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As a common protein modification, asparagine-linked (N-linked) glycosylation has the capacity to greatly influence the biological and biophysical properties of proteins. However, the routine use of glycosylation as a strategy for engineering proteins with advantageous properties is limited by our inability to construct and screen large collections of glycoproteins for cataloguing the consequences of glycan installation. To address this challenge, we describe a combinatorial strategy termed shotgun scanning glycomutagenesis in which DNA libraries encoding all possible glycosylation site variants of a given protein are constructed and subsequently expressed in glycosylation-competent bacteria, thereby enabling rapid determination of glycosylatable sites in the protein. The resulting neoglycoproteins can be readily subjected to available high-throughput assays, making it possible to systematically investigate the structural and functional consequences of glycan conjugation along a protein backbone. The utility of this approach was demonstrated with three different acceptor proteins, namely bacterial immunity protein Im7, bovine pancreatic ribonuclease A, and human anti-HER2 single-chain Fv antibody, all of which were found to tolerate N-glycan attachment at a large number of positions and with relatively high efficiency. The stability and activity of many glycovariants was measurably altered by N-linked glycans in a manner that critically depended on the precise location of the modification. Structural models suggested that affinity was improved by creating novel interfacial contacts with a glycan at the periphery of a protein–protein interface. Importantly, we anticipate that our glycomutagenesis workflow should provide access to unexplored regions of glycoprotein structural space and to custom-made neoglycoproteins with desirable properties. 

The effects of volcanic eruptions on hurricane statistics are examined using two long simulations from the Community Earth System Model (CESM) Last Millennium Ensemble (LME). The first is an unforced control simulation, wherein all boundary conditions were held constant at their 850 CE values (LME_control). The second is a “fully forced” simulation with time evolving radiative changes from volcanic, solar, and land use changes from 850 CE through present (LME_forced). Large tropical volcanic eruptions produce the greatest change in radiative forcing during this time period, which comprise the focus of this study. The Weather Research and Forecasting (WRF) model is used to dynamically downscale 150 control years of LME_controland an additional 84 years of LME_forcedfor all mid-latitude volcanic eruptions between 1100 and 1850 CE. This time period was selected based on computational considerations. For each eruption, 2 years are dynamically downscaled. 23 of these volcanic eruptions are in the Northern Hemisphere and 19 are in the Southern Hemisphere. The effectiveness of the downscaling methodology is examined by applying the same downscaling approach to historical ERA-I reanalysis data and comparing the downscaled storm tracks and intensities to the International Best Track Archive for Climate Stewardship (IBTrACS) database. Hurricane statistics are then computed from both the downscaled control and downscaled forced LME simulations. Results suggest moderate effects on hurricanes from the average of all northern hemisphere eruptions, with the largest effects being from the volcanoes with the most aerosol forcing. More specifically, reductions in hurricane frequency, intensity, and lifetime following northern hemisphere eruptions are apparent. Strong evidence is also shown for correlation between eruption strength and changes in these diagnostics. The aggregate effect from both northern and southern hemisphere eruptions is minor. While reductions in frequency, intensity, and lifetime from northern hemisphere eruptions occur, the opposite effect is observed from southern hemisphere eruptions.

 

Plant phenology regulates the carbon cycle and land‐atmosphere coupling. Currently, climate models often disagree with observations on the seasonal cycle of vegetation growth, partially due to how spring onset is measured and simulated. Here we use both thermal and leaf area index (LAI) based indicators to characterize spring onset in CMIP6 models. Although the historical timing varies considerably across models, most agree that spring has advanced in recent decades and will continue to arrive earlier with future warming. Across the Northern Hemisphere for the periods 1950–2014, 1981–2014, and 2015–2099 in the historical and SSP5‐8.5 simulations, thermal‐based indicators estimate spring advances of −0.7 ± 0.2, −1.4 ± 0.4, and −2.4 ± 0.7 days/decade, while LAI‐based indicators estimate −0.4 ± 0.3, −0.1 ± 0.3, and −1±1.1 days/decade. Thereby, LAI‐based indicators exhibit weaker trends toward earlier onset, leading to uncertainties from different indices being as large or larger than model uncertainty. Reconciling these discrepancies is critical for understanding future changes in spring onset.

 

Abstract Climate models consistently project a significant drying in the Caribbean during climate change, and between 2013 and 2016 the region experienced the worst multiyear drought in the historical period. Although dynamical mechanisms have been proposed to explain drought in the Caribbean, the contributions from mass convergence and advection to precipitation minus evaporation ( P − E ) anomalies during drought are unknown. Here we analyze the dynamics of contemporaneous droughts in the Caribbean by decomposing the contributions of mass convergence and advection to P − E using observational and simulated data. We find that droughts arise from an anomalous subsidence over the southeastern Caribbean and northeastern South America. Although the contributions from mass convergence and advection vary across the region, it is mass convergence that is the main driver of drought in our study area. A similar dynamical pattern is observed in simulated droughts using the Community Earth System Model (CESM) Large Ensemble (LENS). 

Megadroughts are multidecadal periods of aridity more persistent than most droughts during the instrumental period. Paleoclimate evidence suggests that megadroughts occur in many parts of the world, including North America, Central America, western Europe, eastern Asia, and northern Africa. It remains unclear to what extent such megadroughts require external forcing or whether they can arise from internal climate variability alone. A novel statistical–dynamical approach is used to evaluate the possibility that such events arise solely as a function of interannual tropical sea surface temperature (SST) variations. A statistical emulator of tropical SST variations is constructed by using an empirical moving‐blocks bootstrap approach that randomly samples multiyear sequences of the observational SST record. This approach preserves the power spectrum, seasonal cycle, and spatial pattern of El Niño‐Southern Oscillation (ENSO) but removes longer timescale fluctuations embedded in the observational record. These resampled SST anomalies are then used to force an atmospheric model (the Community Atmosphere Model Version 5). As megadroughts emerge in this run, they should, therefore, be solely a consequence of La Niña sequences combined with internal atmospheric variability and persistence driven by soil moisture storage and other land‐surface processes. We indeed find that megadroughts in this simulation have an amplitude‐duration rate that is generally indistinguishable from the rate documented in paleoclimate records of the western United States. Our findings support the idea that megadroughts may occur randomly when the unforced climate system evolves freely over a sufficiently long period of time, implying that an unforced unusual but statistically plausible series of La Niña events may be sufficient to generate megadrought.

 

Evapotranspiration (ET) is a significant ecosystem flux, governing the partitioning of energy at the land surface. Understanding the seasonal pattern and magnitude ofETis critical for anticipating a range of ecosystem impacts, including drought, heat‐wave events, and plant mortality. In this study, we identified the relative controls of seasonal variability inET, and how these controls vary among ecosystems. We used overlapping AmeriFlux and PhenoCam time series at a daily timestep from 20 sites to explore these linkages (# site‐years >100), and our study area covered a broad climatological aridity gradient in the U.S. and Canada. We focused on disentangling the most important controls of bulk surface conductance (G_s) and evaporative fraction (EF = LE/[H + LE]), whereLEandHrepresent latent and sensible heat fluxes, respectively. Specifically, we investigated how vegetation phenology varied in importance relative to meteorological variables (vapor pressure deficit and antecedent precipitation) as a driver ofG_sandEFusing path analysis, a framework for quantifying and comparing the causal linkages among multiple response and explanatory variables. Our results revealed that the drivers ofG_sandEFseasonality varied significantly between energy‐ and water‐limited ecosystems. Specifically, precipitation had a much higher effect in water‐limited ecosystems, while seasonal patterns in canopy greenness emerged as a stronger control in energy‐limited ecosystems. Given that phenology is expected to shift under future climate, our findings provide key information for understanding and predicting how phenology may impact 21st‐century hydroclimate regimes and the surface‐energy balance.

 

Large-scale changes in the state of the land surface affect the circulation of the atmosphere and the structure and function of ecosystems alike. As global temperatures increase and regional climates change, the timing of key plant phenophase changes are likely to shift as well. Here we evaluate a suite of phenometrics designed to facilitate an “apples to apples” comparison between remote sensing products and climate model output. Specifically, we derive day-of-year (DOY) thresholds of leaf area index (LAI) from both remote sensing and the Community Land Model (CLM) over the Northern Hemisphere. This systematic approach to comparing phenologically relevant variables reveals appreciable differences in both LAI seasonal cycle and spring onset timing between model simulated phenology and satellite records. For example, phenological spring onset in the model occurs on average 30 days later than observed, especially for evergreen plant functional types. The disagreement in phenology can result in a mean bias of approximately 5% of the total estimated Northern Hemisphere NPP. Further, while the more recent version of CLM (v5.0) exhibits seasonal mean LAI values that are in closer agreement with satellite data than its predecessor (CLM4.5), LAI seasonal cycles in CLM5.0 exhibit poorer agreement. Therefore, despite broad improvements for a range of states and fluxes from CLM4.5 to CLM5.0, degradation of plant phenology occurs in CLM5.0. Therefore, any coupling between the land surface and the atmosphere that depends on vegetation state might not be fully captured by the existing generation of the model. We also discuss several avenues for improving the fidelity between observations and model simulations.