORCHIDEE land surface model: application to the Greenland ice sheet

Abstract. Current climate warming is accelerating mass loss from glaciers and ice sheets. In Greenland, the rates of mass changes are now dominated by changes in surface mass balance (SMB) due to increased surface melting. To improve the future sea-level rise projections, it is therefore critical to have an accurate estimate of the SMB, which depends on the representation of the processes occurring within the snowpack. The Explicit Snow (ES) scheme implemented in the land surface model Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) has not yet been adapted to ice-covered areas. Here, we present the preliminary developments we made to apply the ES model to glaciers and ice sheets. Our analysis mainly concerns the model's ability to represent ablation-related processes. At the regional scale, our results are compared to the MAR regional atmospheric model outputs and to MODIS albedo retrievals. Using different albedo parameterizations, we performed offline ES simulations forced by the MAR model over the 2000–2019 period. Our results reveal a strong sensitivity of the modelled SMB components to the albedo parameterization. Results inferred with albedo parameters obtained using a manual tuning approach present very good agreement with the MAR outputs. Conversely, with the albedo parameterization used in the standard ORCHIDEE version, runoff and sublimation were underestimated. We also tested parameters found in a previous data assimilation experiment, calibrating the ablation processes using MODIS snow albedo. While these parameters greatly improve the modelled albedo over the entire ice sheet, they degrade the other model outputs compared to those obtained with the manually tuned approach. This is likely due to the model overfitting to the calibration albedo dataset without any constraint applied to the other processes controlling the state of the snowpack. This underlines the need to perform a “multi-objective” optimization using auxiliary observations related to internal snowpack processes. Although there is still room for further improvements, the developments reported in the present study constitute an important advance in assessing the Greenland SMB with possible extension to mountain glaciers or the Antarctic ice sheet.

Regional evaluation of simulated waves during tropical storm events in the Gulf of Mexico

High waves in the Gulf of Mexico are mainly originated in tropical cyclones and winter storms. The availability of accurate estimations of high wave characteristics is an essential prerequisite for marine spatial planning, coastal zone management, and marine developments over the Northeastern Gulf of Mexico (NeGoM). Moreover, accurate wave simulation relies upon accurate wind field estimations. The wind field data used as input for numerical wave models is typically obtained from global and regional atmospheric model outputs. However, these model outputs may exhibit higher bias relative to observations, especially during fast-traveling tropical weather events. This study evaluates wave sensitivity over the NeGoM to different input wind speed and direction sources using unstructured SWAN model simulations. Five wind data products were employed in this study from ECMWF and NCEP global and regional models to examine the impact of wind forcing on wave simulations. The quality of wind field input data from these sources was evaluated relative to available observations (wind speed and direction) at National Data Buoy Center stations. A detailed sensitivity analysis is carried out to determine optimal wave model configuration. The model's performance is evaluated by comparing its wave simulation results with observational data during selected periods, simultaneously assessing the quality and sufficiency of wind data inputs from various sources. Our study shows that wind fields from FNL and NAM datasets are better correlated with observational data, in terms of vector correlation. Simulations using ECMWF – Real-Time product, combined with Janssen white-capping scheme better estimate wave conditions over the NeGoM compared with the other wind forcings used in this study. Moreover, Janssen white-capping scheme seems to be more realistic for estimations during Hurricane Michael; while for Hurricane Ida, Janssen generation scheme tends to overestimate the wave heights and Komen provides more accurate results.

Advanced Sea Ice Modeling for Short-Term Forecasting for Alaska’s Coasts

Abstract In Alaska’s coastal environment, accurate information of sea ice conditions is desired by operational forecasters, emergency managers, and responders. Complicated interactions among atmosphere, waves, ocean circulation, and sea ice collectively impact the ice conditions, intensity of storm surges, and flooding, making accurate predictions challenging. A collaborative work to build the Alaska Coastal Ocean Forecast System established an integrated storm surge, wave, and sea ice model system for the coasts of Alaska, where the verified model components are linked using the Earth System Modeling Framework and the National Unified Operational Prediction Capability. We present the verification of the sea ice model component based on the Los Alamos Sea Ice Model, version 6. The regional, high-resolution (3 km) configuration of the model was forced by operational atmospheric and ocean model outputs. Extensive numerical experiments were conducted from December 2018 to August 2020 to verify the model’s capability to represent detailed nearshore and offshore sea ice behavior, including landfast ice, ice thickness, and evolution of air–ice drag coefficient. Comparisons of the hindcast simulations with the observations of ice extent presented the model’s comparable performance with the Global Ocean Forecast System 3.1 (GOFS3.1). The model’s skill in reproducing landfast ice area significantly outperformed GOFS3.1. Comparison of the modeled sea ice freeboard with the Ice, Cloud, and Land Elevation Satellite-2 product showed a mean bias of −4.6 cm. Daily 5-day forecast simulations for October 2020–August 2021 presented the model’s promising performance for future implementation in the coupled model system. Significance Statement Accurate sea ice information along Alaska’s coasts is desired by the communities for preparedness of hazardous events, such as storm surges and flooding. However, such information, in particular predicted conditions, remains to be a gap. This study presents the verification of the state-of-art sea ice model for Alaska’s coasts for future use in the more comprehensive coupled model system where ocean circulation, wave, and sea ice models are integrated. The model demonstrates comparable performance with the existing operational ocean–ice coupled model product in reproducing overall sea ice extent and significantly outperformed it in reproducing landfast ice cover. Comparison with the novel satellite product presented the model’s ability to capture sea ice freeboard in the stable ice season.

Process‐Based Intercomparison of Water Isotope‐Enabled Models and Reanalysis Nudging Effects

AbstractThe products from the Stable Water Isotope Intercomparison Group, Phase 2, are currently used for numerous studies, allowing water isotope model‐data comparisons with various isotope‐enabled atmospheric general circulation model (AGCMs) outputs. However, the simulations under this framework were performed using different parameterizations and forcings. Therefore, a uniform experimental design with state‐of‐the‐art AGCMs is required to interpret isotope observations rigorously. Here, we evaluate the outputs from three isotope‐enabled numerical models nudged by three different reanalysis products and investigate the ability of the isotope‐enabled AGCMs to reproduce the spatial and temporal patterns of water isotopic composition observed at the surface and in the atmospheric airborne water. Through correlation analyses at various spatial and temporal scales, we found that the model's performance depends on the model or reanalysis we use, the observations we compare, and the vertical levels we select. Moreover, we employed the stable isotope mass balance method to conduct decomposition analyses on the ratio of isotopic changes in the atmosphere. Our goal was to elucidate the spread in simulated atmospheric column δ18O, which is influenced by factors such as evaporation, precipitation, and horizontal moisture flux. Satisfying the law of conservation of water isotopes, this budget method is expected to explain various fractionation phenomena in atmospheric meteorological and climatic events. It also aims to highlight the spreads in modeled isotope results among different experiments using multiple models and reanalyses, which are primarily dominated by uncertainties in moisture flux and precipitation, respectively.

On the Kalman Smoother Interpolation Error Distribution in Collocation Comparison of Atmospheric Profiles

The intercomparison between different atmospheric monitoring systems is key for instrument calibration and validation. Common cases involve satellites, radiosonde and atmospheric model outputs. Since instruments and/or measures are not perfectly collocated, miss-collocation uncertainty must be considered in related intercomparison uncertainty budgets. This paper is motivated by the comparison of GNSS-RO, the Global Navigation Satellite System Radio Occultation, with ERA5, the version 5 Reanalysis of the European Centre for Medium-range Weather Forecasts. We consider temperature interpolation observed at GNSS-RO pressure levels to the ERA5 levels. We assess the interpolation uncertainty using as ‘truth’ high-resolution reference data obtained by GRUAN, the Reference Upper-Air Network of the Global Climate Observing System. In this paper, we propose a mathematical representation of the interpolation problem based on the well-known State-space model and the related Kalman filter and smoother. We show that it performs the same (sometimes better) than linear interpolation and, in addition, provides an estimate of the interpolation uncertainty. Moreover, with both techniques, the interpolation error is not Gaussian distributed, and a scaled Student’s t distribution with about 4.3 degrees of freedom is an appropriate approximation for various altitudes, latitudes, seasons and times of day. With our data, interpolation uncertainty results larger at the equator, the Mean Absolute Error being MAE≅0.32 K, and smaller at a high latitude, MAE≅0.21 K at −80° latitude. At lower altitudes, it is close to the measurement uncertainty, with MAE<0.2 K below the tropopause. Around 300 hPa, it starts increasing and reaches about 0.8 K above 100 hPa, except at the equator, where we observed MAE about 1 K.

Spatiotemporal Analysis of XCO2 and Its Relationship to Urban and Green Areas of China’s Major Southern Cities from Remote Sensing and WRF-Chem Modeling Data from 2010 to 2019

Monitoring CO2 concentrations is believed to be an effective measure for assisting in the control of greenhouse gas emissions. Satellite measurements compensate for the sparse and uneven spatial distribution of ground observation stations, allowing for the collection of a wide range of CO2 concentration data. However, satellite monitoring’s spatial coverage remains limited. This study fills the knowledge gaps of column-averaged dry-air mole fraction of CO2 (XCO2) products retrieved from the Greenhouse Gases Observing Satellite (GOSAT) and Orbiting Carbon Observatory Satellite (OCO-2) based on the normalized output of atmospheric chemical models, WRF-Chem, in Southern China during 2010–2019. Hefei (HF)/Total Carbon Column Observing Network (TCCON), Lulin (LLN)/World Data Centre for Greenhouse Gases (WDCGG) station observations were used to validate the results of void filling with an acceptable accuracy for spatiotemporal analysis (R = 0.96, R2 = 0.92, RMSE = 2.44 ppm). Compared to the IDW (inverse distance weighting) and Kriging (ordinary Kriging) interpolation methods, this method has a higher validation accuracy. In addition, spatiotemporal distributions of CO2, as well as the sensitivity of CO2 concentration to the urban built-up areas and urban green space areas in China’s major southern cities during 2010–2019, are discussed. The approximate annual average concentrations have gradually increased from 388.56 to 414.72 ppm, with an annual growth rate of 6.73%, and the seasonal cycle presents a maximum in spring and a minimum in summer or autumn from 2010 to 2019. CO2 concentrations have a strong positive correlation with the impervious area to city area ratio, while anomaly values of the impervious area to urban green area ratio occurred in individual cities. The experimental findings demonstrate the viability of the study hypothesis that combines remote sensing data with the WRF-Chem model to produce a local area dataset with high spatial resolution and an extracted urban unit from statistical data.

En-GARD: A Statistical Downscaling Framework to Produce and Test Large Ensembles of Climate Projections

Abstract Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically postprocess output from weather and climate models. The Ensemble Generalized Analog Regression Downscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations for use in parametric or nonparametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.

A One-Step Blended Soundproof-Compressible Model with Balanced Data Assimilation: Theory and Idealized Tests

Abstract A challenge arising from the local Bayesian assimilation of data in an atmospheric flow simulation is the imbalances it may introduce. Acoustic fast-mode imbalances of the order of the slower dynamics can be negated by employing a blended numerical model with seamless access to the compressible and the soundproof pseudo-incompressible dynamics. Here, the blended modeling strategy by Benacchio et al. is upgraded in an advanced numerical framework and extended with a Bayesian local ensemble data assimilation method. Upon assimilation of data, the model configuration is switched to the pseudo-incompressible regime for one time step. After that, the model configuration is switched back to the compressible model for the duration of the assimilation window. The switching between model regimes is repeated for each subsequent assimilation window. An improved blending strategy for the numerical model ensures that a single time step in the pseudo-incompressible regime is sufficient to suppress imbalances coming from the initialization and data assimilation. This improvement is based on three innovations: (i) the association of pressure fields computed at different stages of the numerical integration with actual time levels, (ii) a conversion of pressure-related variables between the model regimes derived from low Mach number asymptotics, and (iii) a judicious selection of the pressure variables used in converting numerical model states when a switch of models occurs. Idealized two-dimensional traveling vortex and buoyancy-driven bubble convection experiments show that acoustic imbalances arising from data assimilation can be eliminated by using this blended model, thereby achieving balanced analysis fields. Significance Statement Weather forecasting models use a combination of physics-based algorithms and meteorological measurements. A problem with combining outputs from the model with measurements of the atmosphere is that insignificant signals may generate noise and compromise the physical soundness of weather-relevant processes. By selecting atmospheric processes through the toggling of parameters in a mixed model, we propose to suppress the undesirable signals in an efficient way and retain the physical features of solutions produced by the model. The approach is validated here for acoustic imbalances using a compressible/pseudo-incompressible model pair. This development has the potential to improve the techniques used to bring observations into models and with them the quality of atmospheric model output.

Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data

Reliable long-horizon PM2.5 forecasts are crucial and beneficial for health protection through early warning against air pollution. However, the dynamic nature of air quality makes PM2.5 forecasts at long horizons very challenging. This study proposed a novel machine learning-based model (MCNN-BP) that fused multiple convolutional neural networks (MCNN) with a back-propagation neural network (BPNN) for making spatiotemporal PM2.5 forecasts for the next 72 h at 74 stations covering the whole Taiwan simultaneously. Model configuration involved an ensemble of massive hourly air quality and meteorological monitoring datasets and the existing publicly-available PM2.5 simulated (forecasted) datasets from an atmospheric chemical transport (ACT) model. The proposed methodology collaboratively constructed two CNNs to mine the observed data (the past) and the forecasted data from ACT (the future) separately. The results showed that the MCNN-BP model could significantly improve the accuracy of spatiotemporal PM2.5 forecasts and substantially reduce the forecast biases of the ACT model. We demonstrated that the proposed MCNN-BP model with effective feature extraction and good denoising ability could overcome the curse of dimensionality and offer satisfactory regional long-horizon PM2.5 forecasts. Moreover, the MCNN-BP model has considerably shorter computational time (5 min) and lower computational load than the compute-intensive ACT model. The proposed approach hits a milestone in multi-site and multi-horizon forecasting, which significantly contributes to early warning against regional air pollution.

Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases

Abstract. Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly, scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model, and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 h over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction (NWP) models where observations are sparse or non-existent, we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations, although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps. In addition there is a greater increase in skill score over time for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level; e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty, but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.

Summertime precipitation in Hokkaido and Kyushu, Japan in response to global warming

We analyzed a large ensemble dataset called the database for Policy Decision Making for Future climate change (d4PDF), which contains 60-km resolution atmospheric general circulation model output and 20-km resolution dynamical downscaling for the Japanese domain. The increase in moisture and precipitation, and their global warming response in June–July–August were described focusing on the differences between Hokkaido and Kyushu; Hokkaido is cool and dry and Kyushu is hot and humid in the current climate. The results suggested that the specific humidity increased almost following the Clausius Clapeyron relation, but the change in stationary circulation suppressed the precipitation increase, except for in western Kyushu. The + 4 K climate in Hokkaido would be as hot and humid as the present climate in Kyushu. The circulation change related to the southward shift of the jet stream and an eastward shift of the Bonin high weakened the moisture flux convergence via a stationary field over central Japan including eastern Kyushu. The transient eddy activity counteracted the increase in humidity, so that the moisture flux convergence and precipitation did not change much over Hokkaido. Because the contribution of tropical cyclones to the total precipitation was at most 10%, the decrease in the number of tropical cyclones did not explain the predicted change in precipitation.

An Urgently Needed Repository for Planetary Atmospheric Model Output

Whitepaper #505 submitted to the Planetary Science and Astrobiology Decadal Survey 2023-2032. Topics: other: Repository for model output; other science themes: Processes on planetary bodies with atmospheres; theory, computation, and modeling

How Groundwater Level can Predict Under the Effect of Climate Change by Using Artificial Neural Networks of NARX

The phenomenon of climate change in recent years has led to significant changes in climatic elements and as a result the status of surface and groundwater resources, especially in arid and semi-arid regions, this issue has sometimes caused a significant decline in groundwater resources. In this paper, the effects of climate change on the status of groundwater resources in Marvdasht plain have been investigated. Water supply of different parts of this region is highly dependent on groundwater resources and therefore the study of groundwater changes in future periods is important in the development of this plain and the management of its water resources. In order to evaluate the effects of climate change, the output of atmospheric circulation models (GCM) has been used. Then, in order to adapt the output scale of these models to the scale required by local studies of climate change, precipitation and temperature data have been downscaled by LARS-WG model. Downscaled information was used to determine the amount of feed and drainage of the aquifer in future periods. To investigate changes in groundwater levels at different stages, a neural network dynamic model has been developed in MATLAB software environment. It is also possible to study and compare other points using other scenarios and mathematical modeling. The results of the study, assuming the current state of development in the region, indicate a downward trend in the volume of the aquifer due to climate change and its effects on resources and uses of the study area. The results also introduce Scenario A2 as the most critical scenario related to climate change, which also shows the largest aquifer decline in neural network modeling.

Comparison of evapotranspiration results under climate change RCP scenarios

Evapotranspiration after rainfall is the main component of the hydrological cycle that quantifies the amount of water lost. Since climate change directly affects the temperature and rainfall parameters, and the evapotranspiration rate is directly related to the air temperature, it is expected to have significant changes in the future. Therefore, in this research, the possible impacts of climate change on evapotranspiration changes under two RCP2.6 and RCP8.5 climate scenarios are investigated using statistical downscaling model (SDSM) and CanESM2 atmospheric general circulation model output for the future period 2030-2059. Reference evapotranspiration rate was compared with Hargreaves-Samani and Thornthwaite methods in monthly and annual periods for the base and future periods. The results showed a mean increase of annual maximum and minimum temperatures in both scenarios from 0.06 to 0.26 °C compared to the base period. The highest increase was in May by 2.61 °C under the RCP8.5 emission scenario. Overall, the trend of evapotranspiration has been increasing throughout the years. The evapotranspiration in the Hargreaves-Samani method in the RCP8.5 scenario had the highest mean change of 0.08 mm or 2.79%. Although annual changes were not perceptible, as evapotranspiration in the basin reaches its maximum in July, the changes increased by 0.45 mm/day in RCP8.5 scenario. The results of monthly survey can be used in surface and underground water resources management and watershed projects estimating the water needs of plants plus appropriate timing of useful irrigation.

Extending Limited In Situ Mountain Weather Observations to the Baseline Climate: A True Verification Case Study

The availability of in situ atmospheric observations decreases with elevation and topographic complexity. Data sets based on numerical atmospheric modeling, such as reanalysis data sets, represent an alternative source of information, but they often suffer from inaccuracies, e.g., due to insufficient spatial resolution. sDoG (statistical Downscaling for Glacierized mountain environments) is a reanalysis data postprocessing tool designed to extend short-term weather station data from high mountain sites to the baseline climate. In this study, sDoG is applied to ERA-Interim predictors to produce a retrospective forecast of daily air temperature at the Vernagtbach climate monitoring site (2640 MSL) in the Central European Alps. First, sDoG is trained and cross-validated using observations from 2002 to 2012 (cross-validation period). Then, the sDoG retrospective forecast and its cross-validation-based uncertainty estimates are evaluated for the period 1979–2001 (hereafter referred to as the true evaluation period). We demonstrate the ability of sDoG to model air temperature in the true evaluation period for different temporal scales: day-to-day variations, year-to-year and season-to-season variations, and the 23-year mean seasonal cycle. sDoG adds significant value over a selection of reference data sets available for the site at different spatial resolutions, including state-of-the-art global and regional reanalysis data sets, output by a regional climate model, and an observation-based gridded product. However, we identify limitations of sDoG in modeling summer air temperature variations particularly evident in the first part of the true evaluation period. This is most probably related to changes of the microclimate around the Vernagtbach climate monitoring site that violate the stationarity assumption underlying sDoG. When comparing the performance of the considered reference data sets, we cannot demonstrate added value of the higher resolution data sets over the data sets with lower spatial resolution. For example, the global reanalyses ERA5 (31 km resolution) and ERA-Interim (80 km resolution) both clearly outperform the higher resolution data sets ERA5-Land (9 km resolution), UERRA HARMONIE (11 km resolution), and UERRA MESCAN-SURFEX (5.5 km resolution). Performance differences among ERA5 and ERA-Interim, by contrast, are comparably small. Our study highlights the importance of station-scale uncertainty assessments of atmospheric numerical model output and downscaling products for high mountain areas both for data users and model developers.

Spatial-Temporal Characteristics of Past and Projected Climate Over Dairy Production Zones: A Case of Nandi County, Kenya

Climate change is regarded as a significant global environmental threat. This study assesses downscaled projections of climate change over dairy production regions (Nandi County) of Kenya using Rossby Centre Regional Atmospheric Model (RCA4) outputs driven by the eight (8) coordinated regional Downscaling Experiment (CORDEX) models. Climate baseline period (1971-2000) was used to evaluate CORDEX model performance against different sources of precipitation and temperature observations. Graphical and statistical approaches which correlation; Mann-Kendall test and nonparametric Sen’s method were used to assess the trends in both past and future climate. Spatial analysis involved mapping of climate variables. Assessment of the skill of CORDEX models shows significant bias in the individual models in simulating precipitation. However, maximum and minimum temperatures performed well based on both individual and ensemble based outputs. CORDEX model outputs were comparable to observations, and either overestimated or underestimated the climate. Past and projected precipitation remains bimodal and highly variable (increasing/decreasing) in both space and time. Positive change between baseline and projected temperatures were noted for RCP45 and RCP85. As a response to the effects of climate variability and change, adoption of climate smart agricultural technologies is necessary to ensure that smallholder farmers put adequate measures to adapt and mitigate impact of climate change Keywords: Precipitation; temperature; climate; projections; CORDEX DOI: 10.7176/JEES/10-6-06 Publication date: June 30 th 2020

Forcing ocean model with atmospheric model outputs to simulate storm surge in the Bangladesh coast

Tropical cyclones are devastating hazards and have been a major problem for the coastal population of Bangladesh. Among the advancements in atmospheric and oceanic prediction, accurate forecasting of storm surges is of specific interest due to their great potential to inflict loss of life and property. For decades, the numerical model based storm surge prediction systems have been an important tool to reduce the loss of human lives and property damage. In order to improve the accuracy in predicting storm surge and coastal inundation, recent model development efforts tended to include more modeling components, such as meteorology model and surface wave model in storm surge modeling. In this study, we used the outputs of an atmospheric model to force the ocean model for simulating storm surges in the Bay of Bengal with particular focus on the Bangladesh coast. The ability of the modeling system was investigated simulating water levels in the Bangladesh coast of two tropical cyclones Sidr (2007) and Aila (2009). The effectiveness of the model was verified through comparing the obtained computational outputs against tide gauge data. The cyclone tracks and intensities reproduced by the atmospheric model were reasonable, though the model had a tendency to overestimate the cyclone intensity during peaks and also close to coast. The water levels are reproduced fairly well by the ocean model, although errors still exist. The root mean square errors in water level at different gauges range from 0.277 to 0.419 m with coefficient of correlation (R2) between 0.64 and 0.97 in case of Sidr and 0.209–0.581 m with R2 0.62 to 0.98 for Aila. The overall coupled modeling system is found to be useful with reasonable accuracy and precision, though there are spaces for improvement. Higher-resolution modeling approaches are recommended to gain more skills.

Cloud Cover over the Arabian Peninsula from Global Remote Sensing and Reanalysis Products

Abstract Clouds – their coverage, nature, and interactions with the energy budget of the planet – represent one of the primary sources of uncertainty in future climate prediction. Despite the crucial role of desert clouds in the distribution of water and energy budgets, their climatology is still largely incomplete. With arid regions projected to become dryer under global warming conditions, understanding the characteristics of their cloud cover can provide critical insights. In this work, cloud coverage was investigated over one of the Earth's most arid regions – the Arabian Peninsula. Four total cloud cover (TCC) products, namely the International Satellite Cloud Climatology Project H (ISCCP), the CM SAF Cloud, Albedo and Surface Radiation AVHRR 2 (CLARA) satellite datasets, the National Centers for Environmental Prediction – National Center for Atmospheric Research (NCEP–NCAR) Reanalysis (R-2) (NCEP) and the ECMWF Interim Re-Analysis (ERA) reanalyses, were used to construct a climatology of desert clouds over the peninsula between 1984 and 2009, accounting for the different products' uncertainties and limitations. Satellite retrievals and reanalysis fields were first validated against ground observations from the United Arab Emirates, for which homogeneity assessments were conducted. The validation was done using statistical indicators, including Normalized Root Mean Square Errors (nRMSE), relative biases (rBIAS), and correlation coefficients, on monthly and seasonal scales. The ISCCP dataset resulted in the highest correlations with the ground observations (overall 0.38) and the lowest nRMSE (overall 0.54), while CLARA had the lowest rBIAS (overall 0.02). All products showed discrepancies when compared to the ground observations, both annually and on a seasonal basis. When extended to the entire Arabian Peninsula, the satellite and reanalysis products showed decreasing spring and increasing summer (except for ISCCP) TCC evolution across the region. At inter-annual scales, the TCC over the peninsula showed a significant discontinuity in 1998. This shift could be linked to the forcing of the El Nino Southern Oscillation on water vapor transport over the region, as well as to documented artifacts in satellite retrievals and model outputs. These discrepancies are indicative of a need for detailed assessments to be made of the uncertainties existing in TCC data for the Arabian Peninsula. Plain Language Summary Clouds represent a primary source of uncertainty in future climate predictions. This is particularly pronounced in dryland clouds, given their sporadic and intermittent nature. With global warming predicted to enhance aridification trends in terrestrial arid zones, historical cloudiness trends over arid and hyper arid regions are very important – yet their climatology to date is still largely incomplete. The quality of observations and/or model outputs are an important consideration, making validation and intercomparison assessments imperative. This work investigated cloud cover over the Arabian Peninsula, one of the most arid regions on the Earth. Four cloud cover datasets were used, derived from satellite measurements and atmospheric reanalysis model outputs. The selected study period was between 1984 and 2009. The four datasets were initially compared with observations taken at ground stations in the United Arab Emirates. The four products were then studied over the entire Arabian Peninsula. Results showed that cloud cover displayed marked seasonal characteristics and large inter-annual temporal evolution. An abrupt change in regional cloud cover time series was found to occur in 1998. This could be attributed to the forcing of the El Nino Southern Oscillation, although significant documented uncertainties in satellite products over this time span call for deeper investigations of this causal relation.

Evaluation of the relative contribution of meteorological and oceanic forces to the drift of ice islands offshore Newfoundland

AbstractOn 29 April 2015, four beacons were deployed onto an ice island in the Strait of Belle Isle to record positional data. The ice island later broke up into many fragments, four of which were tracked by the beacons. The relative influences of wind drag, current drag, Coriolis force, sea surface height gradient and sea-ice force on the drift of the tracked ice island fragments were analyzed. Using atmospheric and oceanic model outputs, the sea-ice force was calculated as the residual of the fragments' net forces and the sum of all other forces. This was compared against the force obtained through ice concentration-dependent relationships when sea ice was present. The sea-ice forces calculated from the residual approach and concentration-dependent relationships were significant only when sea ice was present at medium-high concentrations in the vicinity of the ice island fragments. The forces from ocean currents and sea surface tilt contributed the most to the drift of the ice island fragments. Wind, however, played a minimal role in the total force governing the drift of the four ice island fragments, and Coriolis force was significant when the fragments were drifting at higher speeds.