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A framework to quantify uncertainty in simulations of oil transport in the ocean
A framework to quantify uncertainty in simulations of oil transport in the ocean
An uncertainty quantification framework is developed for the DeepC Oil Model based on a nonintrusive polynomial chaos method. This allows the model's output to be presented in a probabilistic framework so that the model's predictions reflect the uncertainty in the model's input data. The new capability is illustrated by simulating the far-field dispersal of oil in a Deepwater Horizon blowout scenario. The uncertain input consisted of ocean current and oil droplet size data and the main model output analyzed is the ensuing oil concentration in the Gulf of Mexico. A 1331 member ensemble was used to construct a surrogate for the model which was then mined for statistical information. The mean and standard deviations in the oil concentration were calculated for up to 30 days, and the total contribution of each input parameter to the model's uncertainty was quantified at different depths. Also, probability density functions of oil concentration were constructed by sampling the surrogate and used to elaborate probabilistic hazard maps of oil impact. The performance of the surrogate was constantly monitored in order to demarcate the spacetime zones where its estimates are reliable., Keywords: deep-water, Dispersion, droplet breakup, gas blowouts, gulf-of-mexico, numerical-model, predictions, sea, subsea oil, water-horizon spill, Publication Note: The publisher’s version of record is available at http://www.dx.doi.org/10.1002/2015JC011311
An Assessment of Multimodel Simulations for the Variability of Western North Pacific Tropical Cyclones and Its Association with ENSO
An Assessment of Multimodel Simulations for the Variability of Western North Pacific Tropical Cyclones and Its Association with ENSO
An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Nino-Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation. Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Nino-namely, eastern Pacific (EP) and central Pacific (CP) El Nino-and weaker activity during La Nina. However, none of the models capture the differences in TC activity between EP and CP El Nino as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Nino events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Nino., Keywords: climate-change, el-nino, events, general-circulation models, high-resolution, interannual variability, precipitation, seasonal predictions, temperature, typhoon tracks, Publication Note: The publisher’s version of record is available at http://www.dx.doi.org/10.1175/JCLI-D-15-0720.1
Analysis methods for characterizing salinity variability from multivariate time series          applied to the Apalachicola Bay estuary
Analysis methods for characterizing salinity variability from multivariate time series applied to the Apalachicola Bay estuary
Statistical analysis methods are developed to quantify the impacts of multiple forcing variables on the hydrographic variability within an estuary instrumented with an enduring observational system. The methods are applied to characterize the salinity variability within Apalachicola Bay, a shallow multiple-inlet estuary along the northeastern Gulf of Mexico coast. Thirteen-year multivariate time series collected by the National Estuary Research Reserve at three locations within the bay are analyzed to determine how the estuary responds to variations in external forcing mechanisms, such as freshwater discharge, precipitation, tides and local winds, at multiple time scales. The analysis methods are used to characterize the estuarine variability under differing flow regimes of the Apalachicola River, a managed waterway, with particular focus on extreme events and scales of variability that are critical to local ecosystems. Multivariate statistical models are applied that describe the salinity response to winds from multiple directions, river flow, and precipitation at daily, weekly, and monthly time scales to understand the response of the estuary under different climate regimes. Results show that the salinity is particularly sensitive to river discharge and wind magnitude and direction, with local precipitation being largely unimportant. Applying statistical analyses with conditional sampling quantifies how the likelihoods of high salinity and long duration high salinity events, conditions of critical importance to estuarine organisms, change given the state of the river flow. Intraday salinity range is shown to be negatively correlated with the salinity, and correlated with river discharge rate., Keywords: Estuaries, Regression analysis, Statistical techniques, Time series, Oceanic variability, Apalachicola Bay, Note: © Copyright [2012] American Meteorological Society (AMS), Citation: Morey, Steven L., Dmitry S. Dukhovskoy, 2012: Analysis Methods for Characterizing Salinity Variability from Multivariate Time Series Applied to the Apalachicola Bay Estuary. J. Atmos. Oceanic Technol., 29, 613–628. http://dx.doi.org/10.1175/JTECH-D-11-00136.1
Applying Automated Underway Ship Observations to Numerical Model Evaluation
Applying Automated Underway Ship Observations to Numerical Model Evaluation
Numerical models are used widely in the oceanic and atmospheric sciences to estimate and forecast conditions in the marine environment. Herein the application of in situ observations collected by automated instrumentation on ships at sampling rates <= 5 min is demonstrated as a means to evaluate numerical model analyses. Specific case studies use near-surface ocean observations collected by a merchant vessel, an ocean racing yacht, and select research vessels to evaluate various ocean analyses from the Hybrid Coordinate Ocean Model (HYCOM). Although some specific differences are identified between the observations and numerical model analyses, the purpose of these comparisons is to demonstrate the value of high-sampling-rate in situ observations collected on ships for numerical model evaluation., Keywords: Automatic weather stations, Circulation, coordinate ocean model, forecast model, gulf-of-mexico, In situ atmospheric observations, In situ oceanic observations, Model evaluation/performance, Models and modeling, Observational techniques and algorithms, Ocean models, persian-gulf, prediction, products, Ship observations, simulations, stream, variability, Publication Note: The publisher’s version of record is available at http://www.dx.doi.org/10.1175/JTECH-D-15-0052.1
Assessing Crop Yield Simulations Driven By The Narccap Regional Climate Models In The Southeast United States
Assessing Crop Yield Simulations Driven By The Narccap Regional Climate Models In The Southeast United States
A set of the North American Regional Climate Change Assessment Program (NARCCAP) regional climate models is used in crop modeling systems to assess economically valuable agricultural production in the southeast United States, where weather/climate exerts strong impact on agriculture. The maize/peanut/ cotton yield amounts for the period of 1981-2003 are obtained in a regularly gridded (similar to 20km) southeast U.S. using (a) observed, (b) a reanalysis, and (c) the NARCCAP Phase I multimodel data set. It is shown that the regional-climate model-driven crop yield amounts are better simulated than the reanalysis-driven ones. Multimodel ensemble methods are then adopted to examine their usefulness in improving the simulation of regional crop yield amounts and are compared to each other. The bias-corrected or weighted composite methods combine the crop yield ensemble members better than the simple compositemethod. In general, the weighted ensemble crop yield simulations match marginally better with the observed-weather-driven yields compared to those of the other ensemble methods., Keywords: system, prediction, uncertainties, circulation model, demeter, ensembles, forecasts, maize yield, soybean model, Publication Note: The publisher's version of record is available at https://doi.org/10.1002/2016JD025576
California reanalysis downscaling at 10 km using an ocean-atmosphere coupled regional model system
California reanalysis downscaling at 10 km using an ocean-atmosphere coupled regional model system
A fully coupled regional downscaling system for both the Regional Spectral Model (RSM) for atmosphere and the Regional Ocean Modeling System (ROMS) for the ocean was developed for the purpose of downscaling observed analysis or global model outputs. The two models share the same grid and resolution with efficient parallelization through the use of dual message passing interfaces. Coupled downscaling was performed using historical Simple Ocean Data Assimilation (SODA) oceanic reanalysis and NCEP/DOE (R-2) atmospheric reanalysis in order to study the impact of coupling on the regional scale atmospheric analysis. The results were subsequently compared with the uncoupled downscaling forced by the prescribed observed sea surface temperature (SST). An evaluation of the SST and ocean current from the coupled experiment yielded realistic small-scale oceanic features that are nearly absent in the oceanic reanalysis. Upwelling over the California coast is well resolved and comparable to findings obtained from high-resolution observations. The coupling impact on the atmospheric circulation mainly modulates the near surface atmospheric variables when compared to the simulation conducted without coupling. The duration of the Catalina Eddy detected in the coupled experiment increased by about 6.5% when compared to that in the uncoupled experiment. The offshore land breeze is enhanced by about 10%, whereas the change in the onshore sea breeze is very small during the summer., Keywords: Regional climate, climate change, coupled model, reanalysis, upwelling, regional ocean model, Note: Published in The Journal of Geophysical Research, Vol. 117, D12118, 16 pp., 2012, doi:10.1029/2011JD017372, Citation: Li, H., M. Kanamitsu, and S.-Y. Hong (2012), California reanalysis downscaling at 10 km using an ocean-atmosphere coupled regional model system, J. Geophys. Res., 117, D12118, doi:10.1029/2011JD017372.
Characterizing the onset and demise of the Indian summer monsoon
Characterizing the onset and demise of the Indian summer monsoon
An objective index of the onset and demise of the Indian summer monsoon (ISM) is introduced. This index has the advantage of simplicity by using only one variable, which is the spatially averaged all-India rainfall, a reliably observed quantity for more than a century. The proposed onset index is shown to be insensitive to all historic false onsets. By definition, now the seasonal mean rainfall anomalies become a function of variations in onset and demise dates, rendering their monitoring to be very meaningful. This new index provides a comprehensive representation of the seasonal evolution of the ISM by capturing the corresponding changes in large-scale dynamic and thermodynamic variables. We also show that the interannual variability of the onset date of the ISM is associated with El Nino-Southern Oscillation (ENSO) with early (late) onsets preceded by cold (warm) ENSO., Keywords: definition, Dynamics, Evolution, interannual variability, ocean, plateau, rainfall, season, sensitivity, south asian monsoon, Publication Note: The publisher’s version of record is available at http://www.dx.doi.org/10.1002/2016GL068409
Climate Data Issues from an Oceanographic Remote Sensing Perspective
Climate Data Issues from an Oceanographic Remote Sensing Perspective
In this chapter we review several climatologically important variables with a history of observation from spaceborne platforms. These include sea surface temperature and wind vectors, altimetric estimates of sea surface height, energy and water vapor fluxes at the sea surface, precipitation over the ocean, and ocean color. We then discuss possible improvements in sampling for climate and climate change definition. Issues of consistency of different data sources, archiving and distribution of these types of data are discussed. The practical prospect of immediate international coordination through the concept of virtual constellations is discussed and applauded., Keywords: Oceanographic satellite sensors, Scatterometers, Altimeters, Microwave radiometers, Winds, Sea surface temperature, Air-sea fluxes, Sampling, Consist, Note: Published in Remote Sensing of the Changing Oceans, Ed. Tang, DanLing, Copyright 2011 Springer-Verlag. dx.doi.org/10.1007/978-3-642-16541-2, Citation: Katsaros, K. B., A. Bentamy, M. Bourassa, N. Ebuchi, J. Gower, W. T. Liu, and S. Vignudelli, 2011: Climate Data Issues from an Oceanographic Remote Sensing Perspective. Remote Sensing of the Changing Oceans, Tang, DanLing (Ed.), Springer-Verlag, doi:10.1007/978-3-642-16541-2_2.
Climate scenarios
Climate scenarios
The purpose of this document is to provide an informed opinion on future climate scenarios relevant to Florida. It offers a primer on Florida's vulnerabilities to climate variability and change. The document is an excellent compilation of diverse viewpoints on future climate projection. It implores the readers to be cognizant of the associated uncertainty but not to use that as an excuse for inaction in climate adaptation and mitigation. Experts in diverse fields employed in institutions across Florida have contributed to this document and provided candid and informed assessments of future climate variation and change. The uniqueness of this document is that it broadens the discussion of a rather restrictive sounding title like "climate scenarios" to involve experts in sociology, environmental law, and economics, in addition to oceanography and meteorology. The earth's climate is a very complex system. Climate is intimately interrelated to many components of the earth system. However, climate is not limited to these interactions alone. It also includes the modulation of these interactions by external factors such as anthropogenic influence (or interference), volcanic eruptions, changes in solar activity, and changing planetary factors like orbital eccentricity, obliquity, and precession. Against this backdrop of complexity, this paper has tried to distill the information that is relevant to Florida. It is well understood that climate has no borders, and yet we focus here on Florida because of the huge demand for locally applicable information on climate change and variation. Therefore, time and again throughout this paper the impact of remote climate variations and change on Florida is emphasized. Finally this document provides some initial suggestions to further fortify our understanding of the impact of global climate change on Florida. The caveat however, is that these fledgling suggestions will have to be further molded by a developing synergy between the federal, state, private stakeholders and university researchers., Note: published
Comparison of the ocean surface vector winds from atmospheric reanalysis and scatterometer-based wind products over the Nordic Seas and the northern North Atlantic and their application for ocean modeling
Comparison of the ocean surface vector winds from atmospheric reanalysis and scatterometer-based wind products over the Nordic Seas and the northern North Atlantic and their application for ocean modeling
Ocean surface vector wind fields from reanalysis data sets and scatterometer-derived gridded products are analyzed over the Nordic Seas and the northern North Atlantic for the time period from 2000 to 2009. The data sets include the National Center for Environmental Prediction Reanalysis 2 (NCEPR2), Climate Forecast System Reanalysis (CFSR), Arctic System Reanalysis (ASR), Cross-Calibrated Multiplatform (CCMP) wind product version 1.1 and recently released version 2.0, and QuikSCAT. The goal of the study is to assess discrepancies across the wind vector fields in the data sets and demonstrate possible implications of these differences for ocean modeling. Large-scale and mesoscale characteristics of winds are compared at interannual, seasonal, and synoptic timescales. A cyclone tracking methodology is developed and applied to the wind fields to compare cyclone characteristics in the data sets. Additionally, the winds are evaluated against observations collected from meteorological buoys deployed in the Iceland and Irminger Seas. The agreement among the wind fields is better for longer time and larger spatial scales. The discrepancies are clearly apparent for synoptic timescales and mesoscales. CCMP, ASR, and CFSR show the closest overall agreement with each other. Substantial biases are found in the NCEPR2 winds. Numerical sensitivity experiments are conducted with a coupled ice-ocean model forced by different wind fields. The experiments demonstrate differences in the net surface heat fluxes during storms. In the experiment forced by NCEPR2 winds, there are discrepancies in the large-scale wind-driven ocean dynamics compared to the other experiments., Keywords: Ocean surface winds, Scatterometer winds, Atmospheric reanalysis, Northern North Atlantic, Nordic Seas, Sensitivity of numerical simulation, Publication Note: The publisher's version of record for this article is available at https://doi.org/10.1002/2016JC012453., Preferred Citation: Dukhovskoy, D. S., Bourassa, M. A., Peterson, G. N., Steffen, J. (2017) "Comparison of the ocean surface vector winds from atmospheric reanalysis and scatterometer-based wind products over the Nordic Seas and the northern North Atlantic and their application for ocean modeling." Journal of Geophysical Research: Oceans DOI 10.1002/2016JC012453
Contribution of Monthly and Regional Rainfall to the Strength of Indian Summer Monsoon
Contribution of Monthly and Regional Rainfall to the Strength of Indian Summer Monsoon
Indian summer monsoon rainfall (ISMR; June September) has both temporal and spatial variability causing floods and droughts in different seasons and locations, leading to a strong or weak monsoon. Here, the authors present the contribution of all-India monthly, seasonal, and regional rainfall to the ISMR, with an emphasis on the strong and weak monsoons. Here, regional rainfall is restricted to the seasonal rainfall over four regions defined by the India Meteorological Department (IMD) primarily for the purpose of forecasting regional rainfall: northwest India (NWI), northeast India (NEI), central India (CI), and south peninsula India (SPIN). In this study, two rainfall datasets provided by IMD are used: 1) all-India monthly and seasonal (June September) rainfall series for the entire Indian subcontinent as well as seasonal rainfall series for the four homogeneous regions for the period 1901-2013 and 2) the latest daily gridded rainfall data for the period 1951-2014, which is used for assessment at the extent to which the four regions are appropriate for the intended purpose. Rainfall during July August contributes the most to the total seasonal rainfall, regardless of whether it is a strong or weak monsoon. Although NEI has the maximum area-weighted rainfall, its contribution is the least toward determining a strong or weak monsoon. It is the rainfall in the remaining three regions (NWI, CI, and SPIN) that controls whether an ISMR is strong or weak. Compared to monthly rainfall, regional rainfall dominates the strong or weak rainfall periods., Keywords: enso, indexes, interannual variability, ocean, Publication Note: The publisher’s version of record is available at http://www.dx.doi.org/10.1175/MWR-D-15-0318.1
Downscaling Future Climate Change Projections Over Puerto Rico Using A Non-hydrostatic Atmospheric Model
Downscaling Future Climate Change Projections Over Puerto Rico Using A Non-hydrostatic Atmospheric Model
We present results from 20-year "high-resolution" regional climate model simulations of precipitation change for the sub-tropical island of Puerto Rico. The Japanese Meteorological Agency Non-Hydrostatic Model (NHM) operating at a 2-km grid resolution is nested inside the Regional Spectral Model (RSM) at 10-km grid resolution, which in turn is forced at the lateral boundaries by the Community Climate System Model (CCSM4). At this resolution, the climate change experiment allows for deep convection in model integrations, which is an important consideration for sub-tropical regions in general, and on islands with steep precipitation gradients in particular that strongly influence local ecological processes and the provision of ecosystem services. Projected precipitation change for this region of the Caribbean is simulated for the mid-twenty-first century (2041-2060) under the RCP8.5 climate-forcing scenario relative to the late twentieth century (1986-2005). The results show that by the mid-twenty-first century, there is an overall rainfall reduction over the island for all seasons compared to the recent climate but with diminished mid-summer drought (MSD) in the northwestern parts of the island. Importantly, extreme rainfall events on sub-daily and daily time scales also become slightly less frequent in the projected mid-twenty-first-century climate over most regions of the island., Keywords: united-states, cmip5, precipitation, simulations, prediction, surfaces, weather, Publication Note: The publisher's version of record is available at https://doi.org/10.1007/s10584-017-2130-x
Dynamic downscaling of the North American Monsoon with the NCEP-Scripps Regional          Spectral Model from the NCEP CFS global model
Dynamic downscaling of the North American Monsoon with the NCEP-Scripps Regional Spectral Model from the NCEP CFS global model
The June-September (JJAS) 2000-2007 NCEP coupled Climate Forecasting System (CFS) global hindcasts are downscaled over the North and South American continents with the NCEP-Scripps Regional Spectral Model (RSM) with anomaly nesting (AN) and without bias correction (control). A diagnosis of the North American Monsoon (NAM) in CFS and RSM hindcasts is presented here. RSM reduces errors caused by coarse resolution, but is unable to address larger scale CFS errors even with bias correction. CFS has relatively weak Great Plains and Gulf of California low-level jets. Low-level jets are strengthened from downscaling, especially after AN bias correction. The RSM NAM hydroclimate shares similar flaws with CFS with problematic diurnal and seasonal variability. Flaws in both diurnal and monthly variability are forced by erroneous convection-forced divergence outside the monsoon core region in eastern and southern Mexico. NCEP Reanalysis shows significant seasonal variability errors, and AN shows little improvement in regional scale flow errors. Our results suggest extreme caution must be taken when the correction is applied relative to reanalyses. Analysis also shows North American Regional Reanalysis NAM seasonal variability has benefited from precipitation data assimilation, but many questions remain concerning NARR's representation of NAM., Keywords: Monsoons, Anomalies, Spectral analysis/models/distribution, Regional models, Seasonal variability, Note: © Copyright [2011] American Meteorological Society., Citation: Chan, S. C., and V. Misra (2011), Dynamic downscaling of the North American Monsoon with the NCEP-Scripps Regional Spectral Model from the NCEP CFS global model, J. Climate, doi:10.1175/2010JCLI3593.1.
Estimation Of Net Surface Radiation Using Eddy Flux Tower Data Over A Tropical Mangrove Forest Of Sundarban, West Bengal
Estimation Of Net Surface Radiation Using Eddy Flux Tower Data Over A Tropical Mangrove Forest Of Sundarban, West Bengal
In this study, net surface radiation (R-n) was estimated using artificial neural network (ANN) and Linear Model (LM). Then, estimated R-n with both the models (ANN and LM) were compared with measured R-n from eddy covariance (EC) flux tower. The routinely measured meteorological variables namely air temperature, relative humidity and wind velocity were used as input to the ANN and global solar radiation as input to the LM. All the input data are from the EC flux tower. Sensitivity analysis of ANN with all the meteorological variables is carried out by excluding one by one meteorological variable. The validation results demonstrated that, ANN and LM estimated R-n values were in good agreement with the measured values, with root mean square error (RMSE) varying between 21.63 W/m(2) and 34.94 W/m(2), mean absolute error (MAE) between 17.93 W/m2 and 22.28 W/m(2) and coefficient of residual mass (CRM) between -0.007 and -0.04 respectively. Further we have computed modelling efficiency (0.97 for ANN and 0.99 for LM) and coefficient of determination (R-2 = 0.97 for ANN and 0.99 for LM) for both the models. Even though both the models could predict R-n successfully, ANN was better in terms of minimum number of routinely measured meteorological variables as input. The results of the ANN sensitivity analysis indicated that air temperatuere is the more important parameter followed by relative humidity, wind speed and wind direction., Keywords: artificial neural network, artificial neural-networks, china, eddy flux tower, energy-balance archive, global solar-radiation, land, linear model, models, net surface radiation, photosynthetically active radiation, Turkey, Publication Note: The publisher's version of record is available at https://doi.org/10.15233/gfz.2016.33.5
Evaluation of dynamically downscaled reanalysis precipitation data for hydrological          application in the southeast United States
Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application in the southeast United States
Skillful and reliable precipitation data is essential for seasonal hydrologic forecasting, and generation of hydrological data. Though output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 basins located in the southeast United States (U.S.) that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global (NCEP R2 and ERA40) at their native resolution; dynamically downscaled; synthetically generated; bias-corrected, dynamically downscaled and bias-corrected global reanalysis. Our study indicates that over the 28 watersheds in the southeast U.S., the simulated hydrological response to the biascorrected dynamically downscaled data is superior. In comparison to synthetically generated meteorological forcing, the dynamically downscaled data result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling, although resource intensive, is better suited for hydrological simulation in the southeast U.S., Keywords: Reanalysis, Bias correction, rainfall runoff model, Note: Submitted for publication in Hydrological Processes. A pre-peer reviewed version of the article is accessible at http://coaps.fsu.edu/bibliography/papers/bastola/2012jawra.pdf, Citation: Bastola, S., and V. Misra, 2012: Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application in the southeast United States. Hydrological Processes, (submitted). Submitted version accessible at http://coaps.fsu.edu/bibliography/papers/bastola/2012jawra.pdf
Evolution of Land Surface Air Temperature Trend
Evolution of Land Surface Air Temperature Trend
The global climate has been experiencing significant warming at an unprecedented pace in the past century1, 2. This warming is spatially and temporally non-uniform, and one needs to understand its evolution in order to better evaluate its potential societal and economic impact. In this paper, the evolution of global land surface temperature trend in the last century is diagnosed using the spatial–temporally multidimensional ensemble empirical mode decomposition method3. We find that the noticeable warming (>0.5 K) started sporadically over the global land and accelerated until around 1980. Both the warming rate and spatial structure have changed little since. The fastest warming in recent decades (>0.4 K/decade) occurred in northern midlatitudes. From a zonal average perspective, noticeable warming (>0.2 K since 1900) first took place in the subtropical and subpolar regions of the Northern Hemisphere, followed by subtropical warming in the Southern Hemisphere. The two bands of warming in the Northern Hemisphere expanded from 1950 to 1985 and merged to cover the entire Northern Hemisphere., Keywords: climate change, land surface temperature, Note: This is a submitted version of the paper published by Nature Climate Change at DOI: 10.1038/nclimate2223, Citation: Ji, F., Wu, Z., Huang, J., & Chassignet, E., (2014). Evolution of land surface air temperature trend. Nature Climate Change. DOI: 10.1038/nclimate2223
Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets.
Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets.
In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders., Keywords: Adaptive and local data analysis, Data compression, Empirical orthogonal function, Fast algorithm, Multidimensional ensemble empirical mode decomposition, Principal component analysis, Publication Note: This NIH-funded author manuscript originally appeared in PubMed Central at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792406.
Frequency Content Of Sea Surface Height Variability From Internal Gravity Waves To Mesoscale Eddies
Frequency Content Of Sea Surface Height Variability From Internal Gravity Waves To Mesoscale Eddies
High horizontal-resolution (1/12: 5 degrees and 1/25 degrees) 41-layer global simulations of the HYbrid Coordinate Ocean Model (HYCOM), forced by both atmospheric fields and the astronomical tidal potential, are used to construct global maps of sea surface height (SSH) variability. The HYCOM output is separated into steric and nonsteric and into subtidal, diurnal, semidiurnal, and supertidal frequency bands. The model SSH output is compared to two data sets that offer some geographical coverage and that also cover a wide range of frequencies-a set of 351 tide gauges that measure full SSH and a set of 14 in situ vertical profilers from which steric SSH can be calculated. Three of the global maps are of interest in planning for the upcoming Surface Water and Ocean Topography (SWOT) two-dimensional swath altimeter mission: (1) maps of the total and (2) nonstationary internal tidal signal (the latter calculated after removing the stationary internal tidal signal via harmonic analysis), with an average variance of 1: 05 and 0: 43 cm(2), respectively, for the semidiurnal band, and (3) a map of the steric supertidal contributions, which are dominated by the internal gravity wave continuum, with an average variance of 0: 15 cm2. Stationary internal tides (which are predictable), nonstationary internal tides (which will be harder to predict), and nontidal internal gravity waves (which will be very difficult to predict) may all be important sources of high-frequency "noise" that could mask lower frequency phenomena in SSH measurements made by the SWOT mission., Keywords: spectra, wind, energy, accuracy, dissipation, general-circulation, global ocean, ocean circulation model, satellite altimetry, tide-gauge, Publication Note: The publisher's version of record is available at https://doi.org/10.1002/2016JC012331
Generation of an empirical soil moisture initialization and its potential impact on          subseasonal forecasting skill of continental precipitation and air temperature
Generation of an empirical soil moisture initialization and its potential impact on subseasonal forecasting skill of continental precipitation and air temperature
The effect of the PAR technique on the model soil moisture estimates is evaluated using the Global Soil Wetness Project Phase 2 (GSWP-2) multimodel analysis product (used as a proxy for global soil moisture observations) and actual in-situ observations from the state of Illinois. The results show that overall the PAR technique is effective; across most of the globe, the seasonal and anomaly variability of the model soil moisture estimates well reproduce the values of GSWP-2 in the top 1.5 m soil layer; by comparing to in-situ observations in Illinois, we find that the seasonal and anomaly soil moisture variability is also well represented deep into the soil. Therefore, in this study, we produce a new global soil moisture analysis dataset that can be used for many land surface studies (crop modeling, water resource management, soil erosion, etc.). Then, the contribution of the resulting soil moisture analysis (used as initial conditions) on air temperature and precipitation forecasts are investigated. For this, we follow the experimental set up of a model intercomparison study over the time period 1986-1995, the Global Land-Atmosphere Coupling Experiment second phase (GLACE-2), in which the FSU/COAPS climate model has participated. The results of the summertime air temperature forecasts show a significant increase in skill across most of the U.S. at short-term to subseasonal time scales. No increase in summertime precipitation forecasting skill is found at short-term to subseasonal time scales between 1986 and 1995, except for the anomalous drought year of 1988. We also analyze the forecasts of two extreme hydrological events, the 1988 U.S. Drought and the 1993 U.S. flood. In general, the comparison of these two extreme hydrological event forecasts shows greater improvement for the summertime of 1988 than that of 1993, suggesting that soil moisture contributes more to the development of a drought than a flood. This result is consistent with Dirmeyer and Brubaker [1999] and Weaver et al. [2009]. By analyzing the evaporative sources of these two extreme events using the back-trajectory methodology of Dirmeyer and Brubaker [1999], we find similar results as this latter paper; the soil moisture-precipitation feedback mechanism seems to play a greater role during the drought year of 1988 than the flood year of 1993. Finally, the accuracy of this soil moisture initialization depends upon the quality of the precipitation dataset that is assimilated. Because of the lack of observed precipitation at a high temporal resolution (3-hourly) for the study period (1986-1995), a reanalysis product is used for precipitation assimilation in this study. It is important to keep in mind that precipitation data in reanalysis sometimes differ significantly from observations since precipitation is often not assimilated into the reanalysis model. In order to investigate that aspect, a similar analysis to that we performed in this study could be done using the 3-hourly Tropical Rainfall Measuring Mission (TRMM) dataset available for a the time period 1998-present. Then, since the TRMM dataset is a fully observational dataset, we expect the soil moisture initialization to be improved over that obtained in this study, which, in turn, may further increase the forecast skill., Keywords: Soil moisture, land-atmosphere interactions, climate forecasts, Submitted Note: A Dissertation submitted to the Department of Meteorology in partial fulfillment of the requirements for the degree of Doctor of Philosophy., Degree Awarded: Spring Semester, 2010., Date of Defense: January 26, 2010.
Generation of mesoscale eddies in the lee of the Hawaiian Islands
Generation of mesoscale eddies in the lee of the Hawaiian Islands
The ocean west of the main Hawaiian Islands is characterized by enhanced eddy kinetic energy arising from the abundance of locally generated mesoscale eddies, most frequently in the area west of the island of Hawaii. Two mechanisms of eddy generation in the wake of an island are examined with numerical model experiments. The first, eddy generation and shedding by an oceanic flow around an oceanic barrier, requires the existence of strong westward flows to the north and south of the island of Hawaii. Model solutions show such westward flows and generation of eddies by these flows although the intensity of the eddies and the generation frequency are much lower than that derived from altimetry. As a result, these eddies contribute an insignificant amount of eddy kinetic energy in the region. The second, eddy generation and shedding by an atmospheric flow around an atmospheric barrier, is based on oceanic upwelling and downwelling induced by surface wind shear, effectively introducing sinks and sources to the ocean interior. Previous idealized modeling studies have shown that oceanic eddies can be generated by sufficiently strong forcing (source or sink), providing an explanation why eddy occurrences in the lee of the island of Hawaii coincide with periods of strong trade winds. Eddy generation characteristics in the model experiments are consistent with this mechanism in terms of time of occurrence, strength and the resulting eddy kinetic energy. Major discrepancies are in eddy propagation and therefore eddy distribution in the regional domain due to the complex nature of eddy-eddy interactions., Keywords: Hawaiian Islands, ocean circulation, oceanic eddies, Note: Copyright [2011] American Geophysical Union., Citation: Jia, Y., P.H.R. Calil, E.P. Chassignet, E.J. Metzger, J.T. Potemra, K.J. Richards, and A.J. Wallcraft (2011), Generation of mesoscale eddies in the lee of the Hawaiian Islands, J. Geophys. Res., 116, C11009, doi:10.1029/2011JC007305.

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