Chris Jack

Emerging patterns of simulated regional climatic changes for the 21st century due to anthropogenic forcings

We analyse temperature and precipitation changes for the late decades of the 21st century (with respect to present day conditions) over 23 land regions of the world from 18 recent transient climate change experiments with coupled atmosphere-ocean General Circulation Models (AOGCMs). The analysis involves two different forcing scenarios and nine models, and it focuses on model agreement in the simulated regional changes for the summer and winter seasons. While to date very few conclusions have been presented on regional climatic changes, mostly limited to some broad latitudinal bands, our analysis shows that a number of consistent patterns of regional change across models and scenarios are now emerging. For temperature, in addition to maximum winter warming in northern high latitudes, warming much greater than the global average is found over Central Asia, Tibet and the Mediterranean region in summer. Consistent warming lower than the global average is found in some seasons over Southern South America, Southeast Asia and South Asia, while cases of inconsistent warming amplification compared to the global average occur mostly in some tropical and southern sub-tropical regions. Consistent increase in winter precipitation is found in northern high latitude regions, as well as Central Asia, Tibet, Western and Eastern North America, and Western and Eastern Africa regions. The experiments also indicate an increase in South Asia and East Asia summer monsoon precipitation. A number of regions show a consistent decrease in precipitation, such as Southern Africa and Australia in winter, the Mediterranean region in summer and Central America in both seasons. Possible physical mechanisms that lead to the simulated changes are discussed.

Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors

Monthly-mean precipitation, mean (TAVG), maximum (TMAX) and minimum (TMIN) surface air temperatures, and cloudiness from the CORDEX-Africa regional climate model (RCM) hindcast experiment are evaluated for model skill and systematic biases. All RCMs simulate basic climatological features of these variables reasonably, but systematic biases also occur across these models. All RCMs show higher fidelity in simulating precipitation for the west part of Africa than for the east part, and for the tropics than for northern Sahara. Interannual variation in the wet season rainfall is better simulated for the western Sahel than for the Ethiopian Highlands. RCM skill is higher for TAVG and TMAX than for TMIN, and regionally, for the subtropics than for the tropics. RCM skill in simulating cloudiness is generally lower than for precipitation or temperatures. For all variables, multi-model ensemble (ENS) generally outperforms individual models included in ENS. An overarching conclusion in this study is that some model biases vary systematically for regions, variables, and metrics, posing difficulties in defining a single representative index to measure model fidelity, especially for constructing ENS. This is an important concern in climate change impact assessment studies because most assessment models are run for specific regions/sectors with forcing data derived from model outputs. Thus, model evaluation and ENS construction must be performed separately for regions, variables, and metrics as required by specific analysis and/or assessments. Evaluations using multiple reference datasets reveal that cross-examination, quality control, and uncertainty estimates of reference data are crucial in model evaluations.

Cloud computing and virtualization within the regional climate model and evaluation system

The Regional Climate Model Evaluation System (RCMES) facilitates the rapid, flexible inclusion of NASA observations into climate model evaluations. RCMES provides two fundamental components. A database (RCMED) is a scalable point-oriented cloud database used to elastically store remote sensing observations and to make them available using a space time query interface. The analysis toolkit (RCMET) is a Python-based toolkit that can be delivered as a cloud virtual machine, or as an installer package deployed using Python Buildout to users in order to allow for temporal and spatial regridding, metrics calculation (RMSE, bias, PDFs, etc.) and end-user visualization. RCMET is available to users in an “offline”, lone scientist mode based on a virtual machine dynamically constructed with model outputs and observations to evaluate; or on an institution’s computational cluster seated close to the observations and model outputs. We have leveraged RCMES within the content of the Coordinated Regional Downscaling Experiment (CORDEX) project, working with the University of Cape Town and other institutions to compare the model output to NASA remote sensing data; in addition we are also working with the North American Regional Climate Change Assessment Program (NARCCAP). In this paper we explain the contribution of cloud computing to RCMES’s specifically describing studies of various cloud databases we evaluated for RCMED, and virtualization toolkits for RCMET, and their potential strengths in delivering user-created dynamic regional climate model evaluation virtual machines for our users.