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Weighting models according to their performance has been used to produce multimodel climate change projections. But the added value of model weighting for future projection is not always examined. Here we apply an imperfect model framework to evaluate the added value of model weighting in projecting summer temperature changes over China. Members of large-ensemble simulations by three climate models of different climate sensitivities are used as pseudo-observations for the past and the future. Performance of the models participating in the phase 6 of the Coupled Model Intercomparison Project (CMIP6) are evaluated against the pseudo-observations based on simulated historical climatology and trends in global, regional, and local temperatures to determine the model weights for future projection. The weighted projections are then compared with the pseudo-observations in the future period. We find that regional trend as a metric of model performance yields generally better skill for future projection, while past climatology as performance metric does not lead to a significant improvement to projection. Trend at the grid-box scale is also not a good performance indicator as small-scale trend is highly uncertain. For the model weighting to be effective, the metric for evaluating the model’s performance must be relatable to future changes, with the response signal separable from internal variability. Projected summer warming based on model weighting is similar to that of unweighted projection but the 5th–95th-percentile uncertainty range of the weighted projection is 38% smaller with the reduction mainly in the upper bound, with the largest reduction appearing in southeast China.

Water temperature is a key variable affecting fish habitat in rivers. The Sockeye salmon (Oncorhynchus nerka), a keystone species in north western aquatic ecosystems of North America, is profoundly affected by thermal regime changes in rivers, and it holds a pivotal role in ecological and economic contexts due to its life history, extensive distribution, and commercial fishery. In this study, we explore the effects of climate change on the thermal regime of the Nechako River (British Columbia, Canada), a relatively large river partially controlled by the Skins Lake Spillway. The CEQUEAU hydrological-thermal model was calibrated using discharge and water temperature observations. The model was forced using the Fifth generation of ECMWF Atmospheric Reanalysis data for the past and meteorological projections (downscaled and bias-corrected) from climate models for future scenarios. Hydrological calibration was completed for the 1980–2019 period using data from two hydrometric stations, and water temperature calibration was implemented using observations for 2005–2019 from eight water temperature stations. Changes in water temperature were assessed for two future periods (2040–2069 and 2070–2099) using eight Coupled Model Intercomparison Project Phase 6 climate models and using two Shared Socioeconomic Pathway scenarios (4.5 and 8.5 W/m2 by 2100) for each period. Results show that water temperatures above 20°C (an upper threshold for adequate thermal habitat for Sockeye salmon migration in this river) at the Vanderhoof station will increase in daily frequency. While the frequency of occurrence of this phenomenon is 1% (0–9 days/summer) based on 2005–2019 observations, this number range is 3.8–36% (0–62 days/summer) according to the ensemble of climate change scenarios. These results show the decreasing habitat availability for Sockeye salmon due to climate change and the importance of water management in addressing this issue.

The generalized extreme value (GEV) regression provides a framework for modeling extreme events across various fields by incorporating covariates into the location parameter of GEV distributions. When the covariates are subject to errors-in-variables (EIV) or measurement error, ignoring the EIVs leads to biased estimation and degraded inferences. This problem arises in detection and attribution analyses of changes in climate extremes because the covariates are estimated with uncertainty. It has not been studied even for the case of independent EIVs, let alone the case of dependent EIVs, due to the complex structure of GEV. Here we propose a general Monte Carlo corrected score method and extend it to address temporally correlated EIVs in GEV modeling with application to the detection and attribution analyses for climate extremes. Through extensive simulation studies, the proposed method provides an unbiased estimator and valid inference. In the application to the detection and attribution analyses of temperature extremes in central regions of China, with the proposed method, the combined anthropogenic and natural signal is detected in the change in the annual minimum of daily maximum and the annual minimum of daily minimum.

Coastal convection is often organized into multiple mesoscale systems that propagate in either direction across the coastline (i.e., landward and oceanward). These systems interact non-trivially with synoptic and intraseasonal disturbances such as convectively coupled waves and the Madden–Julian oscillation. Despite numerous theoretical and observational efforts to understand coastal convection, global climate models still fail to represent it adequately, mainly because of limitations in spatial resolution and shortcomings in the underlying cumulus parameterization schemes. Here, we use a simplified climate model of intermediate complexity to simulate coastal convection under the influence of the diurnal cycle of solar heating. Convection is parameterized via a stochastic multicloud model (SMCM), which mimics the subgrid dynamics of organized convection due to interactions (through the environment) between the cloud types that characterize organized tropical convection. Numerical results demonstrate that the model is able to capture the key modes of coastal convection variability, such as the diurnal cycle of convection and the accompanying sea and land breeze reversals, the slowly propagating mesoscale convective systems that move from land to ocean and vice-versa, and numerous moisture-coupled gravity wave modes. The physical features of the simulated modes, such as their propagation speeds, the timing of rainfall peaks, the penetration of the sea and land breezes, and how they are affected by the latitudinal variation in the Coriolis force, are generally consistent with existing theoretical and observational studies.

Recently, concerns have arisen as to whether temperature-based proxy methods used to estimate potential evapotranspiration (PET) are reliable when examining future drought severity, especially in the context of a warmer climate. The objective of this study was to assess the effect of different PET approaches, focusing on proxies for radiation and humidity, on future Standardized Precipitation Evapotranspiration Index (SPEI) calculations across Canada. Using output from 22 CMIP6 global climate models (GCMs), seasonal and annual SPEI comparisons were carried out between the physically-based Penman-Monteith (PM) method and two approaches that incorporate temperature proxies to calculate radiation and/or humidity. These included the temperature-based Hargreaves (HG) approach and a PM method with derived humidity (PM-m). Results revealed that although the general patterns of SPEI projections across Canada were consistent among the methods, notable spatial and temporal differences were apparent. Specifically, both median and extreme SPEI projections based on the two temperature proxy methods revealed less annual and summer drying in much of central, eastern, and northern regions of Canada when compared to the physically based SPEI-PM. In extreme western regions (British Columbia, Yukon) these two methods, particularly HG, projected drier conditions. Differences of using temperature derived radiation and humidity were also most apparent in spring (and to a lesser degree, autumn), where the HG approach overestimated spring drying (and autumn wetting) over large regions of the country. Overall, differences tended to be more pronounced for the fully temperature-based HG approach during all periods considered. Results from this study strongly suggest that when possible, a physically-based approach be used when estimating PET to assess future drought projections. If a temperature proxy is used, the differences to a physically-based method should be understood and resultant implications be evaluated.

Highlights:
Improve the estimation of water temperature extremes at ungauged sites.
Incorporate non-linearities in the homogenous region delineation step using NLCCA.
Consider non-linear models in the whole estimation procedure (NLCCA + GAM).
Compare fully and partially non-linear approaches for water temperature regionalization.
The results underline the importance of considering the non-linearity of thermal processes.

El Niño–Southern Oscillation (ENSO) has a profound influence on the occurrence of extreme precipitation events at local and regional scales in the present-day climate, and thus it is important to understand how that influence may change under future global warming. We consider this question using the large-ensemble simulations of CESM2, which simulates ENSO well historically. CESM2 projects that the influence of ENSO on extreme precipitation will strengthen further under the SSP3–7.0 scenario in most regions whose extreme precipitation regimes are strongly affected by ENSO in the boreal cold season. Extreme precipitation in the boreal cold season that exceeds historical thresholds is projected to become more common throughout the ENSO cycle. The difference in the intensity of extreme precipitation events that occur under El Niño and La Niña conditions will increase, resulting in “more extreme and more variable hydroclimate extremes.” We also consider the processes that affect the future intensity of extreme precipitation and how it varies with the ENSO cycle by partitioning changes into thermodynamic and dynamic components. The thermodynamic component, which reflects increases in atmospheric moisture content, results in a relatively uniform intensification of ENSO-driven extreme precipitation variation. In contrast, the dynamic component, which reflects changes in vertical motion, produces a strong regional difference in the response to forcing. In some regions, this component amplifies the thermodynamic-induced changes, while in others, it offsets them or even results in reduction in extreme precipitation variation.

Study region:
Nechako Reservoir, British Columbia, Canada.

Study focus:
Hydrological regulation affect both hydrological and thermal conditions in the reservoir and downstream reach, subsequently disrupting fish habitats. This paper aims at developing an integrated model simulating physical processes that govern the quantity and quality of inflow, reservoir, and outflow water of the Nechako Reservoir. Such a model would help stakeholders understand the response of in-reservoir water temperature stratification and downstream water temperature to changes in inflow and reservoir operation under future climate change.

New hydrological insights for the region:
The model was calibrated against historical reservoir levels and in-reservoir and outlet water temperature field data. The integrated model simulated accurately the wide variation of reservoir levels as well as the in-reservoir water temperature at Kenney Dam and the outlet temperature. Sensitivity analysis shows that reservoir water temperature particularly the epilimnion is sensitive to changes in both meteorological and hydrological forcing. Forcing the model with different outflow scenarios shows the weak sensitivity of temperature of water released to outflow rates. Given epilimnion water releases at the spillway, the Summer Temperature Management Program could be inefficient to provide cool water in the Nechako River during the critical period of salmon migration in a warming climate. However, colder water remains available at depth at Kenney Dam to potentially mitigate and better control downstream water temperature.

As glaciers across the world continue to recede, there is a concern that their loss as a fresh water reservoir within mountainous basins will have a negative impact on stream temperatures and downstream water resources. Currently, there are relatively few glacio-hydrological models (GHMs) appropriate to study such phenomena and studies that have used GHMs generally acknowledge the high uncertainty associated with their simulations. Calibration techniques present a particular issue in GHMs as available glacier observations are limited and errors in the glacierized portion of a basin can be compensated by errors in the non-glacierized portion. Using as a study site the Cheakamus Basin in British Columbia, Canada, we 1) present a new, fully-coupled GHM, 2) analyze the effects different calibration techniques have on the model’s summer streamflow projections, and 3) compare the fully-coupled GHM results to projections using a one-way GHM. The calibration techniques studied vary in terms of glacier representation (dynamic/static), and glacier constraint (mass balance/thinning rates/thinning rates and area change). We find projected future climate forcings are sufficiently strong in the Cheakamus Basin so as to generally make the sign and significance of changes to the basin’s hydrology insensitive to the calibration and projection procedures studied. However, the variation among these procedures produces significant changes in the projected magnitude of future hydrological changes and therefore should be carefully considered in studies where precision beyond the sign and significance of change is required. Based on analysis of the variation within each procedure’s set of model outputs, we conclude 1) the two-way GHM has benefits over the one-way model, 2) calibration using dynamic glaciers and a thinning rate constraint is preferable for the new GHM, and 3) there is a need for additional studies on the uncertainties associated with the calibration of glacio-hydrological models.