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Understanding the long-term evolution of Earth's plate–mantle system is reliant on absolute plate motion models in a mantle reference frame, but such models are both difficult to construct and controversial. We present a tectonic-rules-based optimization approach to construct a plate motion model in a mantle reference frame covering the last billion years and use it as a constraint for mantle flow models. Our plate motion model results in net lithospheric rotation consistently below 0.25∘ Myr−1, in agreement with mantle flow models, while trench motions are confined to a relatively narrow range of −2 to +2 cm yr−1 since 320 Ma, during Pangea stability and dispersal. In contrast, the period from 600 to 320 Ma, nicknamed the “zippy tricentenary” here, displays twice the trench motion scatter compared to more recent times, reflecting a predominance of short and highly mobile subduction zones. Our model supports an orthoversion evolution from Rodinia to Pangea with Pangea offset approximately 90∘ eastwards relative to Rodinia – this is the opposite sense of motion compared to a previous orthoversion hypothesis based on paleomagnetic data. In our coupled plate–mantle model a broad network of basal mantle ridges forms between 1000 and 600 Ma, reflecting widely distributed subduction zones. Between 600 and 500 Ma a short-lived degree-2 basal mantle structure forms in response to a band of subduction zones confined to low latitudes, generating extensive antipodal lower mantle upwellings centred at the poles. Subsequently, the northern basal structure migrates southward and evolves into a Pacific-centred upwelling, while the southern structure is dissected by subducting slabs, disintegrating into a network of ridges between 500 and 400 Ma. From 400 to 200 Ma, a stable Pacific-centred degree-1 convective planform emerges. It lacks an antipodal counterpart due to the closure of the Iapetus and Rheic oceans between Laurussia and Gondwana as well as due to coeval subduction between Baltica and Laurentia and around Siberia, populating the mantle with slabs until 320 Ma when Pangea is assembled. A basal degree-2 structure forms subsequent to Pangea breakup, after the influence of previously subducted slabs in the African hemisphere on the lowermost mantle structure has faded away. This succession of mantle states is distinct from previously proposed mantle convection models. We show that the history of plume-related volcanism is consistent with deep plumes associated with evolving basal mantle structures. This Solid Earth Evolution Model for the last 1000 million years (SEEM1000) forms the foundation for a multitude of spatio-temporal data analysis approaches.

The CO2 liberated along subduction zones through intrusive/extrusive magmatic activity and the resulting active and diffuse outgassing influences global atmospheric CO2. However, when melts derived from subduction zones intersect buried carbonate platforms, decarbonation reactions may cause the contribution to atmospheric CO2 to be far greater than segments of the active margin that lacks buried carbon-rich rocks and carbonate platforms. This study investigates the contribution of carbonate-intersecting subduction zones (CISZs) to palaeo-atmospheric CO2 levels over the past 410 million years by integrating a plate motion and plate boundary evolution model with carbonate platform development through time. Our model of carbonate platform development has the potential to capture a broader range of degassing mechanisms than approaches that only account for continental arcs.Continuous and cross-wavelet analyses as well as wavelet coherence are used to evaluate trends between the evolving lengths of carbonate-intersecting subduction zones, non-carbonate-intersecting subduction zones and global subduction zones, and are examined for periodic, linked behaviour with the proxy CO2 record between 410 Ma and the present. Wavelet analysis reveals significant linked periodic behaviour between 60 and 40 Ma, when CISZ lengths are relatively high and are correlated with peaks in palaeo-atmospheric CO2, characterised by a 32–48 Myr periodicity and a ∼ 8–12 Myr lag of CO2 peaks following CISZ length peaks. The linked behaviour suggests that the relative abundance of CISZs played a role in affecting global climate during the Palaeogene. In the 200–100 Ma period, peaks in CISZ lengths align with peaks in palaeo-atmospheric CO2, but CISZ lengths alone cannot be determined as the cause of a warmer Cretaceous–Jurassic climate. Nevertheless, across the majority of the Phanerozoic, feedback mechanisms between the geosphere, atmosphere and biosphere likely played dominant roles in modulating climate. Our modelled subduction zone lengths and carbonate-intersecting subduction zone lengths approximate magmatic activity through time, and can be used as input into fully coupled models of CO2 flux between deep and shallow carbon reservoirs.

Abstract To help with preparedness efforts of Canadian public health and safety systems for adaptation to climate change, the humidity index (humidex) and three threshold‐based humidex indices (annual number of days with humidex greater than 30, 35 and 40) were computed for a multi‐model ensemble of climate change projections, over Canada. The ensemble consists of one run from each 19 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and offers historical simulations starting in 1950 and future projections out to 2100 following Shared Socioeconomic Pathways (SSPs): SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5. Each ensemble member was bias‐adjusted and statistically downscaled using the Multivariate bias correction—N‐dimensional probability density function transform (MBCn) with hourly data from ERA5‐Land as the target dataset and following a method proposed by Diaconescu et al. (2023; International Journal of Climatology, 43, 837) to calculate humidex from daily climate model outputs. This paper details the steps for data production including evaluation of the target historical gridded data and selection of downscaling method and presents some of the resulting humidex projections at the end of the century.

We describe a novel method implemented in the GPlates plate tectonic reconstruction software to interactively reconstruct arbitrarily high-resolution raster data to past geological times using a rotation model. The approach is based on the projection of geo-referenced raster data into a cube map followed by a reverse projection onto rotated tectonic plates on the surface of the globe. This decouples the rendering of a geo-referenced raster from its reconstruction, providing a number of benefits including a simple implementation and the ability to combine rasters with different geo-referencing or inbuilt raster projections. The cube map projection is accelerated by graphics hardware in a wide variety of computer systems manufactured over the last decade. Furthermore, by integrating a multi-resolution tile partitioning into the cube map we can provide on-demand tile streaming, level-of-detail rendering and hierarchical visibility culling, enabling researchers to visually explore essentially unlimited resolution geophysical raster data attached to tectonic plates and reconstructed through geological time. This capability forms the basis for interactively building and improving plate reconstructions in an iterative fashion, particularly for tectonically complex regions.

Understanding tectonic and geodynamic processes leading to the present-day configuration of the Earth involves studying data and models across a variety of disciplines, from geochemistry, geochronology and geophysics, to plate kinematics and mantle dynamics. All these data represent a 3-D spatial and 1-D temporal framework, a formalism which is not exploited by traditional spatial analysis tools. This is arguably a fundamental limit in both the rigour and sophistication in which datasets can be combined for geological deep time analysis, and often confines the extent of data analyses to the present-day configurations of geological objects. The GPlates Geological Information Model (GPGIM) represents a formal specification of geological and geophysical data in a time-varying plate tectonics context, used by the GPlates virtual-globe software. It provides a framework in which relevant types of geological data are attached to a common plate tectonic reference frame, allowing the data to be reconstructed in a time-dependent spatio-temporal plate reference frame. The GPlates Markup Language (GPML), being an extension of the open standard Geography Markup Language (GML), is both the modelling language for the GPGIM and an XML-based data format for the interoperable storage and exchange of data modelled by it. The GPlates software implements the GPGIM allowing researchers to query, visualise, reconstruct and analyse a rich set of geological data including numerical raster data. The GPGIM has recently been extended to support time-dependent geo-referenced numerical raster data by wrapping GML primitives into the time-dependent framework of the GPGIM. Coupled with GPlates' ability to reconstruct numerical raster data and import/export from/to a variety of raster file formats, as well as its handling of time-dependent plate boundary topologies, interoperability with geodynamic softwares is established, leading to a new generation of deep-time spatio-temporal data analysis and modelling, including a variety of new functionalities, such as 4-D data-mining.

IntroductionIn the US, women are one of the fastest-growing segments of the prison population and more than a quarter of women in state prison are incarcerated for drug offenses. Substance use criminal diversion programs can be effective. It may be beneficial to identify individuals who are most likely to complete the program versus terminate early as this can provide information regarding who may need additional or unique programming to improve the likelihood of successful program completion. Prior research investigating prediction of success in these programs has primarily focused on demographic factors in male samples.MethodsThe current study used machine learning (ML) to examine other non-demographic factors related to the likelihood of completing a substance use criminal diversion program for women. A total of 179 women who were enrolled in a criminal diversion program consented and completed neuropsychological, self-report symptom measures, criminal history and demographic surveys at baseline. Model one entered 145 variables into a machine learning (ML) ensemble model, using repeated, nested cross-validation, predicting subsequent graduation versus termination from the program. An identical ML analysis was conducted for model two, in which 34 variables were entered, including the Women’s Risk/Needs Assessment (WRNA).ResultsML models were unable to predict graduation at an individual level better than chance (AUC = 0.59 [SE = 0.08] and 0.54 [SE = 0.13]). Post-hoc analyses indicated measures of impulsivity, trauma history, interoceptive awareness, employment/financial risk, housing safety, antisocial friends, anger/hostility, and WRNA total score and risk scores exhibited medium to large effect sizes in predicting treatment completion (p

Abstract The 2021 heatwave over Western North America (WNA) led to record‐breaking air temperatures and human‐perceived heat stress (humidex) values. The event was accompanied by drier conditions driven by prolonged atmospheric blocking. During the heatwave, the maximum 6‐day means of humidex and temperature (HX‐6 and TX‐6) exhibited larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C), relative to 1981–2021 extended summer (June‐September) averages. Extreme indices of humidex show faster and larger increases than those of temperature, reflecting the nonlinear positive relationship between humidex and temperature. Future projections from a multi‐model ensemble of 19 Coupled Model Intercomparison Project Phase six (CMIP6) Global Climate Models (GCMs) clearly show an increase in humidex and temperature extremes, especially under intermediate and high emissions scenarios. Humidex indices (HX‐6 and HX95) show faster and larger increases than temperature indices (TX‐6 and TX95) for the same future years and global warming levels. Controlling for differences in GCM climate sensitivity to greenhouse gas forcing yields robust projections at various global warming levels, reducing the ranges of projected changes from the multi‐model ensemble. At 3.0°C global warming from pre‐industrial, the multi‐model ensemble projects occurrences of HX‐6, TX‐6, HX95, and TX95 over WNA that exceed 2021 levels to occur every 3.9, 1.7, 1.4, and 2.2 years, respectively, increasing to almost annually at 4.0°C.

Research affirms that survivors of post-traumatic stress disorder (PTSD) experience psychological distress that affects their romantic partners, and that a bi-directional effect between PTSD symptoms and romantic relationship satisfaction exists, indicating that improvements in the romantic relationship may lead to the improved well-being of the survivor. Indeed, as romantic partners of PTSD survivors are both negatively impacted by the distress of the survivor, and romantic relationship satisfaction can affect the distress of the PTSD survivor, partners are a key stakeholder for mental health. Unfortunately, theoretical models have not adequately captured the experience of this population to properly illuminate their experience and provide appropriate treatment directives. This paper examines the informal caregiving integrative model to determine its applicability to the romantic partners of PTSD survivors with respect to the determinants, mediators, and outcomes. The current literature on romantic partners is used to evaluate the adequacy of fit, as well as to provide the components unique to partners. Future directions, clinical implications, and limitations of current research are explored based on the results of this review.

About one-fourth of patients with pancreatic ductal adenocarcinoma (PDAC) are categorized as borderline resectable (BR) or locally advanced (LA). Chemotherapy and radiation therapy have not yielded the anticipated outcomes in curing patients with BR/LA PDAC. The surgical resection of these tumors presents challenges owing to the unpredictability of the resection margin, involvement of vasculature with the tumor, the likelihood of occult metastasis, a higher ratio of positive lymph nodes, and the relatively larger size of tumor nodules. Oncolytic virotherapy has shown promising activity in preclinical PDAC models. Unfortunately, the desmoplastic stroma within the PDAC tumor microenvironment establishes a barrier, hindering the infiltration of oncolytic viruses and various therapeutic drugs—such as antibodies, adoptive cell therapy agents, and chemotherapeutic agents—in reaching the tumor site. Recently, a growing emphasis has been placed on targeting major acellular components of tumor stroma, such as hyaluronic acid and collagen, to enhance drug penetration. Oncolytic viruses can be engineered to express proteolytic enzymes that cleave hyaluronic acid and collagen into smaller polypeptides, thereby softening the desmoplastic stroma, ultimately leading to increased viral distribution along with increased oncolysis and subsequent tumor size regression. This approach may offer new possibilities to improve the resectability of patients diagnosed with BR and LA PDAC.

Abstract While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we describe a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface.

Abstract Cellular organization and functions encompass multiple scales in vivo. Emerging high-plex imaging technologies are limited in resolving subcellular biomolecular features. Expansion Microscopy (ExM) and related techniques physically expand samples for enhanced spatial resolution, but are challenging to be combined with high-plex imaging technologies to enable integrative multiscaled tissue biology insights. Here, we introduce Expand and comPRESS hydrOgels (ExPRESSO), an ExM framework that allows high-plex protein staining, physical expansion, and removal of water, while retaining the lateral tissue expansion. We demonstrate ExPRESSO imaging of archival clinical tissue samples on Multiplexed Ion Beam Imaging and Imaging Mass Cytometry platforms, with detection capabilities of > 40 markers. Application of ExPRESSO on archival human lymphoid and brain tissues resolved tissue architecture at the subcellular level, particularly that of the blood-brain barrier. ExPRESSO hence provides a platform for extending the analysis compatibility of hydrogel-expanded biospecimens to mass spectrometry, with minimal modifications to protocols and instrumentation.

Type 2 diabetes disproportionately affects older adults, persons from racial and ethnic minority groups, and persons of low socioeconomic status. It can be prevented or delayed through evidence-based interventions such as the National Diabetes Prevention Program (National DPP) lifestyle change program (LCP). This analysis is aimed at evaluating the outcomes (i.e., retention, physical activity, and weight loss) associated with participation in the National DPP LCP by participant characteristics and delivery mode (i.e., in-person, online, distance learning, and combination) using the 2012–2018 Diabetes Prevention Recognition Program (DPRP) data. Across all delivery modes, there were generally no substantial differences in retention between male and female participants, but male participants tended to have higher physical activity and weight loss (e.g., average weight loss for in-person delivery: 5.0% for males and 4.3% for females). Older participants had better retention rates than younger participants in all delivery modes and mostly higher physical activity and weight loss except for distance learning delivery (e.g., average weight loss for in-person delivery: 5.1% for those aged 65+ and 3.3% for those aged 18–34). Among the seven racial and ethnic groups studied, retention was generally highest for non-Hispanic/Latino (NH)-White participants and lowest for Hispanic/Latino participants. Physical activity varied by racial and ethnic groups and delivery mode. NH-White participants generally had the most weight loss except for distance learning delivery, and NH-Black/African American participants had the least (e.g., average weight loss for in-person delivery: 5.1% for NH-White participants, 3.3% for both NH-Black/African American and NH-American Indian/Alaska Native participants, and other racial and ethnic minority groups ranged from 3.4% to 4.9%). Monitoring and identifying disparities across demographics and delivery modes, particularly across multiple racial and ethnic groups, provides information that can be used to improve the implementation of the National DPP LCP by tailoring the intervention to reduce disparities.

Abstract The unprecedented 2021 June‐July heatwave in Western North America resulted in record‐breaking human‐perceived heat stress across the region, measured by the humidex considering both air temperature and humidity. During extended summer (June‐September), both 95th percentiles of daily maximum humidex (HX95) and air temperature (TX95) have increased over the 1940–2022 period, with even faster intensification in the last two decades (2001–2022). HX95 has increased more than TX95 because of the positive monotonic nonlinear relationship between humidex and air temperature at a given level of relative humidity. The Canadian Earth System Model version 5 (CanESM5) projects a larger increase in human‐perceived heat stress than air temperature across the region under low to high emission scenarios (HX95 increases 4.40–7.04°C and TX95 increases 2.92–4.65°C between 1981–2010 and 2041–2060). Moreover, CanESM5 projects significant increases in the frequency of HX and TX conditions that exceed the levels reached in 2021 under intermediate and high emission scenarios.

Background Ovarian cancer (OC), a highly lethal cancer in women, has a 48% 5-year overall survival rate. Prior studies link the presence of IL-17 and Th17 T cells in the tumor microenvironment to improved survival in OC patients. To determine if Th17-inducing vaccines are therapeutically effective in OC, we created a murine model of Th17-inducing dendritic cell (DC) (Th17-DC) vaccination generated by stimulating IL-15 while blocking p38 MAPK in bone marrow-derived DCs, followed by antigen pulsing.Methods ID8 tumor cells were injected intraperitoneally into mice. Mice were treated with Th17-DC or conventional DC (cDC) vaccine alone or with immune checkpoint blockade (ICB). Systemic immunity, tumor associated immunity, tumor size and survival were examined using a variety of experimental strategies.Results Th17-DC vaccines increased Th17 T cells in the tumor microenvironment, reshaped the myeloid microenvironment, and improved mouse survival compared with cDC vaccines. ICB had limited efficacy in OC, but Th17-inducing DC vaccination sensitized it to anti-PD-1 ICB, resulting in durable progression-free survival by overcoming IL-10-mediated resistance. Th17-DC vaccine efficacy, alone or with ICB, was mediated by CD4 T cells, but not CD8 T cells.Conclusions These findings emphasize using biologically relevant immune modifiers, like Th17-DC vaccines, in OC treatment to reshape the tumor microenvironment and enhance clinical responses to ICB therapy.

Globally, wildfires are increasing in extent, frequency, and severity. Although global climate change is a major driver and large-scale governance interventions are essential, focusing on governance at smaller scales is of great importance for fostering resilience to wildfires. Inherent tensions in managing wildfire risk are evident at such scales, as objectives and mandates may conflict, and trade-offs and impacts vary across ecosystems and communities. Our study feeds into debates about how to manage wildfire risk to life and property in a way that does not undermine biodiversity and amenity values in social-ecological systems. Here, we describe a case study where features of adaptive governance emerged organically from a dedicated planning process for wildfire governance in Australia. We found that a governance process that is context specific, allows for dialogue about risk, benefits, and trade-offs, and allows for responsibility and risk to be distributed amongst many different actors, can provide the conditions needed to break down rigidity traps that constrain adaptation. The process enabled actors to question whether the default risk management option (in this case, prescribed burning) is aligned with place-based risks and values so they could make an informed choice, built from their participation in the governance process. Ultimately, the community supported a move away from prescribed burning in favor of other wildfire risk management strategies. We found that the emergent governance system has many features of adaptive governance, even though higher level governance has remained resistant to change. Our study offers positive insights for other governments around the world interested in pursuing alternative strategies to confronting wildfire risk.

Abstract Canada's boreal forests and tundra ecosystems are responding to unprecedented climate change with implications for the global carbon (C) cycle and global climate. However, our ability to model the response of Canada's terrestrial ecosystems to climate change is limited and there has been no comprehensive, process‐based assessment of Canada's terrestrial C cycle. We tailor the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) to Canada and evaluate its C cycling performance against independent reference data. We utilize skill scores to assess model performance against reference data alongside benchmark scores that quantify the level of agreement between the reference data sets to aid in interpretation. Our results demonstrate CLASSIC's sensitivity to prescribed vegetation cover. They also show that the addition of five region‐specific Plant functional types (PFTs) improves CLASSIC's skill at simulating the Canadian C cycle. CLASSIC performs well when tailored to Canada, falls within the range of the reference data sets, and meets or exceeds the benchmark scores for most C cycling processes. New region‐specific land cover products, well‐informed PFT parameterizations, and more detailed reference data sets will facilitate improvements to the representation of the terrestrial C cycle in regional and global land surface models. Incorporating a parameterization for boreal disturbance processes and explicitly representing peatlands and permafrost soils will improve CLASSIC's future performance in Canada and other boreal regions. This is an important step toward a comprehensive process‐based assessment of Canada's terrestrial C cycle and evaluating Canada's net C balance under climate change.

The Centers for Disease Control and Prevention’s Diabetes Prevention Recognition Program (DPRP) has helped organizations deliver the National Diabetes Prevention Program (National DPP) lifestyle change program for over 10 years. Four delivery modes are now approved: in person, online (self-paced, asynchronous delivery), distance learning (remote, synchronous delivery), and combination (hybrid delivery using more than one delivery mode). We assessed outcomes using data from 333,715 participants who started the 12-month program between January 1, 2012, and December 31, 2018. The average number of sessions attended was highest for in-person participants (15.0), followed by online (12.9), distance learning (12.2), and combination (10.7). The average number of weeks in the program was highest for in-person participants (28.1), followed by distance learning (20.1), online (18.7), and combination (18.6). The average difference between the first and last reported weekly physical activity minutes reflected an increase for in person (42.0), distance learning (27.1), and combination (15.0), but a decrease for online (-19.8). Among participants retained through session 6 or longer, average weekly physical activity minutes exceeded the program goal of 150 for all delivery modes. Average weight loss (percent of body weight) was greater for in person (4.4%) and distance learning (4.7%) than for online (2.6%) or combination (2.9%). Average participant weight loss increased gradually by session for all delivery modes; among participants who remained in the program for 22 sessions, average weight loss exceeded the program goal of 5% for all delivery modes. In summary, if participants stay in the program, most have positive program outcomes regardless of delivery mode; they have some outcome improvement even if they leave early; and their outcomes improve more the longer they stay. This highlights the benefits of better retention and increased enrollment in the National DPP lifestyle change programs, as well as enhancements to online delivery.

Twenty novel Mn, Fe, and Cu complexes of ethylene cross-bridged tetraazamacrocycles with potentially copolymerizable allyl and benzyl pendant arms were synthesized and characterized. Multiple X-ray crystal structures demonstrate the cis-folded pseudo-octahedral geometry forced by the rigidifying ethylene cross-bridge and show that two cis coordination cites are available for interaction with substrate and oxidant. The Cu complexes were used to determine kinetic stability under harsh acidic and high-temperature conditions, which revealed that the cyclam-based ligands provide superior stabilization with half-lives of many minutes or even hours in 5 M HCl at 50–90 °C. Cyclic voltammetry studies of the Fe and Mn complexes reveal reversible redox processes indicating stabilization of Fe2+/Fe3+ and Mn2+/Mn3+/Mn4+ oxidation states, indicating the likelihood of catalytic oxidation for these complexes. Finally, dye-bleaching experiments with methylene blue, methyl orange, and rhodamine B demonstrate efficient catalytic decolorization and allow selection of the most successful monomeric catalysts for copolymerization to produce future heterogeneous water purification materials.

Climate change is rapidly altering composition, structure, and functioning of the boreal biome, across North America often broadly categorized into ecoregions. The resulting complex changes in different ecoregions present a challenge for efforts to accurately simulate carbon dioxide (CO _2 ) and energy exchanges between boreal forests and the atmosphere with terrestrial ecosystem models (TEMs). Eddy covariance measurements provide valuable information for evaluating the performance of TEMs and guiding their development. Here, we compiled a boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from eight black spruce ( Picea mariana )-dominated, mature forest stands. The eight forest stands, located in six boreal ecoregions of North America, differ in stand characteristics, disturbance history, climate, permafrost conditions and soil properties. By compiling various data streams, the benchmarking dataset comprises data to parameterize, force, and evaluate TEMs. Specifically, it includes half-hourly, gap-filled meteorological forcing data, ancillary data essential for model parameterization, and half-hourly, gap-filled or partitioned component flux data on CO _2 (net ecosystem production, gross primary production [GPP], and ecosystem respiration [ER]) and energy (latent [LE] and sensible heat [H]) and their daily aggregates screened based on half-hourly gap-filling quality criteria. We present a case study with the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) to: (1) demonstrate the utility of our dataset to benchmark TEMs and (2) provide guidance for model development and refinement. Model skill was evaluated using several statistical metrics and further examined through the flux responses to their environmental controls. Our results suggest that CLASSIC tended to overestimate GPP and ER among all stands. Model performance regarding the energy fluxes (i.e., LE and H) varied greatly among the stands and exhibited a moderate correlation with latitude. We identified strong relationships between simulated fluxes and their environmental controls except for H, thus highlighting current strengths and limitations of CLASSIC.

This study evaluates and compares historical simulations of daily sea-level pressure circulation types over 6 continental-scale regions (North America, South America, Europe, Africa, East Asia, and Australasia) by 15 pairs of global climate models from modeling centers that contributed to both Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6. Atmospheric circulation classifications are constructed using two different methodologies applied to two reanalyses. Substantial improvements in performance, taking into account internal variability, are found between CMIP5 and CMIP6 for both frequency (24% reduction in global error) and persistence (12% reduction) of circulation types. Improvements between generations are robust to different methodological choices and reference datasets. A modest relationship between model resolution and skill is found. While there is large intra-ensemble spread in performance, the best performing models from CMIP6 exhibit levels of skill close to those from the reanalyses. In general, the latest generation of climate models should provide less biased simulations for use in regional dynamical and statistical downscaling efforts than previous generations.

Internal climate variability (ICV) is one of the major sources of uncertainty in climate projections, yet it is seldom quantified for projections of crop production. Our study focuses on quantifying the uncertainty due to ICV in projections of crop productions in Canada. We utilize climate scenarios from two large ensembles (LEs, CanESM2-LE and CanRCM4-LE with 25 members each) as inputs to the crop models in the Decision Support System for Agrotechnology Transfer. We simulate crop yields for canola, maize and spring wheat under the future climates of four global warming levels. The coefficient of variation (CV) of the projected crop production across the LE members is used to quantify the uncertainty related to ICV and this is compared with the CVs generated using the 20 GCMs in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Crop production in Canada could increase with global warming, e.g. spring wheat production could increase by up to 21% at the warming level of 3.0 °C. The projections often produce larger uncertainty associated with the GCMs than from ICV at all warming levels above 2.0 °C. The results from an asymptotic test for the equality of CVs show a significant difference in CVs of projections of canola production between CanESM2-LE/CanRCM4-LE and CMIP5 for the warming level of 3.0 °C. However, the test results do not indicate a significant difference among the ensembles at all four warming levels for maize and spring wheat. The uncertainty due to ICV is often comparable to that associated with GCMs at the warming level of 1.5 °C, e.g. a CV of 6.0 and 6.4% for CanESM2-LE and CanRCM4-LE and 6.6% for CMIP5 in the projections of spring wheat production. We conclude there is a need to account for uncertainty related to ICV in projections of Canadian crop production, especially at lower warming levels.