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The effect of climate change on the hydro-climatology, particularly the streamflow, of six major Canadian rivers (Mackenzie, Yukon, Columbia, Fraser, Nelson, and St. Lawrence) is investigated by analyzing results from the historical and future simulations (RCP 4.5 and 8.5 scenarios) performed with the Canadian regional climate model (CanRCM4). Streamflow is obtained by routing runoff using river networks at 0.5° resolution. Of these six rivers, the Nelson and St. Lawrence are the most regulated. As a result, the streamflow at the mouth of these rivers shows very little seasonality. Additionally, the Great Lakes significantly dampen the seasonality of streamflow for the St. Lawrence River. Mean annual precipitation (P), evaporation (E), runoff (R), and temperature increase for all six river basins in both future scenarios considered here, and the increases are higher for the more fossil-fuel-intensive RCP 8.5 scenario. The only exception is the Nelson River basin, for which the simulated runoff increases are extremely small. The hydrological response of these rivers to climate warming is characterized by their existing climate states. The northerly Mackenzie and Yukon River basins show a decrease in the evaporation ratio (E/P) and an increase in the runoff ratio (R/P) since the increase in precipitation is more than enough to offset the increase in evaporation associated with increasing temperature. For the southerly Fraser and Columbia River basins, the E/P ratio increases despite an increase in precipitation, and the R/P ratio decreases due to an already milder climate in the northwestern Pacific region. The seasonality of simulated monthly streamflow is also more affected for the southerly Fraser and Columbia rivers than for the northerly Mackenzie and Yukon rivers as snow amounts decrease and snowmelt occurs earlier. The streamflow seasonality for the Mackenzie and Yukon rivers is still dominated by snowmelt at the end of the century, even in the RCP 8.5 scenario. The simulated streamflow regime for the Fraser and Columbia rivers shifts from a snow-dominated to a hybrid or rainfall-dominated regime towards the end of this century in the RCP 8.5 scenario. While we expect the climate change signal from CanRCM4 to be higher than that from other climate models, owing to the higher-than-average climate sensitivity of its parent global climate model, the results presented here provide a consistent overview of hydrological changes across six major Canadian river basins in response to a warmer climate. [ABSTRACT FROM AUTHOR]

Canada has major sources of atmospheric methane (CH4), with the world's second-largest boreal wetland and the world's fourth-largest natural gas production. However, Canada's CH4 emissions remain uncertain among estimates. Better quantification and characterization of Canada's CH4 emissions are critical for climate mitigation strategies. To improve our understanding of Canada's CH4 emissions, we performed an ensemble regional inversion for 2007–2017 constrained with the Environment and Climate Change Canada (ECCC) surface measurement network. The decadal CH4 estimates show no significant trend, unlike some studies that reported long-term trends. The total CH4 estimate is 17.4 (15.3–19.5) TgCH4yr-1 , partitioned into natural and anthropogenic sources at 10.8 (7.5–13.2) and 6.6 (6.2–7.8) TgCH4yr-1 , respectively. The estimated anthropogenic emission is higher than inventories, mainly in western Canada (with the fossil fuel industry). Furthermore, the results reveal notable spatiotemporal characteristics. First, the modelled differences in atmospheric CH4 among the sites show improvement after inversion when compared to observations, implying the CH4 observation differences could help in verifying the inversion results. Second, the seasonal variations show slow onset and a late-summer maximum, indicating wetland CH4 flux has hysteretic dependence on air temperature. Third, the boreal winter natural CH4 emissions, usually treated as negligible, appear quantifiable (≥ 20 % of annual emissions). Understanding winter emission is important for climate prediction, as the winter in Canada is warming faster than the summer. Fourth, the inter-annual variability in estimated CH4 emissions is positively correlated with summer air temperature anomalies. This could enhance Canada's natural CH4 emission in the warming climate. [ABSTRACT FROM AUTHOR]

Terrestrial biosphere models are a key tool in investigating the role played by land surface in the global climate system. However, few models simulate the geographic distribution of biomes dynamically, opting instead to prescribe them using remote sensing products. While prescribing land cover still allows for the simulation of the impacts of climate change on vegetation growth and the impacts of land use change, it prevents the simulation of climate-change-driven biome shifts, with implications for the projection of future terrestrial carbon sink. Here, we isolate the impacts of prescribed vs. dynamic land cover implementations in a terrestrial biosphere model. We first introduce a new framework for evaluating dynamic land cover (i.e., the spatial distribution of plant functional types across the land surface), which can be applied across terrestrial biosphere models alongside standard benchmarking of energy, water, and carbon cycle variables in model intercomparison projects. After validating simulated land cover, we then show that the simulated terrestrial carbon sink differs significantly between simulations with dynamic vs. prescribed land cover for a high-CO 2 future scenario. This is because of important range shifts that are only simulated when dynamic land cover is implemented: tree expansion into the Arctic and Amazonian transition from forest to grassland. In particular, the projected change in net land–atmosphere CO 2 flux at the end of the 21st century is twice as large in simulations with dynamic land cover than in simulations with prescribed land cover. Our results illustrate the importance of climate-change-driven biome shifts for projecting future terrestrial carbon sink. [ABSTRACT FROM AUTHOR]

The effect of climate change is investigated on the hydro-climatology of six major Canadian rivers (Mackenzie, Yukon, Columbia, Fraser, Nelson, and St. Lawrence), in particular streamflow, by analyzing results from the historical and future simulations (RCP 4.5 and 8.5 scenarios) performed with the Canadian regional climate model (CanRCM4). Streamflow is obtained by routing runoff using river networks at 0.5° resolution. Of these six rivers, Nelson and St. Lawrence are the most regulated. As a result, the streamflow at the mouth of these rivers shows very little seasonality. Additionally, the Great Lakes significantly dampen the seasonality of streamflow for the St. Lawrence River. Mean annual precipitation (P), evaporation (E), runoff (R), and temperature increase for all six river basins considered and the increases are higher for the more fossil fuel-intensive RCP 8.5 scenario. The only exception is the Nelson River basin for which the simulated runoff increases are extremely small. The hydrological response of these rivers to climate warming is characterized by their existing climate states. The northerly Mackenzie and Yukon River basins show a decrease in evaporation ratio (E/P) and an increase in runoff ratio (R/P) since the increase in precipitation is more than enough to offset the increase in evaporation associated with increasing temperature. For the southerly Fraser and Columbia River basins, the E/P ratio increases, and the R/P ratio decreases due to an already milder climate in the Pacific north-western region. The seasonality of simulated monthly streamflow is also more affected for the southerly Fraser and Columbia Rivers than for the northerly Mackenzie and Yukon Rivers as snow amounts decrease and snowmelt occurs earlier. The streamflow seasonality for the Mackenzie and Yukon rivers is still dominated by snowmelt at the end of the century even in the RCP 8.5 scenario. The simulated streamflow regime for the Fraser and Columbia Rivers shifts from a snow-dominated to a hybrid/rainfall-dominated regime towards the end of this century in the RCP 8.5 scenario. While we expect the climate change signal from CanRCM4 to be higher than other climate models, owing to the higher-than-average climate sensitivity of its parent global climate model, the results presented here provide a consistent overview of hydrological changes across six major Canadian river basins in response to a warmer climate. [ABSTRACT FROM AUTHOR]

Canada has major sources of atmospheric methane (CH4), with the world second-largest boreal wetland and the world fourth-largest natural gas production. However, Canada's CH4 emissions remain uncertain among estimates. Better quantification and characterization of Canada's CH4 emissions are critical for climate mitigation strategies. To improve our understanding of Canada's CH4 emissions, we performed an ensemble regional inversion (2007-2017) constrained with the Environment and Climate Change Canada (ECCC) surface measurement network. The decadal CH4 estimates show no significant trend, unlike some studies that reported long-term trends. The total CH4 estimate is 17.4 (15.3-19.5) Tg CH4 year-1, partitioned into natural and anthropogenic sources, 10.8 (7.5-13.2) and 6.6 (6.2-7.8) Tg CH4 year-1, respectively. The estimated anthropogenic emission is higher than inventories, mainly in western Canada (with the fossil fuel industry). Furthermore, the results reveal notable spatiotemporal characteristics. First, the modelled gradients of atmospheric CH4 show improvement after inversion when compared to observations, implying the CH4 gradients could help verify the inversion results. Second, the seasonal variations show slow onset and late summer maximum, indicating wetland CH4 flux has hysteretic dependence on air temperature. Third, the boreal winter natural CH4 emissions, usually treated as negligible, appear quantifiable (≥ 20 % of annual emissions). Understanding winter emission is important for climate prediction, as the winter in Canada is warming faster than the summer. Fourth, the inter-annual variability in estimated CH4 emissions is positively correlated with summer air temperature anomalies. This could enhance Canada's natural CH4 emission in the warming climate. [ABSTRACT FROM AUTHOR]

Terrestrial biosphere models are a key tool in investigating the role played by the land surface in the global climate system. However, few models simulate the geographic distribution of biomes dynamically, opting to prescribe them instead using remote sensing products. While prescribing land cover still allows for the simulation of the impacts of climate change on vegetation growth as well as the impacts of land use change, it prevents the simulation of climate change-driven biome shifts, with implications for projecting the future terrestrial carbon sink. Here, we isolate the impacts of prescribed vs. dynamic land cover implementations in a terrestrial biosphere model. We first introduce a framework for evaluating dynamic land cover (i.e., the spatial distribution of plant functional types across the land surface), which can be applied across terrestrial biosphere models alongside standard benchmarking of energy, water, and carbon cycle variables. After establishing confidence in simulated land cover, we then show that the simulated terrestrial carbon sink differs significantly between simulations with dynamic vs. prescribed land cover for a high CO2 future scenario. This is because of important range shifts that are only simulated when dynamic land cover is implemented: tree expansion into the Arctic and Amazonian transition from forest to grassland. In particular, the projected net land-atmosphere CO2 flux at the end of the 21st century is twice as large in simulations with dynamic land cover than in simulations with prescribed land cover. Our results illustrate the importance of climate change-driven biome shifts for projecting the future terrestrial carbon sink. [ABSTRACT FROM AUTHOR]

Quantification of uncertainty in fluxes of energy, water, and CO 2 simulated by land surface models (LSMs) remains a challenge. LSMs are typically driven with, and tuned for, a specified meteorological forcing data set and a specified set of geophysical fields. Here, using two data sets each for meteorological forcing and land cover representation (in which the increase in crop area over the historical period is implemented in the same way), as well as two model structures (with and without coupling of carbon and nitrogen cycles), the uncertainty in simulated results over the historical period is quantified for the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) model. The resulting eight (2×2×2) model simulations are evaluated using an in-house model evaluation framework that uses multiple observation-based data sets for a range of quantities. The simulated area burned, fire CO 2 emissions, soil carbon mass, vegetation carbon mass, runoff, heterotrophic respiration, gross primary productivity, and sensible heat flux show the largest spread across the eight simulations relative to their global ensemble mean values. Simulated net atmosphere–land CO 2 flux, a critical determinant of the performance of LSMs, is found to be largely independent of the simulated pre-industrial vegetation and soil carbon mass, although our framework represents the historical increase in crop area in the same way in both land cover representations. This indicates that models can provide reliable estimates of the strength of the land carbon sink despite some biases in carbon stocks. Results show that evaluating an ensemble of model results against multiple observations disentangles model deficiencies from uncertainties in model inputs, observation-based data, and model configuration. [ABSTRACT FROM AUTHOR]

The prediction of atmospheric CO2 concentrations is limited by the high interannual variability (IAV) of terrestrial gross primary productivity (GPP). However, there are large uncertainties in the drivers of GPP IAV among Earth system models (ESMs). Here, we evaluate the impact of these uncertainties on the predictability of atmospheric CO2 in six ESMs. We use regression analysis to determine the role of environmental drivers on (i) the patterns of GPP IAV, and (ii) the predictability of GPP. There are large uncertainties in the spatial distribution of GPP IAV. Although all ESMs agree on the high IAV in the tropics, several ESMs have unique hotspots of GPP IAV. The main driver of GPP IAV is temperature in the ESMs using the Community Land Model, and soil moisture in IPSL-CM6A-LR and MPI-ESM-LR, revealing underlying differences in the source of GPP IAV among ESMs. Between 13 % and 24 % of the GPP IAV is predictable one year ahead, with four out of six ESMs between 19 % and 24 %. Up to 32 % of the GPP IAV induced by soil moisture is predictable, while only 7 % to 13 % of the GPP IAV induced by radiation. The results show that while ESMs are fairly similar in their ability to predict themselves, their predicted contribution to the atmospheric CO2 variability originates from different regions and is caused by different drivers. A higher coherence in atmospheric CO2 predictability could be achieved by reducing uncertainties of GPP sensitivity to soil moisture, and by accurate observational products for GPP IAV. [ABSTRACT FROM AUTHOR]

Plant functional types (PFTs) are used to represent vegetation distribution in land surface models (LSMs). Large differences are found in the geographical distribution of PFTs currently used in various LSMs. These differences arise from the differences in the underlying land cover products but also the methods used to map or reclassify land cover data to the PFTs that a given LSM represents. There are large uncertainties associated with existing PFT mapping methods since they are largely based on expert judgment and therefore are subjective. In this study, we propose a new approach to inform the mapping or the cross-walking process using analyses from sub-pixel fractional error matrices, which allows for a quantitative assessment of the fractional composition of the land cover categories in a dataset. We use the Climate Change Initiative (CCI) land cover product produced by the European Space Agency (ESA). A previous study has shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an improved land cover distribution compared to that from the GLC2000 dataset currently used in the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs represented in the CLASSIC model. There are large differences between the new PFTs and those currently used in the model. Offline simulations performed with the CLASSIC model using the ESA-CCI based PFTs show improved winter albedo compared to that based on the GLC2000 dataset. This emphasizes the importance of accurate representation of vegetation distribution for realistic simulation of surface albedo in LSMs. Results in this study suggest that the sub-pixel fractional composition analyses are an effective way to reduce uncertainties in the PFT mapping process and therefore, to some extent, objectify the otherwise subjective process. [ABSTRACT FROM AUTHOR]

The observed global net land carbon sink is captured by current land models. All models agree that atmospheric CO2 and nitrogen deposition driven gains in carbon stocks are partially offset by climate and land-use and land-cover change (LULCC) losses. However, there is a lack of consensus in the partitioning of the sink between vegetation and soil, where models do not even agree on the direction of change in carbon stocks over the past 60 years. This uncertainty is driven by plant productivity, allocation, and turnover response to atmospheric CO2 (and to a smaller extent to LULCC), and the response of soil to LULCC (and to a lesser extent climate). Overall, differences in turnover explain ~70% of model spread in both vegetation and soil carbon changes. Further analysis of internal plant and soil (individual pools) cycling is needed to reduce uncertainty in the controlling processes behind the global land carbon sink. The global net land sink is relatively well constrained. However, the responsible drivers and above/below-ground partitioning are highly uncertain. Model issues regarding turnover of individual plant and soil components are responsible. [ABSTRACT FROM AUTHOR]

Quantification of uncertainty in fluxes of energy, water, and CO2 simulated by land surface models (LSMs) remains a challenge. LSMs are typically driven with, and tuned for, a specified meteorological forcing data set and a specified set of geophysical fields. Here, using two data sets each for meteorological forcing and historical land cover reconstruction, as well as two model structures (with and without coupling of carbon and nitrogen cycles), the uncertainty in simulated results over the historical period is quantified for the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) model. The resulting eight (2 x 2 x 2) equally probable model simulations are evaluated using an in-house model evaluation framework that uses multiple observations-based data sets for a range of quantities. Among the primary global energy, water, and carbon related fluxes and state variables, simulated area burned, fire CO2 emissions, soil carbon mass, vegetation biomass, runoff, heterotrophic respiration, gross primary productivity, and sensible heat flux show the largest spread across the eight simulations relative to their mean. Simulated net atmosphere-land CO2 flux, which is considered a critical determinant of the performance of LSMs, is found to be largely independent of the simulated pre-industrial vegetation and soil carbon mass. This indicates that models can provide reliable estimates of the strength of the land carbon sink despite biases in carbon stocks. Results show that evaluating an ensemble of model results against multiple observations allows to disentangle model deficiencies from uncertainties in model inputs, observation-based data, and model configuration. [ABSTRACT FROM AUTHOR]

Quantification of uncertainty in fluxes of energy, water, and CO2 simulated by land surface models (LSMs) remains a challenge. LSMs are typically driven with, and tuned for, a specified meteorological forcing data set and a specified set of geophysical fields. Here, using two data sets each for meteorological forcing and historical land cover reconstruction, as well as two model structures (with and without coupling of carbon and nitrogen cycles), the uncertainty in simulated results over the historical period is quantified for the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) model. The resulting eight (2 x 2 x 2) equally probable model simulations are evaluated using an in-house model evaluation framework that uses multiple observations-based data sets for a range of quantities. Among the primary global energy, water, and carbon related fluxes and state variables, simulated area burned, fire CO2 emissions, soil carbon mass, vegetation biomass, runoff, heterotrophic respiration, gross primary productivity, and sensible heat flux show the largest spread across the eight simulations relative to their mean. Simulated net atmosphere-land CO2 flux, which is considered a critical determinant of the performance of LSMs, is found to be largely independent of the simulated pre-industrial vegetation and soil carbon mass. This indicates that models can provide reliable estimates of the strength of the land carbon sink despite biases in carbon stocks. Results show that evaluating an ensemble of model results against multiple observations allows to disentangle model deficiencies from uncertainties in model inputs, observation-based data, and model configuration. [ABSTRACT FROM AUTHOR]

To explore mechanisms of response to combined PD-1/CTLA-4 immune checkpoint blockade (ICB) treatment in individual cell types, we generated scRNA-seq using a mouse model of invasive urothelial carcinoma with three conditions: untreated tumor, treated tumor, and tumor treated after CD4+ T cell depletion. After classifying tumor cells based on detection of somatic variants and assigning non-tumor cell types using SingleR, we performed differential expression analysis, overrepresentation analysis, and gene set enrichment analysis (GSEA) within each cell type. GSEA revealed that endothelial cells were enriched for upregulated IFN-g response genes when comparing treated cells to both untreated cells and cells treated after CD4+ T cell depletion. Functional analysis showed that knocking out IFNgR1 in endothelial cells inhibited treatment response. Together, these results indicated that IFN-g signaling in endothelial cells is a key mediator of ICB induced anti-tumor activity., Competing Interests: Declaration of interests V.K.A. currently serves as an employee of Bristol Myers Squibb and has stock options in the company. J.K.B currently serves as an employee of Pfizer Inc.