Observed Temperature Time Series Research Articles

Modelling water temperature dynamics for eelgrass (Zostera marina) areas in the nearshore Scotian Shelf

Water temperature is an important environmental factor for many ecological processes in coastal ecosystems. Here, we study water temperature dynamics at a set of study sites on the Atlantic coast of Nova Scotia where eelgrass beds are found. The central emphasis is to predict temperature on scales relevant to coastal ecosystem processes using a high-resolution nearshore oceanographic model based on the Finite Volume Community Ocean Model (FVCOM). The model predictions were evaluated against observed temperature time series at six sites for three years from 2017-2019; the evaluation indicates that the model was able to replicate the temperature variation on time scales from hours to seasonal. We also used various biologically tailored temperature metrics relevant to eelgrass condition, including mean seasonal values and variability, daily ranges, growing degree day (GDD), and warm events, to validate the model against time series observations to better understand the temperature regime at the study sites. Frequency resolved Willmott skill scores were >0.7, and the temperature metrics were well predicted with the exception of a bias in GDD at some of the shallow sites. The eelgrass sites have a wide range of temperature conditions. Mean water temperature in the summer differed by more than 7°C between the shallowest and the deepest sites, and the rate of heat accumulation was fastest at shallow sites which had ≥ 12 extreme warm events per year. While the amplitude of the temperature variations within the high frequency band (<48 hr) was greater in shallower sites, temperature changes on meteorological time scales (48 hr to 60 days) were coherent at all sites, suggesting the importance of coast-wide processes. The results of this study demonstrated that our high resolution numerical model captured biologically relevant temperature dynamics at different time scales and over a large spatial region, and yet still accurately predicted detailed temperature dynamics at specific nearshore sites. Thus, the model can provide important insights into coastal temperature dynamics that are potentially useful for conservation planning and understanding the implications of future change.

Chaos Theory Modelling for Temperature Time Series at Malaysian High Population Area during Dry Season

The aim of this study is to model the temperature time series at Malaysian high population area during dry season through chaos theory. The selected high population area is Shah Alam located in Selangor state of Malaysia. Chaos theory modelling is categorized into two parts namely analysis and prediction. Analysis by the phase space plot showed that the nature of the observed temperature time series is chaos. Hence, the time series is predicted via the chaotic model. Results from the chaotic model showed that the temperature time series is well predicted with Pearson correlation coefficient near to 1. The result is compared with the traditional method of autoregressive linear model. Based on the computed values of average absolute error, root mean squared error and Pearson correlation coefficient, the chaotic model is found better in predicting temperature time series at Shah Alam area during dry season. This indicates that the chaos theory is applicable for temperature time series at Malaysian high population area. This finding is expected to facilitate stakeholders such as Malaysian Meteorological Department and Department of Environment Malaysia in managing temperature and climate change problem.

Upscaling Heat Flow in Porous Media With Periodic Surface Temperature Fluctuation Using a One‐Dimensional Subordinated Heat Transfer Equation

AbstractThe one‐dimensional (1‐d), constant‐parameter heat transport equation (HTE) built upon Fourier's law proposed in 1822 has been widely used to model heat transport in soil, where the core question is how to fit the observed temperature time series without detailed geomedium heterogeneity being mapped in the model. To address this question, this study extended the classical HTE by adding one parameter (the time index) using the time subordination approach to capture the impact of random operational times spent by individual heat parcels on heat dynamics. This led to a parsimonious, 1‐d, Subordinated HTE (Sub‐HTE), which assumes the temporally non‐Fourier heat transport and contains the classical HTE as an end member, with analytical solutions aimed at upscaling local heat transport in heterogeneous porous media. To test this approach, temperature time‐series were collected in unsaturated soils built in the laboratory that were subjected to periodic surface temperature fluctuations. Model applications showed that, although the HTE captures the general shape of the observed temperature time series, the Sub‐HTE is a mild improvement of the HTE in characterizing the nuances of temperature amplitude attenuation (likely due to the small operational time at the intermediate/late time stages). Both Eulerian and Lagrangian frameworks were developed to interpret other dynamics in heat flow, including the early time temperature jump and phase advance. Parameter analyses also revealed that the thermal front velocity in the Sub‐HTE is a function of Darcy flux, the time index, and the system's thermal properties, while the thermal diffusivity (the only parameter related to thermal properties in the Sub‐HTE) can be scale independent. Therefore, the 1‐d Sub‐HTE can be a reliable generalization of the classical 1‐d HTE in upscaling heat transport in soil.

A Pilot Study Using Chaos Theory to Predict Temperature Time Series in Malaysian Semi Urban Area

Chaos theory draws more attention since it has been widely used in modeling various time series. Changes in temperature can cause bad effect on health and lead to death. Therefore, in this study, chaos theory was applied to the temperature time series. The temperature time series is observed hourly in one of Malaysiansemi urban area namely Tanjong Malim,located in the state of Perak. This pilot study begins by detecting the chaos nature in time series through phase space approach and Cao method. Next, the time series was predicted through the local approximation method, a method based on chaos theory. This study resulted that the nature of the observed temperature time series was chaos. Prediction through the local approximation method was success with correlation coefficient value 0.9138. This shows that there exist a strong relationship between the predicted and observed temperature time series. Therefore, chaos theorywas a good approach that can be used to determine the nature and predict temperature time series in the semi urban area. In implication, this findingwas expected to serve stakeholders such as Ministry of Higher Education, Meteorological Department as well as Department of Environment in temperature time series management.

Quality Control of Observed Temperature Time Series from the Korea Ocean Research Stations: Preliminary Application of Ocean Observation Initiative’s Approach and Its Limitation

Quality Control of Observed Temperature Time Series from the Korea Ocean Research Stations: Preliminary Application of Ocean Observation Initiative’s Approach and Its Limitation

Modeling temperature and pricing weather derivatives based on subordinate Ornstein-Uhlenbeck processes

In this paper we employ a time-changed Ornstein-Uhlenbeck (OU) process for modeling temperature and pricing weather derivatives, where the time change process is a Levy subordinator time changed by a deterministic clock with seasonal activity rate. The drift, diffusion volatility and jumps under the new model are all seasonal, which are supported by the observed temperature time series. An important advantage of our model is that we are able to derive the analytical pricing formulas for temperature futures and future options based on eigenfunction expansion technique. Our empirical study indicates the new model has the potential to capture the main features of temperature data better than the competing models.

Hydrodynamic modeling of Edmonton storm-water ponds

Computational models often are applied to simulate water quality for management of natural and constructed aquatic systems. As part of a larger study, a three-dimensional hydrodynamic and biogeochemical model was applied to three storm-water ponds in the City of Edmonton to examine relations between nutrients and eutrophication. This paper reports on the calibration and validation procedure of the hydrodynamic component of the model suite. It was found that correcting surface heat fluxes for atmospheric instability (defined as the difference between air and water temperature) led to greater latent and sensible heat loss (up to ~ 32% and ~ 34%, respectively) and resulted in the most accurate simulation of observed temperature time series (root-mean-square error, RMSE < 3.03 °C). The calibration also involved tuning of albedo and light attenuation coefficients, which were optimized by applying a global sensitivity analysis tool that showed the model was more sensitive to albedo. Global sensitivity analysis, can be applied to develop a resulting limited area, which is shown to be a valuable tool to optimize the calibration process.

Effect of gravity wave temperature fluctuations on homogeneous ice nucleation in the tropical tropopause layer

Abstract. The impact of high-frequency fluctuations of temperature on homogeneous nucleation of ice crystals in the vicinity of the tropical tropopause is investigated using a bin microphysics scheme for air parcels. The imposed temperature fluctuations come from measurements during isopycnic balloon flights near the tropical tropopause. The balloons collected data at high frequency, guaranteeing that gravity wave signals are well resolved.With the observed temperature time series, the numerical simulations with homogeneous freezing show a full range of ice number concentration (INC) as previously observed in the tropical upper troposphere. In particular, a low INC may be obtained if the gravity wave perturbations produce a non-persistent cooling rate (even with large magnitude) such that the absolute change in temperature remains small during nucleation. This result is explained analytically by a dependence of the INC on the absolute drop in temperature (and not on the cooling rate). This work suggests that homogeneous ice nucleation is not necessarily inconsistent with observations of low INCs.

Newest developments of ACMANT

Abstract. The seasonal cycle of radiation intensity often causes a marked seasonal cycle in the inhomogeneities (IHs) of observed temperature time series, since a substantial portion of them have direct or indirect connection to radiation changes in the micro-environment of the thermometer. Therefore the magnitudes of temperature IHs tend to be larger in summer than in winter. A new homogenisation method, the Adapted Caussinus – Mestre Algorithm for Networks of Temperature series (ACMANT) has recently been developed which treats appropriately the seasonal changes of IH-sizes in temperature time series. The performance of ACMANT was proved to be among the best methods (together with PRODIGE and MASH) in the efficiency test procedure of COST ES0601 project. A further improved version of the ACMANT is described in this paper. In the new version the ANOVA procedure is applied for correcting inhomogeneities, and with this change the iterations applied in the earlier version have become unnecessary. Some other modifications have also been made, from which the most important one is the new way for estimating the timings of IHs. With these modifications the efficiency of the ACMANT has become even higher, therefore its use is strongly recommended when networks of monthly temperature series from mid- or high geographical latitudes are subjected to homogenisation. The paper presents the main properties and the operation of the new ACMANT.

Adapted Caussinus-Mestre Algorithm for Networks of Temperature series (ACMANT)

Any change in technical or environmental conditions of observations may result in bias from the precise values of observed climatic variables. The common name of these biases is inhomogeneity (IH). IHs usually appear in a form of sudden shift or gradual trends in the time series of any variable, and the timing of the shift indicates the date of change in the conditions of observation. The seasonal cycle of radiation intensity often causes marked seasonal cycle in the IHs of observed temperature time series, since a substantial portion of them has direct or indirect connection to radiation changes in the micro-environment of the thermometer. Therefore the magnitudes of temperature IHs tend to be larger in summer than in winter. A new homogenisation method (ACMANT) has recently been developed which treats in a special way the seasonal changes of IH-sizes in temperature time series. The ACMANT is a further development of the Caussinus-Mestre method, that is one of the most effective tool among the known homogenising methods. The ACMANT applies a bivariate test for searching the timings of IHs, the two variables are the annual mean temperature and the amplitude of seasonal temperature-cycle. The ACMANT contains several further innovations whose efficiencies are tested with the benchmark of the COST ES0601 project. The paper describes the properties and the operation of ACMANT and presents some verification results. The results show that the ACMANT has outstandingly high performance. The ACMANT is a recommended method for homogenising networks of monthly temperature time series that observed in mid- or high geographical latitudes, because the harmonic seasonal cycle of IH-size is valid for these time series only.

Thermal instability of the fluid column in a borehole: application to the Yaxcopoil hole (Mexico)

Observational evidence proved that even when a borehole is in “fully” stabilized conditions, temperature data may exhibit certain unrest resembling irregular oscillations in the order of hundredths or (in the extreme case) even tenths of degree. Temperature was monitored in complicated hydrogeological conditions in the Yaxcopoil-1 hole (Chicxulub impact structure, Mexico). Two experiments are reported: (a) 20-day monitoring when a logger was located in the center of the high temperature gradient anomaly produced by the cold wave slowly propagating downwards and (b) simultaneous three-loggers 18-day monitoring with loggers located above, in and below the anomaly. All observed temperature–time series displayed intermittent oscillations of temperature with sharp gradients and large fluctuations over all observed time scales. While the “upper” and “lower” records revealed quasi-periodic temperature variations, the “central” record shows fast temperature oscillations with strong up-and-down reversals, all with amplitudes up to a few tenths of degree. The observed temperature–time series were processed by recurrence and recurrence interval quantification as well as by spectral analyses. It is shown that fluid in a borehole, subject to thermal gradient, is stable, as far as the gradient remains below a certain critical value. At higher Rayleigh numbers, the periodic character of oscillations typical for “quiescent” regime is superseded by stochastic features. This “oscillatory” convection occurs due to instability of the horizontal boundary layers. In the specific case of the Yaxcopoil hole, the time series above and below the cold wave (characterized by relatively lower temperature gradients between 20 and 50 mK/m) contain a clear low frequency component produced by tidal forcing. This component dominates over the high frequency domain (periods from 10–15 to 1 min), which exhibit a scaling behavior. This pattern conspicuously changes in the center part of the cold wave, where the local temperature gradient exceeds 200 mK/m and where tidal forcing composes only ~3% of the signal.

Thermal characterisation of active layer across a soil moisture gradient in the McMurdo Dry Valleys, Antarctica

AbstractHeat transport into active layer soils is important to understanding potential responses to changes in surface energy balance, particularly in the context of changing climate. Here we present results of a study to characterise soil thermal properties along a soil moisture gradient adjacent to Lake Fryxell in Taylor Valley, Antarctica. Our goals were to characterise the thermal characteristics of these relatively wet soils (compared to the rest of the McMurdo Dry Valleys landscape), and to assess the response of the active layer to possible increases in soil moisture. We measured subsurface temperatures at depths from 3 to 50 cm at four locations along a natural gradient of wet to dry soils adjacent to Lake Fryxell from January 2006 to January 2007. We used a numerical model to estimate apparent thermal diffusivity (ATD) and simulate observed temperature time series. Calculations of ATD at discrete locations yielded values ranging from 1.0 × 10−9 – 2.4 × 10−5 m2 s−1, and the corresponding range of bulk (i.e. depth averaged at a single surface location) ATD was 2.9 × 10−9–1.2 × 10−7 m2 s−1. Thawed soils had a range of bulk ATD during warming of 2.9 × 10−9–3.8 × 10−8 m2 s−1, and during cooling of 2.9 × 10−9–4.8 × 10−8 m2 s−1. When soils were frozen, however, the range of bulk ATD was 7.6 × 10−9–1.2 × 10−7 m2 s−1 during warming, and 7.8 × 10−9–1.1 × 10−7 m2 s−1 during cooling. Estimated bulk ATD values were consistently greater in locations of enhanced soil moisture, so lakeside soils were more likely to conduct energy into the subsurface. Increased soil moisture across the landscape would likely increase ATD, allowing for greater heat exchange between the atmosphere and the subsurface. Copyright © 2009 John Wiley &amp; Sons, Ltd.