[1] Current climate model projections are uncertain. This uncertainty is partly driven by the uncertainty in key model parameters such as climate sensitivity (CS), vertical ocean diffusivity (K v ), and strength of anthropogenic sulfate aerosol forcing. These parameters are commonly estimated using ensembles of model runs constrained by observations. Here we obtain a probability density function (pdf) of these parameters using the University of Victoria Earth System Climate Model (UVic ESCM) -an intermediate complexity model with a dynamic three-dimensional ocean. Specifically, we run an ensemble of UVic ESCM runs varying parameters that affect CS, ocean vertical diffusion, and the effects of anthropogenic sulfate aerosols. We use a statistical emulator that interpolates the UVic ESCM output to parameter settings where the model was not evaluated. We adopt a Bayesian approach to constrain the model output with instrumental surface temperature and ocean heat observations. Our approach accounts for the uncertainties in the properties of model-data residuals. We use a Markov chain Monte Carlo method to obtain a posterior pdf of these parameters. The mode of the climate sensitivity estimate is 2.8°C, with the corresponding 95% credible interval ranging from 1.8 to 4.9°C. These results are generally consistent with previous studies. The CS pdf is sensitive to the assumptions about the priors, to the effects of anthropogenic sulfate aerosols, and to the background vertical ocean diffusivity. Our method can be used with more complex climate models.

How will the climate system respond to anthropogenic forcings? One approach to this question relies on climate model projections. Current climate projections are considerably uncertain. Characterizing and, if possible, reducing this uncertainty is an area of ongoing research. We consider the problem of making projections of the North Atlantic meridional overturning circulation (AMOC). Uncertainties about climate model parameters play a key role in uncertainties in AMOC projections. When the observational data and the climate model output are high-dimensional spatial data sets, the data are typically aggregated due to computational constraints. The effects of aggregation are unclear because statistically rigorous approaches for model parameter inference have been infeasible for high-resolution data. Here we develop a flexible and computationally efficient approach using principal components and basis expansions to study the effect of spatial data aggregation on parametric and projection uncertainties. Our Bayesian reduced-dimensional calibration approach allows us to study the effect of complicated error structures and data-model discrepancies on our ability to learn about climate model parameters from high-dimensional data. Considering high-dimensional spatial observations reduces the effect of deep uncertainty associated with prior specifications for the data-model discrepancy. Also, using the unaggregated data results in sharper projections based on our climate model. Our computationally efficient approach may be widely applicable to a variety of high-dimensional computer model calibration problems.

NARCliM (New South Wales and Australian Capital Territory Regional Climate Modelling project) is a climate downscaling project for Australia and the surrounding regions. Present and future climate simulations are performed using a 1-way nested dynamical downscaling approach and span 2 domains. We focus on the inner 10 km domain that extends across southeast Australia. Three regional climate models (RCMs) based on the Weather Research and Forecasting System (WRF) version 3.3 dynamically downscale 4 global climate model (GCM) simulations to finer resolutions. This project complements and improves on already available GCM projections for the region. Our simulations cover 3 epochs: present (1990−2009), near future (2020− 2039), and far future (2060−2079). Here, we focus on the mean surface air temperature and precipitation. The RCMs are better able to capture spatial patterns of temperature and precipitation and improve the temperature root mean square error (RMSE) compared to the GCMs, at least for the inner domain. The RCMs tend to be biased cold compared to observations and are wetter than the GCMs during warm seasons. The downscaled RCM projections exhibit a weaker warming over land compared to the GCMs. The RCMs project no significant precipitation changes in the far future over most areas. However, Victoria is expected to see significant springtime drying of 15 mm mo −1 , which is considerably higher than previous GCM results. This drying is associated with a larger strengthening of the subtropical ridge than modelled previously by the GCMs. In addition, the RCMs project significant precipitation changes in contradicting directions for some inland areas during winter.

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