Constraining the response of the hydrological cycle, land surface and regional weather to global change (HYDRA)

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The IPCC's CMIP-3 and CMIP-5 model inter-comparisons focus on uncertainty in the large-scale temperature response of the coupled atmosphere-ocean system to a given emissions or concentration scenario, treating uncertainty in downscaling, in the hydrological cycle and in the biosphere as additional sources of error. This fully coupled approach only permits relatively coarse-resolution global models and limited ensemble sizes, with large systematic errors and low signal-to-noise in hydrological variables, problems which persist in initialized decadal forecasts. As a result, precipitation observations play only a minor role in constraining precipitation forecasts, resulting in large and potentially unphysical ranges of uncertainty on high-impact variables such as the frequency of occurrence of extreme rainfall. Hence CMIP-style simulations have been found to be of limited use by what should be one of our key stakeholders, insurance risk modeling. Much of this uncertainty may be unnecessary since we know how sea surface temperatures and ice cover (SSTICs) have evolved over the past few decades and how the hydrological cycle has responded, so we should be using this information directly to constrain atmospheric and land-surface parameters. Moreover, all studies suggest a limited range of degrees of freedom in the large-scale externally-driven SSTIC change over the next few decades. Hence a powerful complementary approach to the CMIP 'emissions scenario driven' paradigm is the 'temperature scenario driven' approach under which a range of large-scale SSTIC changes are used to drive higher-resolution models, either directly or by relaxation in a simple model of the ocean mixed layer. This allows much larger ensembles and reduced bias over the recent observational period, providing a more systematic exploration of uncertainty in the atmospheric, hydrological and land-surface response. Detailed comparison with observations for the same years, including satellite-derived top-of-atmosphere fluxes, should allow much tighter constraints to be placed on atmospheric and land-surface parameters than is possible when coupled models are run free and comparisons are restricted to large-scale climatology and recent trends. A key challenge in quantitative comparison of simulated precipitation trends with observations is systematic biases in the location of precipitation features such as convergence zones. We will address this using image-warping techniques developed for neuro-imaging which have been demonstrated on a pilot scale to correct feature-location biases in climate models. These will also provide a powerful tool to detect externally-driven shifts in feature location, such as an expansion of the Hadley circulation. We will run large ensembles of global atmospheric/land-surface models driven with observed SSTICs over the past 60 years together with projected changes to 2040 derived from a broad range of sources, including CMIP-3, CMIP-5 and the UKCP09 and climateprediction.net perturbed physics ensembles. Repeat simulations of the past 60 years with the estimated signature of anthropogenic influence removed will be used to address how far recent observed changes in precipitation, land-surface variables and run-off can be attributed to human influence. Multi-thousand-member ensembles will allow the distribution of hydrological and land-surface variables to be mapped in detail, largely eliminating stochastic uncertainty from predictions of their underlying statistical moments. A representative subset of these simulations will be used to drive nested regional models over Europe, and the output used to drive run-off models to evaluate their utility for flood and drought risk modeling. The modeling framework will be made available to international partners to address other regions. All simulations will be performed using climateprediction.net public resource distributed computing, minimizing both their cost and environmental impact.