Probabilistic Evaluation of Drought in CMIP6 Simulations

As droughts have widespread social and ecological impacts, it is critical to develop long-term adaptation and mitigation strategies to reduce drought vulnerability. Climate models are important in quantifying drought changes. Here, we assess the ability of 285 CMIP6 historical simulations, from 17 models, to reproduce drought duration and severity in three observational data sets using the Standardized Precipitation Index (SPI). We used summary statistics beyond the mean and standard deviation, and devised a novel probabilistic framework, based on the Hellinger distance, to quantify the difference between observed and simulated drought characteristics. Results show that many simulations have less than error in reproducing the observed drought summary statistics. The hypothesis that simulations and observations are described by the same distribution cannot be rejected for more than of the grids based on our distance framework. No single model stood out as demonstrating consistently better performance over large regions of the globe. The variance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zones. Though the models capture the characteristics of dry spells well, there is considerable bias in low precipitation values. Good model performance in terms of SPI does not imply good performance in simulating low precipitation. Our study emphasizes the need to probabilistically evaluate climate model simulations in order to both pinpoint model weaknesses and identify a subset of best-performing models that are useful for impact assessments

The Perils of Regridding: Examples Using a Global Precipitation Dataset

Canada First Research Excellence Fund’s Global Water Futures program, the Natural Sciences and Engineering Research Council of Canada, the Canada Research Chairs program, and the Pacific Institute for Mathematical StudiesPeer ReviewedGridded precipitation datasets are used in many applications such as the analysis of climate variability/change and hydrological modeling. Regridding precipitation datasets is common for model coupling (e.g., coupling atmospheric and hydrological models) or comparing different models and datasets. However, regridding can considerably alter precipitation statistics. In this global analysis, the effects of regridding a precipitation dataset are emphasized using three regridding methods (first-order conservative, bilinear, and distance-weighted averaging). The differences between the original and regridded dataset are substantial and greatest at high quantiles. Differences of 46 and 0.13 mm are noted in high (0.95) and low (0.05) quantiles, respectively. The impacts of regridding vary spatially for land and oceanic regions; there are substantial differences at high quantiles in tropical land regions, and at low quantiles in polar regions. These impacts are approximately the same for different regridding methods. The differences increase with the size of the grid at higher quantiles and vice versa for low quantiles. As the grid resolution increases, the difference between original and regridded data declines, yet the shift size dominates for high quantiles for which the differences are higher. While regridding is often necessary to use gridded precipitation datasets, it should be used with great caution for fine resolutions (e.g., daily and subdaily), because it can severely alter the statistical properties of precipitation, specifically at high and low quantiles

Final Report developed under Contract #3000704047 for Natural Resources Canada

Natural Resources CanadaNon-Peer ReviewedIn the recent decades, precipitation patterns and corresponding streamflow responses in many cold regions catchments have changed considerably due to warming. Understanding historical changes and predicting future responses are of great importance for planning and management of water resources systems. Regional climate simulations using convention- permitting models are helpful in representing the fine-scale cloud and mesoscale processes, which are critical for understanding the physical mechanisms that cause in convective precipitation. From a hydrological perspective, these fine resolution simulations are helpful in understanding the runoff generation mechanisms, particularly for mountainous watersheds, which have high spatial variation in precipitation due to large differences in elevation over small distances. The sister-study of this report, the Bow River Basin Study (BRBS), used a physically based hydrological land surface scheme along with a water management model, coupled with a high resolution convention- permitting atmospheric regional model (Weather Research and Forecasting, WRF) to understand the streamflow generating mechanisms and identify the changes in streamflow responses of the Bow and Elbow River Basins. The coupled model appears to provide a large improvement in predictability, with minimal calibration of parameters and without bias correction of forcing from the atmospheric model. The model4 was able to provide reliable estimates of streamflows, despite the complex topography in the catchment. Using the WRF Pseudo Global Warming (PGW) scenario, estimated future streamflows simulated were then used to develop projected flow exceedance curves. The uncertainty in the simulations is extremely helpful in the risk assessment for downstream flood inundations. However, the uncertainty in streamflows cannot be assessed as the WRF- PGW dataset was only available for a single realization, because of the high computational cost. The research presented in this report focusses instead on using the highly efficient hydrological model developed and verified in BRBS whilst assessing uncertainty using another regional climate model, the CanRCM4, where many realizations are available for different boundary conditions. Since the CanRCM4 simulations have a relatively low resolution, a novel methodology was developed to adjust regional climate model outputs using the WRF-PGW data. An ensemble of 15 CanRCM4 simulations was used to force the Bow River basin model to determine a measure of the uncertainty in the simulated streamflows, and the projected streamflow exceedance probability curves. These curves are extremely useful for risk assessment for downstream flood inundations. Given the importance of understanding how much extreme precipitation will change in urban areas of the basin, where short duration high intensity events cause flash flooding, frequency analysis of these events was carried out for Calgary and Intensity Duration Frequency (IDF) curves were developed. A ready-to-use empirical form of IDF curve has been proposed from this analysis for the City of Calgary. The results from the WRF-PGW modelling indicated that future high flow, low frequency (exceedances less than 10%) streamflow events will decrease compared to those under the current climate condition by 4, 9 and 1.6 m3/s for the Bow River at Banff and Calgary and Elbow River at Sarcee Bridge respectively. The average of the 15 new CanRCM4-WRF-PGW results supports the above result with some greater decreases in streamflow of 9, 16 and 4 m3/s for Bow River at Banff and Calgary and Elbow River at Sarcee Bridge respectively. However, there were some CanRCM4-WRF-PGW realisations that suggested substantial increases in future low frequency streamflow from those indicated by the average CanRCM4- WRF-PGW-drive MESH model. The below average, high frequency (exceedances greater than 30%) future streamflows will increase modestly in all gauging locations by from 1 to 12.5 m3/s. The results of the extreme precipitation analysis at Calgary indicated an increase in future extreme precipitation events of all duration and return periods. On an average an increase of 1.5 times is noted for short return periods (=2, 5), and an increase of 4 times for long return periods (=500, 1000)

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies