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We present a novel space‐time Bayesian hierarchical model (BHM) to reconstruct annual Sea Surface Temperature (SST) over a large domain based on SST at limited proxy (i.e., sediment core) locations. The model is tested in the equatorial Pacific. The BHM leverages Principal Component Analysis to identify dominant space‐time modes of contemporary variability of the SST field at the proxy locations and employs these modes in a Gaussian process framework to estimate SSTs across the entire domain. The BHM allows us to model the mean field and covariance, varying in space and time in the process layers of the hierarchy. Using the Markov Chain Monte Carlo (MCMC) method and suitable priors on the model parameters, posterior distributions of the model parameters and, consequently, posterior distributions of the SST fields and the attendant uncertainties are obtained for any desired year. The BHM is calibrated and validated in the contemporary period (1854–2014) and subsequently applied to reconstruct SST fields during the Holocene (0–10 ka). Results are consistent with prior inferences of La Niña‐like conditions during the Holocene. This modeling framework opens exciting prospects for modeling and reconstruction of other fields, such as precipitation, drought indices, and vegetation. 

Abstract. Lipid remodeling, the modification of cell membrane chemistry via structural rearrangements within the lipid pool of an organism, is a common physiological response amongst all domains of life to alleviate environmental stress and maintain cellular homeostasis. Whereas culture experiments and environmental studies of phytoplankton have demonstrated the plasticity of lipids in response to specific abiotic stressors, few analyses have explored the impacts of multi-environmental stressors at the community-level scale. Here, we study changes in the pool of intact polar lipids (IPLs) of a phytoplanktonic community exposed to multi-environmental stressors during a ∼ 2-month-long mesocosm experiment deployed in the eastern tropical South Pacific off the coast of Callao, Peru. We investigate lipid remodeling of IPLs in response to changing nutrient stoichiometries, temperature, pH, and light availability in surface and subsurface water masses with contrasting redox potentials, using multiple linear regressions, classification and regression trees, and random forest analyses. We observe proportional increases in certain glycolipids (namely mono- and diglycosyldiacylglycerol – MGDG and DGDG, respectively) associated with higher temperatures and oxic conditions, consistent with previous observations of their utility to compensate for thermal stress and their degradation under oxygen stress. N-bearing (i.e., betaine lipids and phosphatidylethanolamine – BLs and PE) and non-N-bearing (i.e., MGDG; phosphatidylglycerol, PG; and sulfoquinovosyldiacylglycerol, SQDG) IPLs are anti-correlated and have strong positive correlations with nitrogen-replete and nitrogen-depleted conditions, respectively, which suggests a substitution mechanism for N-bearing IPLs under nitrogen limitation. Reduced CO2(aq) availability and increased pH levels are associated with greater proportions of DGDG and SQDG IPLs, possibly in response to the lower concentration of CO2(aq) and the overall lower availability of inorganic carbon for fixation. A higher production of MGDG in surface waters corresponds well with its established photoprotective and antioxidant mechanisms in thylakoid membranes. The observed statistical relationships between IPL distributions, physicochemical parameters, and the composition of the phytoplankton community suggest evidence of lipid remodeling in response to environmental stressors. These physiological responses may allow phytoplankton to reallocate resources from structural or extrachloroplastic membrane lipids (i.e., phospholipids and betaine lipids) under high-growth conditions to thylakoid and/or plastid membrane lipids (i.e., glycolipids and certain phosphatidylglycerols) under growth-limiting conditions. Further investigation of the exact mechanisms controlling the observed trends in lipid distributions is necessary to better understand how membrane reorganization under multi-environmental stressors can affect the pools of cellular C, N, P, and S, as well as their fluxes to higher trophic levels in marine environments subjected to increasing environmental pressure. Our results suggest that future studies addressing the biogeochemical consequences of climate change in the eastern tropical South Pacific Ocean must take into consideration the impacts of lipid remodeling in phytoplankton. 

Abstract Snowpack in mountainous areas often provides water storage for summer and fall, especially in the Western United States. In situ observations of snow properties in mountainous terrain are limited by cost and effort, impacting both temporal and spatial sampling, while remote sensing estimates provide more complete spacetime coverage. Spatial estimates of fractional snow covered area (fSCA) at 30m are available every 16 days from the series of multispectral scanning instruments on Landsat platforms. Daily estimates at 463m spatial resolution are also available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite. Fusing Landsat and MODIS fSCA images creates high resolution daily spatial estimates of fSCA that are needed for various uses: to support scientists and managers interested in energy and water budgets for water resources and to understand the movement of animals in a changing climate. Here, we propose a new machine learning approach conditioned on MODIS fSCA, as well as a set of physiographic features, and fit to Landsat fSCA over a portion of the Sierra Nevada USA. The predictions are daily 30m fSCA. The approach relies on two stages of spatially‐varying models. The first classifies fSCA into three categories and the second yields estimates within (0, 100) percent fSCA. Separate models are applied and fitted within sub‐regions of the study domain. Compared with a recently‐published machine learning model (Rittger, Krock, et al., 2021), this approach uses spatially local (rather than global) random forests, and improves the classification error of fSCA by 16%, and fractionally‐covered pixel estimates by 18%. 

Abstract The timing of melt onset in the Arctic plays a key role in the evolution of sea ice throughout Spring, Summer and Autumn. A major catalyst of early melt onset is increased downwelling longwave radiation, associated with increased levels of moisture in the atmosphere. Determining the atmospheric moisture pathways that are tied to increased downwelling longwave radiation and melt onset is therefore of keen interest. We employed Self Organizing Maps (SOM) on the daily sea level pressure for the period 1979–2018 over the Arctic during the melt season (April–July) and identified distinct circulation patterns. Melt onset dates were mapped on to these SOM patterns. The dominant moisture transport to much of the Arctic is enabled by a broad low pressure region stretching over Siberia and a high pressure over northern North America and Greenland. This configuration, which is reminiscent of the North American-Eurasian Arctic dipole pattern, funnels moisture from lower latitudes and through the Bering and Chukchi Seas. Other leading patterns are variations of this which transport moisture from North America and the Atlantic to the Central Arctic and Canadian Arctic Archipelago. Our analysis further indicates that most of the early and late melt onset timings in the Arctic are strongly related to the strong and weak emergence of these preferred circulation patterns, respectively. 

Abstract We develop a space‐time Bayesian hierarchical modeling (BHM) framework for two flood risk attributes—seasonal daily maximum flow and the number of events that exceed a threshold during a season (NEETM)—at a suite of gauge locations on a river network. The model uses generalized extreme value (GEV) and Poisson distributions as marginals for these flood attributes with non‐stationary parameters. The rate parameters of the Poisson distribution and location, scale, and shape parameters of the GEV are modeled as linear functions of suitable covariates. Gaussian copulas are applied to capture the spatial dependence. The best covariates are selected using the Watanabe‐Akaike information criterion (WAIC). The modeling framework results in the posterior distribution of the flood attributes at all the gauges and various lead times. We demonstrate the utility of this modeling framework to forecast the flood risk attributes during the summer peak monsoon season (July‐August) at five gauges in the Narmada River basin (NRB) of West‐Central India for several lead times (0–3 months). As potential covariates, we consider climate indices such as El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Pacific Warm Pool Region (PWPR) from antecedent seasons, which have shown strong teleconnections with the Indian monsoon. We also include new indices related to the East Pacific and West Indian Ocean regions depending on the lead times. We show useful long lead skill from this modeling approach which has a strong potential to enable robust risk‐based flood mitigation and adaptation strategies 3 months before flood occurrences. 

Abstract We present a Bayesian hierarchical space‐time stochastic weather generator (BayGEN) to generate daily precipitation and minimum and maximum temperatures. BayGEN employs a hierarchical framework with data, process, and parameter layers. In the data layer, precipitation occurrence at each site is modeled using probit regression using a spatially distributed latent Gaussian process; precipitation amounts are modeled as gamma random variables; and minimum and maximum temperatures are modeled as realizations from Gaussian processes. The latent Gaussian process that drives the precipitation occurrence process is modeled in the process layer. In the parameter layer, the model parameters of the data and process layers are modeled as spatially distributed Gaussian processes, consequently enabling the simulation of daily weather at arbitrary (unobserved) locations or on a regular grid. All model parameters are endowed with weakly informative prior distributions. The No‐U Turn sampler, an adaptive form of Hamiltonian Monte Carlo, is used to maximize the model likelihood function and obtain posterior samples of each parameter. Posterior samples of the model parameters propagate uncertainty to the weather simulations, an important feature that makes BayGEN unique compared to traditional weather generators. We demonstrate the utility of BayGEN with application to daily weather generation in a basin of the Argentine Pampas. Furthermore, we evaluate the implications of crop yield by driving a crop simulation model with weather simulations from BayGEN and an equivalent non‐Bayesian weather generator. 

Abstract Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are bacterial cell membrane lipids that, when preserved in sedimentary archives, can be used to infer continental paleotemperatures. Although commonly used global calibrations capture a relationship between the distribution of brGDGTs and temperature, they underestimate temperatures for tropical regions as much as ~16°C. Furthermore, some global calibrations reach saturation at around 24–25°C, and, in general, they have root‐mean‐squared errors (RMSEs ≈ ~4°C) that are too large for them to resolve small variations in paleoclimate variability in tropical regions. We present an in situ regional calibration of soil brGDGTs along altitudinal transects on both flanks of the Eastern Cordillera of Colombia in the northern tropical Andes that spans ~3,200 m in elevation and 17°C and 19°C in mean annual soil and air temperatures, respectively. These new soil and air regional calibrations yield RMSEs of 1.5°C and 1.9°C, respectively. When combined with existing data from elsewhere in the tropics, the integrated data (n = 175) not only fit a linear calibration with a RMSE of 2.7°C but also fit a nonlinear calibration with a RMSE of 2.2°C. These calibrations allow for a more precise and reliable reconstruction of past temperatures in the tropics than global calibrations.