ALBERT

Description: An information theory-based framework is developed to assess the predictability and quantify the forecast uncertainty of ENSO complexity, which includes different types of ENSO events with diverse characteristics. With the assistance of a recently developed multiscale stochastic conceptual model that successfully captures both the large-scale dynamics and many crucial statistical properties of the observed ENSO complexity, it is shown that different ENSO events possess distinct predictability limits. Beyond the ensemble mean value, the spread of the ensemble members also contains valuable information about predictability. First, La Niña events are most predictable at long lead times, especially as a subsequent transition after eastern Pacific (EP) El Niño events or during multi-year La Niña phases. Second, EP El Niños tend to be more predictable than the central Pacific (CP) El Niño events up to one year ahead due to a more favorable signal-to-noise ratio, even though their onset remains hard to predict. Third, 4 out of 6 CP El Niño events seem to be predictable up to 24 months ahead, where such strong predictability is often converted to skillful forecast. Fourth, strengthening/weakening the Walker circulation intensity increases/decreases CP predictability at long leads. Fifth, accounting for intraseasonal wind events in the initial condition strongly contributes to EP predictability at lead times of less than one year. Finally, it is shown that a Gaussian approximation of the information gain computation is accurate, making the information theory approach tractable for studying the predictability of more sophisticated models.

Description: The discrete element method (DEM) provides a new modeling approach for describing sea ice dynamics. It exploits particle-based methods to characterize the physical quantities of each sea ice floe along its trajectory under Lagrangian coordinates. One major challenge in applying DEM models is the heavy computational cost when the number of floes becomes large. In this paper, an efficient Lagrangian parameterization algorithm is developed, which aims at reducing the computational cost of simulating the DEM models while preserving the key features of the sea ice. The new parameterization takes advantage of a small number of artificial ice floes, called the superfloes, to effectively approximate a considerable number of the floes, where the parameterization scheme satisfies several important physics constraints. The physics constraints guarantee the superfloe parameterized system will have short-term dynamical behavior similar to that of the full system. These constraints also allow the superfloe parameterized system to accurately quantify the long-range uncertainty, especially the non-Gaussian statistical features, of the full system. In addition, the superfloe parameterization facilitates a systematic noise inflation strategy that significantly advances an ensemble-based data assimilation algorithm for recovering the unobserved ocean field underneath the sea ice. Such a new noise inflation method avoids ad hoc tunings as in many traditional algorithms and is computationally extremely efficient. Numerical experiments based on an idealized DEM model with multiscale features illustrate the success of the superfloe parameterization in quantifying the uncertainty and assimilating both the sea ice and the associated ocean field.

Description: As critical transition zones between the land and the sea, estuaries are not only hotspots of hydrogeochemical and microbial processes/reactions, but also play a vital role in processing and transferring terrestrial fluxes of metals and nutrients to the sea. This study focused on three estuaries in the Gulf of Bothnia. All of them experience frequent inputs of acidic and Mn/metal-rich creek waters due to flushing of acid sulfate soils that are widespread in the creekś catchments. Analyzing existing long-term water chemistry data revealed a strong seasonal variation of Mn loads, with the highest values in spring (after snow melt) and autumn (after heavy rains). We sampled surface waters, suspended particulate matter (SPM), and sediments from the estuarine mixing zones and determined the loads and solid-phase speciation of Mn as well as the composition and metabolic potentials of microbial communities. The results showed that the removal, cycling, and lateral transport of Mn were governed by similar phases and processes in the three estuaries. Manganese X-ray absorption spectroscopy data of the SPM suggested that the removal of Mn was regulated by silicates (e.g., biotite), organically complexed Mn(II), and MnOx (dominated by groutite and phyllomanganates). While the fractional amounts of silicate-bound Mn(II) were overall low and constant throughout the estuaries, MnOx was strongly correlated with the Mn loadings of the SPM and thus the main vector for the removal of Mn in the central and outer parts of the estuaries, along with organically complexed Mn(II). Down estuary, both the fractional amounts and average Mn oxidation state of the MnOx phases increased with (i) the total Mn loads on the SPM samples and (ii) the relative abundances of several potential Mn-oxidizing bacteria (Flavobacterium, Caulobacter, Mycobacterium, and Pedobacter) in the surface waters. These features collectively suggested that the oxidation of Mn, probably mediated by the potential Mn-oxidizing microorganisms, became more extensive and complete towards the central and outer parts of the estuaries. At two sites in the central parts of one estuary, abundant phyllomanganates occurred in the surface sediments, but were converted to surface-sorbed Mn(II) phases at deeper layers (〉3–4 cm). The occurrence of phyllomanganates may have suppressed the reduction of sulfate in the surface sediments, pushing down the methane sulfate transition zone that is typically shallow in estuarine sediments. At the outermost site in the estuary, deposited MnOx were reduced immediately at the water–sediment interface and converted most likely to Mn carbonate. The mobile Mn species produced by the Mn reduction processes (e.g., aqueous Mn(II) and ligand complexed Mn(III)) could partly diffuse into the overlying waters and, together with the estuarine Mn loads carried by the surface waters, transfer large amounts of reactive Mn into open coastal areas and subsequently contribute to Mn shuttling and inter-linked biogeochemical processes over the seafloor. Given the widespread occurrence of acid sulfate soils and other sulfidic geological materials on many coastal plains worldwide, the identified Mn attenuation and transport mechanisms are relevant for many estuaries globally.

Description: Characterization of regrowing forests is vital for understanding forest dynamics to assess the impacts on carbon stocks and to support sustainable forest management. Although remote sensing is a key tool for understanding and monitoring forest dynamics, the use of exclusively remotely sensed data to explore the effects of different variables on regrowing forests across all biomes in Brazil has rarely been investigated. Here, we analyzed how environmental and human factors affect regrowing forests. Based on Brazil's secondary forest age map, 3060 locations disturbed between 1984 and 2018 were sampled, interpreted and analyzed in different biomes. We interpreted the time since disturbance for the sampled pixels in Google Earth Engine. Elevation, slope, climatic water deficit (CWD), the total Nitrogen of soil, cation exchange capacity (CEC) of soil, surrounding tree cover, distance to roads, distance to settlements and fire frequency were analyzed in their importance for predicting aboveground biomass (AGB) and tree cover derived from global forest aboveground biomass map and tree cover map, respectively. Results show that time since disturbance interpreted from satellite time series is the most important predictor for characterizing AGB and tree cover of regrowing forests. AGB increased with increasing time since disturbance, surrounding tree cover, soil total N, slope, distance to roads, distance to settlements and decreased with larger fire frequency, CWD and CEC of soil. Tree cover increased with larger time since disturbance, soil total N, surrounding tree cover, distance to roads, distance to settlements, slope and decreased with increasing elevation and CWD. These results emphasize the importance of remotely sensing products as key opportunities to improve the characterization of forest regrowth and to reduce data gaps and uncertainties related to forest carbon sink estimation. Our results provide a better understanding of regional forest dynamics, toward developing and assessing effective forest-related restoration and climatic mitigation strategies.