Accurately estimating reservoir parameters from geophysical data is vitally important in hydrocarbon exploration and production. We have developed a new joint-inversion algorithm to estimate reservoir parameters directly, using both seismic amplitude variation with angle of incidence (AVA) data and marine controlled-source electromagnetic (CSEM) data. Reservoir parameters are linked to geophysical parameters through a rock-properties model. Errors in the parameters of the rock-properties model introduce errors of comparable size in the reservoir-parameter estimates produced by joint inversion. Tests of joint inversion on synthetic 1D models demonstrate improved fluid saturation and porosity estimates for joint AVA-CSEM data inversion (compared with estimates from AVA or CSEM inversion alone). A comparison of inversions of AVA data, CSEM data, and joint AVA-CSEM data over the North Sea Troll field, at a location for which we have well control, shows that the joint inversion produces estimates of gas saturation, oil saturation, and porosity that are closest (as measured by the rms difference, the L1 norm of the difference, and net values over the interval) to the logged values. However, CSEM-only inversion provides the closest estimates of water saturation.

We investigate the sensitivity of precipitation characteristics (mean, extreme, and diurnal cycle) to a set of uncertain parameters that influence the qualitative and quantitative behavior of cloud and aerosol processes in the Community Atmosphere Model (CAM5). We adopt both the Latin hypercube and Quasi‐Monte Carlo sampling approaches to effectively explore the high‐dimensional parameter space and then conduct two large sets of simulations. One set consists of 1100 simulations (cloud ensemble) perturbing 22 parameters related to cloud physics and convection, and the other set consists of 256 simulations (aerosol ensemble) focusing on 16 parameters related to aerosols and cloud microphysics. In the cloud ensemble, six parameters having the greatest influences on the global mean precipitation are identified, three of which (related to the deep convection scheme) are the primary contributors to the total variance of the phase and amplitude of the precipitation diurnal cycle over land. The extreme precipitation characteristics are sensitive to a fewer number of parameters. Precipitation does not always respond monotonically to parameter change. The influence of individual parameters does not depend on the sampling approaches or concomitant parameters selected. Generally, the Generalized Linear Model is able to explain more of the parametric sensitivity of global precipitation than local or regional features. The total explained variance for precipitation is primarily due to contributions from the individual parameters (75–90% in total). The total variance shows a significant seasonal variability in midlatitude continental regions, but very small in tropical continental regions.

The atmospheric component of Energy Exascale Earth System Model version 1 has included many new features in the physics parameterizations compared to its predecessors. Potential complex nonlinear interactions among the new features create a significant challenge for understanding the model behaviors and parameter tuning. Using the one‐at‐a‐time method, the benefit of tuning one parameter may offset the benefit of tuning another parameter, or improvement in one target variable may lead to degradation in another target variable. To better understand the Energy Exascale Earth System Model version 1 model behaviors and physics, we conducted a large number of short simulations (three days) in which 18 parameters carefully selected from parameterizations of deep convection, shallow convection, and cloud macrophysics and microphysics were perturbed simultaneously using the Latin hypercube sampling method. From the perturbed parameter ensemble simulations and use of different skill score functions, we identified the most sensitive parameters, quantified how the model responds to changes of the parameters for both global mean and spatial distribution, and estimated the maximum likelihood of model parameter space for a number of important fidelity metrics. Comparison of the parametric sensitivity using simulations of two different lengths suggests that perturbed parameter ensemble using short simulations has some bearing on understanding parametric sensitivity of longer simulations. Results from this analysis provide a more comprehensive picture of the Energy Exascale Earth System Model version 1 behavior. The difficulty in reducing biases in multiple variables simultaneously highlights the need of characterizing model structural uncertainty (so‐called embedded errors) to inform future development efforts.

In snow‐dominated regions, a key source of uncertainty in hydrologic prediction and forecasting is the magnitude and distribution of snow water equivalent (SWE). With ensemble simulations, this work demonstrates that SWE variability across the mountain ranges of the western United States (represented by 246 Snow Telemetry stations) can largely be captured at the daily time scale by a simple mass and energy‐balance snow model with four physically reasonable parameters—three snow albedo parameters and one snow temperature threshold for precipitation partitioning. The model skill is lower in the maritime Pacific Northwest where SWE variability is more sensitive to errors associated with simulated energy balance (e.g., downward radiation fluxes) and the temperature‐only precipitation partitioning approach. Poor model skill in high‐altitude, windy locations in the Northern Rockies can be attributed to precipitation undercatch and underrepresented wind processes. For the purpose of large‐domain hydrologic applications, regional snow parameters were developed for eight ecoregions characterized by a distinct hydroclimatic regime across the western United States. Results suggest that regionally coherent snow parameterizations are able to capture daily variations in SWE at most Snow Telemetry stations, suggesting that areas with a similar hydroclimate share a similar snow regime. While the three albedo parameters show limited spatial variability across all regions, the regional snow temperature threshold (T_s) shows marked spatial variation correlated with relative humidity; the T_s values increase from 0.2 °C in the higher‐humidity Pacific Northwest to 4.0 °C in the colder, lower‐humidity Rocky Mountains.