scholarly journals Forecasting Facing Economic Shifts, Climate Change and Evolving Pandemics

Econometrics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Jennifer L. Castle ◽  
Jurgen A. Doornik ◽  
David F. Hendry
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Low Frequency ◽  
Empirical Modeling ◽  
Measurement Systems ◽  
Data Revisions ◽  
Stochastic Trends ◽  
Economic Climate ◽  
Future Outcomes ◽  
Distributional Shifts

By its emissions of greenhouse gases, economic activity is the source of climate change which affects pandemics that in turn can impact badly on economies. Across the three highly interacting disciplines in our title, time-series observations are measured at vastly different data frequencies: very low frequency at 1000-year intervals for paleoclimate, through annual, monthly to intra-daily for current climate; weekly and daily for pandemic data; annual, quarterly and monthly for economic data, and seconds or nano-seconds in finance. Nevertheless, there are important commonalities to economic, climate and pandemic time series. First, time series in all three disciplines are subject to non-stationarities from evolving stochastic trends and sudden distributional shifts, as well as data revisions and changes to data measurement systems. Next, all three have imperfect and incomplete knowledge of their data generating processes from changing human behaviour, so must search for reasonable empirical modeling approximations. Finally, all three need forecasts of likely future outcomes to plan and adapt as events unfold, albeit again over very different horizons. We consider how these features shape the formulation and selection of forecasting models to tackle their common data features yet distinct problems.

Econometrics for Modelling Climate Change

2021 ◽  
Author(s):  
Jennifer L. Castle ◽  
David F. Hendry
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Econometric Modeling ◽  
Complex Data ◽  
Explanatory Variables ◽  
Stochastic Trends ◽  
Trend Breaks ◽  
Abrupt Shifts ◽  
Climate Time Series ◽  
Do So

Shared features of economic and climate time series imply that tools for empirically modeling nonstationary economic outcomes are also appropriate for studying many aspects of observational climate-change data. Greenhouse gas emissions, such as carbon dioxide, nitrous oxide, and methane, are a major cause of climate change as they cumulate in the atmosphere and reradiate the sun’s energy. As these emissions are currently mainly due to economic activity, economic and climate time series have commonalities, including considerable inertia, stochastic trends, and distributional shifts, and hence the same econometric modeling approaches can be applied to analyze both phenomena. Moreover, both disciplines lack complete knowledge of their respective data-generating processes (DGPs), so model search retaining viable theory but allowing for shifting distributions is important. Reliable modeling of both climate and economic-related time series requires finding an unknown DGP (or close approximation thereto) to represent multivariate evolving processes subject to abrupt shifts. Consequently, to ensure that DGP is nested within a much larger set of candidate determinants, model formulations to search over should comprise all potentially relevant variables, their dynamics, indicators for perturbing outliers, shifts, trend breaks, and nonlinear functions, while retaining well-established theoretical insights. Econometric modeling of climate-change data requires a sufficiently general model selection approach to handle all these aspects. Machine learning with multipath block searches commencing from very general specifications, usually with more candidate explanatory variables than observations, to discover well-specified and undominated models of the nonstationary processes under analysis, offers a rigorous route to analyzing such complex data. To do so requires applying appropriate indicator saturation estimators (ISEs), a class that includes impulse indicators for outliers, step indicators for location shifts, multiplicative indicators for parameter changes, and trend indicators for trend breaks. All ISEs entail more candidate variables than observations, often by a large margin when implementing combinations, yet can detect the impacts of shifts and policy interventions to avoid nonconstant parameters in models, as well as improve forecasts. To characterize nonstationary observational data, one must handle all substantively relevant features jointly: A failure to do so leads to nonconstant and mis-specified models and hence incorrect theory evaluation and policy analyses.

scholarly journals Shift Detection in Hydrological Regimes and Pluriannual Low-Frequency Streamflow Forecasting Using the Hidden Markov Model

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2058 ◽  
Author(s):  
Larissa Rolim ◽  
Francisco de Souza Filho
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Extreme Events ◽  
Hidden Markov ◽  
Low Frequency ◽  
Streamflow Forecasting ◽  
Hydrologic Time Series ◽  
Low Frequency Variability ◽  
The Impact

Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.

scholarly journals Locally and Remotely Forced Subtropical AMOC Variability: A Matter of Time Scales

2020 ◽  
Vol 33 (12) ◽  
pp. 5155-5172
Author(s):  
Quentin Jamet ◽  
William K. Dewar ◽  
Nicolas Wienders ◽  
Bruno Deremble ◽  
Sally Close ◽  
...  
Keyword(s):  
Time Series ◽  
Time Scales ◽  
North Atlantic ◽  
Time Scale ◽  
Low Frequency ◽  
Meridional Overturning Circulation ◽  
Open Boundary ◽  
Overturning Circulation ◽  
Decadal Scale ◽  
Meridional Overturning

AbstractMechanisms driving the North Atlantic meridional overturning circulation (AMOC) variability at low frequency are of central interest for accurate climate predictions. Although the subpolar gyre region has been identified as a preferred place for generating climate time-scale signals, their southward propagation remains under consideration, complicating the interpretation of the observed time series provided by the Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array–Western Boundary Time Series (RAPID–MOCHA–WBTS) program. In this study, we aim at disentangling the respective contribution of the local atmospheric forcing from signals of remote origin for the subtropical low-frequency AMOC variability. We analyze for this a set of four ensembles of a regional (20°S–55°N), eddy-resolving (1/12°) North Atlantic oceanic configuration, where surface forcing and open boundary conditions are alternatively permuted from fully varying (realistic) to yearly repeating signals. Their analysis reveals the predominance of local, atmospherically forced signal at interannual time scales (2–10 years), whereas signals imposed by the boundaries are responsible for the decadal (10–30 years) part of the spectrum. Due to this marked time-scale separation, we show that, although the intergyre region exhibits peculiarities, most of the subtropical AMOC variability can be understood as a linear superposition of these two signals. Finally, we find that the decadal-scale, boundary-forced AMOC variability has both northern and southern origins, although the former dominates over the latter, including at the site of the RAPID array (26.5°N).

scholarly journals Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

2021 ◽  
Author(s):  
Ye Yuan ◽  
Stefan Härer ◽  
Tobias Ottenheym ◽  
Gourav Misra ◽  
Alissa Lüpke ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Strong Decrease ◽  
Temperature Changes ◽  
Interpolation Accuracy ◽  
Observation Network ◽  
Shiny App ◽  
Long Term Trends ◽  
Autumn And Winter

AbstractPhenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year−1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year−1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes.

scholarly journals Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe

2021 ◽  
Vol 193 (4) ◽  
Author(s):  
Stefan Erasmi ◽  
Michael Klinge ◽  
Choimaa Dulamsuren ◽  
Florian Schneider ◽  
Markus Hauck
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Global Climate Change ◽  
Global Climate ◽  
Siberian Larch ◽  
Growing Season ◽  
Forest Productivity ◽  
Forest Steppe ◽  
Fragmented Forest ◽  
Southern Fringe

AbstractThe monitoring of the spatial and temporal dynamics of vegetation productivity is important in the context of carbon sequestration by terrestrial ecosystems from the atmosphere. The accessibility of the full archive of medium-resolution earth observation data for multiple decades dramatically improved the potential of remote sensing to support global climate change and terrestrial carbon cycle studies. We investigated a dense time series of multi-sensor Landsat Normalized Difference Vegetation Index (NDVI) data at the southern fringe of the boreal forests in the Mongolian forest-steppe with regard to the ability to capture the annual variability in radial stemwood increment and thus forest productivity. Forest productivity was assessed from dendrochronological series of Siberian larch (Larix sibirica) from 15 plots in forest patches of different ages and stand sizes. The results revealed a strong correlation between the maximum growing season NDVI of forest sites and tree ring width over an observation period of 20 years. This relationship was independent of the forest stand size and of the landscape’s forest-to-grassland ratio. We conclude from the consistent findings of our case study that the maximum growing season NDVI can be used for retrospective modelling of forest productivity over larger areas. The usefulness of grassland NDVI as a proxy for forest NDVI to monitor forest productivity in semi-arid areas could only partially be confirmed. Spatial and temporal inconsistencies between forest and grassland NDVI are a consequence of different physiological and ecological vegetation properties. Due to coarse spatial resolution of available satellite data, previous studies were not able to account for small-scaled land-cover patches like fragmented forest in the forest-steppe. Landsat satellite-time series were able to separate those effects and thus may contribute to a better understanding of the impact of global climate change on natural ecosystems.

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