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Thomas M. Smith

Bio: Thomas M. Smith is an academic researcher from National Oceanic and Atmospheric Administration. The author has contributed to research in topic(s): Sea surface temperature & Precipitation. The author has an hindex of 34, co-authored 75 publication(s) receiving 20532 citation(s). Previous affiliations of Thomas M. Smith include University of Maryland, College Park.
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Journal ArticleDOI
Boyin Huang1, Chunying Liu, Viva Banzon1, Eric Freeman  +4 moreInstitutions (2)
Abstract: The NOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature (SST), version 2.0, dataset (DOISST v2.0) is a blend of in situ ship and buoy SSTs with satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR). DOISST v2.0 exhibited a cold bias in the Indian, South Pacific, and South Atlantic Oceans that is due to a lack of ingested drifting-buoy SSTs in the system, which resulted from a gradual data format change from the traditional alphanumeric codes (TAC) to the binary universal form for the representation of meteorological data (BUFR). The cold bias against Argo was about −0.14°C on global average and −0.28°C in the Indian Ocean from January 2016 to August 2019. We explored the reasons for these cold biases through six progressive experiments. These experiments showed that the cold biases can be effectively reduced by adjusting ship SSTs with available buoy SSTs, using the latest available ICOADS R3.0.2 derived from merging BUFR and TAC, as well as by including Argo observations above 5-m depth. The impact of using the satellite MetOp-B instead of NOAA-19 was notable for high-latitude oceans but small on global average, since their biases are adjusted using in situ SSTs. In addition, the warm SSTs in the Arctic were improved by applying a freezing point instead of regressed ice-SST proxy. This paper describes an upgraded version, DOISST v2.1, which addresses biases in v2.0. Overall, by updating v2.0 to v2.1, the biases are reduced to −0.07° and −0.14°C in the global ocean and Indian Ocean, respectively, when compared with independent Argo observations and are reduced to −0.04° and −0.08°C in the global ocean and Indian Ocean, respectively, when compared with dependent Argo observations. The difference against the Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) product is reduced from −0.09° to −0.01°C in the global oceans and from −0.20° to −0.04°C in the Indian Ocean.

30 citations


Journal ArticleDOI
Abstract: Warming sea-surface temperatures (SSTs) have implications for the climate-sensitive Caribbean region, including potential impacts on precipitation. SSTs have been shown to influence deep convection and rainfall, thus understanding the impacts of warming SSTs is important for predicting regional hydrometeorological conditions. This study investigates the long-term annual and seasonal trends in convection using the Galvez-Davison Index (GDI) for tropical convection from 1982–2020. The GDI is used to describe the type and potential for precipitation events characterized by sub-indices that represent heat and moisture availability, cool/warm mid-levels at 500 hPa, and subsidence inversion, which drive the regional Late, Early, and Dry Rainfall Seasons, respectively. Results show that regional SSTs are warming annually and per season, while regionally averaged GDI values are decreasing annually and for the Dry Season. Spatial analyses show the GDI demonstrates higher, statistically significant correlations with precipitation across the region than with sea-surface temperatures, annually and per season. Moreover, the GDI climatology results show that regional convection exhibits a bimodal pattern resembling the characteristic bimodal precipitation pattern experienced in many parts of the Caribbean and surrounding region. However, the drivers of these conditions need further investigation as SSTs continue to rise while the region experiences a drying trend.

2 citations


Journal ArticleDOI
Viva Banzon1, Thomas M. Smith1, Michael Steele2, Boyin Huang1  +1 moreInstitutions (2)
Abstract: Arctic sea surface temperatures (SSTs) are estimated mostly from satellite sea ice concentration (SIC) estimates. In regions with sea ice the SST is the temperature of open water or of the ...

31 citations


Journal ArticleDOI
TL;DR: The technique greatly enhances the performance of the satellite estimates, especially over mountain terrain, albeit with an accuracy inferior to that over low elevation areas, with implications for improving the utilization of satellite data in snow assessments and numerical weather prediction.
Abstract: The development of a snow depth product over North America is investigated by applying two-dimensional optimal interpolation to passive microwave satellite-derived and in-situ measured snow depth. At each snow-covered satellite footprint, the technique computes a snow depth increment as the weighted average of data increments, and updates the satellite-derived snow depth accordingly. Data increments are computed as the difference between the in-situ-measured and satellite snow depth at station locations surrounding the satellite footprint. Calculation of optimal weights is based on spatial lag autocorrelation of snow depth increments, modelled as functions of horizontal distance and elevation difference between pairs of observations. The technique is applied to Advanced Microwave Scanning Radiometer 2 (AMSR2) snow depth and in-situ snow depth obtained from the Global Historical Climatology Network. The results over North America during January–February 2017 indicate that the technique greatly enhances the performance of the satellite estimates, especially over mountain terrain, albeit with an accuracy inferior to that over low elevation areas. Moreover, the technique generates more accurate output compared to that from NOAA’s Global Forecast System, with implications for improving the utilization of satellite data in snow assessments and numerical weather prediction.

2 citations


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Journal ArticleDOI
Abstract: As part of seamless modeling strategy the ocean is increasingly becoming an important component of weather and climate forecasting systems across time scales. While the skill of sea surface temperatures (SSTs) at long-range forecasts is widely documented, few studies exist on the representation of evolving SST biases at shorter time-scales in a global coupled model. In this study, daily initialized runs of National Centre for Medium Range Weather Forecasting (NCMRWF) coupled model are analyzed over several seasons for up to two weeks of leadtimes. Analysis of source of SST biases at shorter time scales is carried out. It is found that the model captures well the spatial structure of the SSTs and their seasonal cycle. The SST correlations averaged over different regions are over 0.8 at day-14 leadtime, and RMSEs remain less than 0.6 °C over all regions except the south east equatorial Indian Ocean (SEEIO). SEEIO shows lowest of the correlations and highest of the biases among the regions considered. The difference in the skill of the model over different regions is found to be related to the representation of SST variability over the region. An assessment of the biases in different seasons suggests that the day-1 biases are related to the errors in the ocean state used to initialize the model. However, by day-14 increase in biases are seen over the regions where these are found to be related to the wind biases. Highly significant correlation ( r = − 0.39 ) is seen between winds and SST biases. Errors in winds also affect both the surface heat fluxes and the upper ocean mixing: wind biases are positively correlated to both latent heat fluxes and mixed layer depths (MLDs). It is found that the errors in atmospheric winds drive the biases in SSTs by influencing the upper ocean processes. An analysis of the relationship between latent heat fluxes and SSTs shows that the air-sea interactions are realistically represented in the model. This gives us confidence in attributing the SST biases to the upper ocean parameters. Contributions of different parameters to the SST biases are estimated using the heat budget of the upper ocean. It is shown that errors in LH fluxes and upper-ocean mixing explain large part of biases in SST tendencies. However, the errors in upper-ocean mixing explain much higher variance of biases in SST tendencies as compared to LH fluxes. This is found to be related to stronger dependence of SST tendencies to the MLD tendencies in the model. Identification of the sources of errors will help in our understanding of the biases at longer time scales and provides feedback for further model development.

Journal ArticleDOI
Abstract: Forest growth changes have been a matter of intense research efforts since the 1980s. Owing to the variety of their environmental causes – mainly atmospheric CO2 increase, atmospheric N deposition, changes in temperature and water availability, and their interactions – their interpretation has remained challenging. Recent isolated researches suggest further effects of neglected environmental factors, namely changes in the diffuse fraction of light, more efficient to photosynthesis, and galactic cosmic rays (GCR), both emphasized in this Discussion paper. With growing awareness of GCR influence on global cloudiness (the cosmoclimatologic theory by H. Svensmark), GCR may thus cause trends in diffuse-light, and distinguishing between their direct/indirect influences on forest growth remains uncertain. This link between cosmic rays and diffuse sunlight also forms an alternative explanation to the geological evidence of a negative correlation between GCR and atmospheric CO2 concentration over the past 500 Myr. After a careful scrutiny of this literature and of key contributions in the field, we draw research options to progress further in this attribution. These include i) observational strategies intending to build on differences in the spatio-temporal dynamics of environmental growth factors, ranging from quasi-experiments to meta-analyses, ii) simulation strategies intending to quantify environmental factor's effects based on process-based ecosystem modelling, in a context where progresses for accounting for diffuse-light fraction are ongoing. Also, the hunt for tree-ring based proxies of GCR may offer the perspective of testing the GCR hypothesis on fully coupled forest growth samples.


Journal ArticleDOI
Abstract: Polylepis tarapacana is the highest-elevation tree species worldwide growing between 4000 and 5000 m a.s.l. along the South American Altiplano. P. tarapacana is adapted to live in harsh conditions and has been widely used for drought and precipitation tree-ring based reconstructions. Here, we present a 400-year tree-ring width (TRW) chronology located in southern Peru (17oS; 69oW) at the northernmost limit of P. tarapacana tree species distribution. The objectives of this study are to assess tree growth sensitivity of a northern P. tarapacana population to (1) precipitation, temperature and El Nino Southern Oscillation (ENSO) variability; (2) to compare its growth variability and ENSO sensitivity with southern P. tarapacana forests. Our results showed that this TRW record is highly sensitive to the prior summer season (Nov-Jan) precipitation (i.e. positive correlation) when the South American Summer Monsoon (SASM) reaches its maximum intensity in this region. We also found a positive relationship with current year temperature that suggests that radial growth may be enhanced by warm, less cloudy, conditions during the year of formation. A strong positive relationship was found between el Nino 3.4 and tree growth variability during the current growing season, but negative during the previous growth period. Growth variability in our northern study site was in agreement with other populations that represent almost the full range of P. tarapacana latitudinal distribution (~ 18oS to 23oS). Towards the south of the P. tarapacana TRW network there was a decrease in the strength of the agreement of growth variability with our site,with the exception of higher correlation with the two southeastern sites. Similarly, the TRW chronologies recorded higher sensitivity to ENSO influences in the north and southeastern locations, which are wetter, than the drier southwestern sites . These patterns hold for the entire period, as well as for periods of high and low ENSO activity. Overall, P. tarapacana tree growth at the north of its distribution is mostly influenced by prior year moisture availability and current year temperature that are linked to large-scale climate patterns such as the SASM and ENSO, respectively.

Journal ArticleDOI
Laban Lameck Kebacho1Institutions (1)
Abstract: Previous studies reported an “abrupt regime shift” in the relationship between the East African short rains (EASR), Indian Ocean Dipole (IOD) and the El Nino–Southern Oscillation (ENSO) around 1982. Using observational datasets over the 1951–2018 period, the relationship between large-scale circulation anomalies and EASR before and after 1982 is investigated. Two physical processes that account for rainfall formation over East Africa (EA) are proposed. First, warming over the western Indian Ocean induces Rossby waves that trigger meridional wind convergence anomalies. These are linked with positive rainfall anomalies over EA. Second, strengthened lower level easterly winds over the equatorial Indian Ocean led to a surge of moisture flux flow toward EA, hence contributes to the formation of rainfall. Factors related to positive (negative) rainfall anomalies are of keen interest. They frequently occurred after (before) the regime shift of 1982 than prior to (after) the regime shift. For wet years, co-occurrence of El Nino and positive IOD events’ contribution is more comparable to pure positive IOD and El Nino events. For dry years, the co-occurrence of La Nina and negative IOD is quite similar to pure negative IOD events. This paper studies the major regime shifts of 1982 and 1997. The decadal variations around 1982 (1997) are linked with suppressed (enhanced) rainfall over EA. Therefore, the dynamics governing the relationship between winds, Indian, Pacific Ocean SST, and EASR varies on an interdecadal scale. Understanding the cause of this variability is needed to achieve an improved long-lead empirical rainfall predictions.

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Performance
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Author's H-index: 34

No. of papers from the Author in previous years
YearPapers
20213
20201
20191
20181
20173
20164