Pervasive alterations to snow-dominated ecosystem functions under climate change.

Climate change projections consistently demonstrate that warming temperatures and dwindling seasonal snowpack will elicit cascading effects on ecosystem function and water resource availability. Despite this consensus, little is known about potential changes in the variability of ecohydrological conditions, which is also required to inform climate change adaptation and mitigation strategies. Considering potential changes in ecohydrological variability is critical to evaluating the emergence of trends, assessing the likelihood of extreme events such as floods and droughts, and identifying when tipping points may be reached that fundamentally alter ecohydrological function. Using a single-model Large Ensemble with sophisticated terrestrial ecosystem representation, we characterize projected changes in the mean state and variability of ecohydrological processes in historically snow-dominated regions of the Northern Hemisphere. Widespread snowpack reductions, earlier snowmelt timing, longer growing seasons, drier soils, and increased fire risk are projected for this century under a high-emissions scenario. In addition to these changes in the mean state, increased variability in winter snowmelt will increase growing-season water deficits and increase the stochasticity of runoff. Thus, with warming, declining snowpack loses its dependable buffering capacity so that runoff quantity and timing more closely reflect the episodic characteristics of precipitation. This results in a declining predictability of annual runoff from maximum snow water equivalent, which has critical implications for ecosystem stress and water resource management. Our results suggest that there is a strong likelihood of pervasive alterations to ecohydrological function that may be expected with climate change.

Freshwater Flux Variability Lengthens the Period of the Low-Frequency AMOC Variability.

Atlantic Meridional Overturning Circulation (AMOC) exhibits interdecadal to multidecadal variability, yet the role of surface freshwater flux (FWF) variability in this AMOC variability remains unclear. This study isolates the contribution of FWF variability in modulating AMOC through a partially coupled experiment, in which the effect of the interactive FWF is disabled. It is demonstrated that the impact of the coupled FWF variability enhances the persistence of density and deep convection anomalies in the Labrador Sea (LS), thus lengthening the period of the AMOC oscillation on multidecadal timescale and suppressing its ∼30-year periodicity. Further lead-lag regressions illuminate that the more persistent LS density anomalies are maintained by two mechanisms: (a) The local temperature-salinity coupling through the evaporation and (b) a downstream propagation along the East Greenland Current of the extra salinity anomaly due to the sea ice melting changes associated with an atmosphere forcing over the southern Greenland tip.

Observations of Local Positive Low Cloud Feedback Patterns and Their Role in Internal Variability and Climate Sensitivity.

Modeling studies have shown that cloud feedbacks are sensitive to the spatial pattern of sea surface temperature (SST) anomalies, while cloud feedbacks themselves strongly influence the magnitude of SST anomalies. Observational counterparts to such patterned interactions are still needed. Here we show that distinct large-scale patterns of SST and low-cloud cover (LCC) emerge naturally from objective analyses of observations and demonstrate their close coupling in a positive local SST-LCC feedback loop that may be important for both internal variability and climate change. The two patterns that explain the maximum amount of covariance between SST and LCC correspond to the Interdecadal Pacific Oscillation and the Atlantic Multidecadal Oscillation, leading modes of multidecadal internal variability. Spatial patterns and time series of SST and LCC anomalies associated with both modes point to a strong positive local SST-LCC feedback. In many current climate models, our analyses suggest that SST-LCC feedback strength is too weak compared to observations. Modeled local SST-LCC feedback strength affects simulated internal variability so that stronger feedback produces more intense and more realistic patterns of internal variability. To the extent that the physics of the local positive SST-LCC feedback inferred from observed climate variability applies to future greenhouse warming, we anticipate significant amount of delayed warming because of SST-LCC feedback when anthropogenic SST warming eventually overwhelm the effects of internal variability that may mute anthropogenic warming over parts of the ocean. We postulate that many climate models may be underestimating both future warming and the magnitude of modeled internal variability because of their weak SST-LCC feedback.

Mechanisms for low-frequency variability of summer Arctic sea ice extent.

Satellite observations reveal a substantial decline in September Arctic sea ice extent since 1979, which has played a leading role in the observed recent Arctic surface warming and has often been attributed, in large part, to the increase in greenhouse gases. However, the most rapid decline occurred during the recent global warming hiatus period. Previous studies are often focused on a single mechanism for changes and variations of summer Arctic sea ice extent, and many are based on short observational records. The key players for summer Arctic sea ice extent variability at multidecadal/centennial time scales and their contributions to the observed summer Arctic sea ice decline are not well understood. Here a multiple regression model is developed for the first time, to the author's knowledge, to provide a framework to quantify the contributions of three key predictors (Atlantic/Pacific heat transport into the Arctic, and Arctic Dipole) to the internal low-frequency variability of Summer Arctic sea ice extent, using a 3,600-y-long control climate model simulation. The results suggest that changes in these key predictors could have contributed substantially to the observed summer Arctic sea ice decline. If the ocean heat transport into the Arctic were to weaken in the near future due to internal variability, there might be a hiatus in the decline of September Arctic sea ice. The modeling results also suggest that at multidecadal/centennial time scales, variations in the atmosphere heat transport across the Arctic Circle are forced by anticorrelated variations in the Atlantic heat transport into the Arctic.

Quantifying the effect of intertrial dependence on perceptual decisions.

In the perceptual sciences, experimenters study the causal mechanisms of perceptual systems by probing observers with carefully constructed stimuli. It has long been known, however, that perceptual decisions are not only determined by the stimulus, but also by internal factors. Internal factors could lead to a statistical influence of previous stimuli and responses on the current trial, resulting in serial dependencies, which complicate the causal inference between stimulus and response. However, the majority of studies do not take serial dependencies into account, and it has been unclear how strongly they influence perceptual decisions. We hypothesize that one reason for this neglect is that there has been no reliable tool to quantify them and to correct for their effects. Here we develop a statistical method to detect, estimate, and correct for serial dependencies in behavioral data. We show that even trained psychophysical observers suffer from strong history dependence. A substantial fraction of the decision variance on difficult stimuli was independent of the stimulus but dependent on experimental history.We discuss the strong dependence of perceptual decisions on internal factors and its implications for correct data interpretation.

East Asian summer monsoon enhanced by COVID-19.

Anthropogenic emissions decreased dramatically during the COVID-19 pandemic, but its possible effect on monsoon is unclear. Based on coupled models participating in the COVID Model Intercomparison Project (COVID-MIP), we show modeling evidence that the East Asian summer monsoon (EASM) is enhanced by 2.2% in terms of precipitation and by 5.4% in terms of the southerly wind at lower troposphere, and the amplitude of the forced response reaches about 1/3 of the standard deviation for interannual variability. The enhanced EASM during COVID-19 pandemic is a fast response to reduced aerosols, which is confirmed by the simulated response to the removal of all anthropogenic aerosols. The observational evidence, i.e., the anomalously strong EASM observed in 2020 and 2021, also supports the simulated enhancement of EASM. The essential mechanism for the enhanced EASM in response to COVID-19 is the enhanced zonal thermal contrast between Asian continent and the western North Pacific in the troposphere, due to the reduced aerosol concentration over Asian continent and the associated latent heating feedback. As the enhancement of EASM is a fast response to the reduction in aerosols, the effect of COVID-19 on EASM dampens soon after the rebound of emissions based on the models participating in COVID-MIP. Supplementary Information: The online version contains supplementary material available at 10.1007/s00382-022-06247-8.

Estimation of the uncertainty of hydrologic predictions in a karstic Mediterranean watershed.

The Koiliaris River basin is a semi-arid Mediterranean karstic watershed where water needs during the summer are exclusively covered by the karstic springs flow. Uncertainty assessment of the hydrologic projections for karstic watersheds may reveal possible water deficits that cannot otherwise be taken into account. The Soil Water Assessment Tool (SWAT) along with a karstic model (Karst-SWAT) is used to assess the composite spring and surface flow. The parameter uncertainty of both the surface and karstic flow models is estimated by combining the SUFI2 interface and the @RISK by PALISADE software. Eleven combinations of five Regional Climate Models (RCMs) and three Representative Concentration Pathways (RCPs) provide input to the hydrologic models. Representative rainfall time series for certain scenarios are stochastically modeled with the LARS weather generator. Monte Carlo simulations are used to investigate the effect of input internal variability on the flow output. The uncertainty of karstic flow due to the parameter uncertainty of the SWAT and Karst-SWAT models is 10.0% (Coefficient of Variation), which is comparable to the estimated uncertainty due to climate change scenarios (10.1%) until 2059. The combined uncertainty for the total flow at the basin exit due to both models' parameter uncertainty is 6.6%, comparable to the uncertainty due to the internal variability (5.6%). The total uncertainty of karstic flow, combining model parameter uncertainty and the internal variability of the climate scenarios is 11.0%. The total uncertainty estimate is used in conjunction with the lowest karstic flow projection to assess the most adverse scenario for the future mean annual karstic flow. This is the first study which estimates the combined uncertainty of surface and karstic flow prediction due to model parameter uncertainty and internal variability. Our study provides a rigorous methodology for uncertainty estimation and analysis which is transferable to other karstic regions of the world.

Early Ongoing Speciation of Ogataea uvarum Sp. Nov. Within the Grape Ecosystem Revealed by the Internal Variability Among the rDNA Operon Repeats.

A yeast strain was isolated during a study on vineyard-associated yeast strains from Apulia in Southern Italy. ITS and LSU D1/D2 rDNA sequences showed this strain not to belong to any known species and was described as the type strain of Ogataea uvarum sp. nov., a close relative of O. philodendri. Several secondary peaks appeared in the sequences, suggesting internal heterogeneity among the copies of the rDNA. This hypothesis was tested by sequencing single clones of the marker region. The analyses showed different levels of variability throughout the operon with differences between the rRNA encoding genes and the internally transcribed regions. O. uvarum and O. philodendri share high frequency variants, i.e., variants frequently found in many clones, whereas there is a large variability of the low frequency polymorphisms, suggesting that the mechanism of homogenization is more active with the former than with the latter type of variation. These findings indicate that low frequency variants are detected in Sanger sequencing as secondary peaks whereas in Next Generation Sequencing (NGS) of metagenomics DNA would lead to an overestimate of the alpha diversity. For the first time in our knowledge, this investigation shed light on the variation of the copy number of the rDNA cistron during the yeast speciation process. These polymorphisms can be used to investigate on the processes occurring in these taxonomic markers during the separation of fungal species, it being a genetic process highly frequent in the complex microbial ecosystem existing in grape, must and wine.

Broadband noise masks suppress neural responses to narrowband stimuli.

White pixel noise is widely used to estimate the level of internal noise in a system by injecting external variance into the detecting mechanism. Recent work (Baker and Meese, 2012) has provided psychophysical evidence that such noise masks might also cause suppression that could invalidate estimates of internal noise. Here we measure neural population responses directly, using steady-state visual evoked potentials, elicited by target stimuli embedded in different mask types. Sinusoidal target gratings of 1 c/deg flickered at 5 Hz, and were shown in isolation, or with superimposed orthogonal grating masks or 2D white noise masks, flickering at 7 Hz. Compared with responses to a blank screen, the Fourier amplitude at the target frequency increased monotonically as a function of target contrast when no mask was present. Both orthogonal and white noise masks caused rightward shifts of the contrast response function, providing evidence of contrast gain control suppression. We also calculated within-observer amplitude variance across trials. This increased in proportion to the target response, implying signal-dependent (i.e., multiplicative) noise at the system level, the implications of which we discuss for behavioral tasks. This measure of variance was reduced by both mask types, consistent with the changes in mean target response. An alternative variety of noise, which we term zero-dimensional noise, involves trial-by-trial jittering of the target contrast. This type of noise produced no gain control suppression, and increased the amplitude variance across trials.