CGD 2008 Profiles in Science: Christine Shields

Summary of achievements

As the CCSM deep time paleoclimate liason, Christine has been involved with several deep time community research efforts including studies on the Permian (250 million years ago), the Cretaceous (100 million years ago), the Paleo-Eocene Thermal Maximum (PETM) (55 million years ago), and most recently, the Ordovician (445 million years ago). Christine works with Jeff Kiehl fostering collaborations aimed at providing better interaction between the university paleo-geography community and climate modellers.

A large part of Christine's work involves designing, modifying, and running the CCSM for deep time paleoclimatic periods. In FY2008, she worked on the design and creation of a fully coupled CCSM for the late Ordovician ( a world of of high CO2 yet glacial conditions where the first of earth's five major extinctions occured). Other research projects in FY2008 included studies on CO2 weathering rates, warm polar climates (specifically in the Permian and PETM), and projects on Permian variability (covering topics such as hurricanes to monsoons to Arctic Oscillation-type phenomena). For deep time warm polar climates, the disparity between model results and data proxies has long been a subject of interest to the paleoclimate community.

Christine also works with Jeff Kiehl on climate sensitivity questions. In FY2008, she worked with Jeff Kiehl, Phil Rasch, and Jim Hack on developing a tuned version of eularian low-resolution (T31) CAM3.5. Although difficulties still exist with the tuning process, Christine began development of a web-based tuning tool for use with future CAM versions.

Publications

Shell, K.M., J.T. Kiehl, and C.A. Shields, 2008: Using the Radiative Kernel Technique to Calculate Climate Feedbacks in NCAR's Community Atmospheric Model. J. Climate, 21, 2269-2282.

Abstract: Climate models differ in their responses to imposed forcings, such as increased greenhouse gas concentrations, due to different climate feedback strengths. Feedbacks in NCAR's Community Atmospheric Model (CAM) are separated into two components: the change in climate components in response to an imposed forcing and the "radiative kernel," the effect that climate changes have on the top-of-the-atmosphere (TOA) radiative budget. This technique's usefulness depends on the linearity of the feedback processes. For the case of CO₂ doubling, the sum of the effects of water vapor, temperature, and surface albedo changes on the TOA clear-sky flux is similar to the clear-sky flux changes directly calculated by CAM. When monthly averages are used rather than values from every time step, the global-average TOA shortwave change is underestimated by a quarter, partially as a result of intramonth correlations of surface albedo with the radiative kernel. The TOA longwave flux changes do not depend on the averaging period. The longwave zonal averages are within 10% of the model-calculated values, while the global average differs by only 2%. Cloud radiative forcing (ΔCRF) is often used as a diagnostic of cloud feedback strength. The net effect of the water vapor, temperature, and surface albedo changes on ΔCRF is -1.6 W m-2, based on the kernel technique, while the total ΔCRF from CAM is -1.3 W m^-2, indicating these components contribute significantly to ΔCRF and make it more negative. Assuming linearity of the ΔCRF contributions, these results indicate that the net cloud feedback in CAM is positive.

Figure caption: Example of the kernel technique. (a) The annual-average outgoing longwave flux (F) kernel for water vapor in units of W m^-2 per 100 mb for a specific humidity increase corresponding to a 1-K temperature increase and constant relative humidity. (b) The annual-average specific humidity changes for the doubled-CO₂ case, divided by a specific humidity increase corresponding to a 1-K temperature increase and constant relative humidity (i.e., the specific humidity anomaly used to calculate the radiative kernel). (c) The resulting flux changes for the doubled-CO₂ case, obtained by combining the kernel and variable anomalies.

Support: Shell, supported by NCAR ASP. Kiehl, supported by DOE, Science of Climate Change Predicition Program. Shields, supported by NOAA GFDL and DOE.

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Soden, B.J., I.M. Held, R. Colman, K.M. Shell, J.T. Kiehl, and C.A. Shields, 2008: Quantifying Climate Feedbacks Using Radiative Kernels. J. Climate, 21, 3504-3520.

Abstract: The extent to which the climate will change due to an external forcing depends largely on radiative feedbacks, which act to amplify or damp the surface temperature response. There are a variety of issues that complicate the analysis of radiative feedbacks in global climate models, resulting in some confusion regarding their strengths and distributions. In this paper, the authors present a method for quantifying climate feedbacks based on "radiative kernels" that describe the differential response of the top-of-atmosphere radiative fluxes to incremental changes in the feedback variables. The use of radiative kernels enables one to decompose the feedback into one factor that depends on the radiative transfer algorithm and the unperturbed climate state and a second factor that arises from the climate response of the feedback variables. Such decomposition facilitates an understanding of the spatial characteristics of the feedbacks and the causes of intermodel differences. This technique provides a simple and accurate way to compare feedbacks across different models using a consistent methodology. Cloud feedbacks cannot be evaluated directly from a cloud radiative kernel because of strong nonlinearities, but they can be estimated from the change in cloud forcing and the difference between the full-sky and clear-sky kernels. The authors construct maps to illustrate the regional structure of the feedbacks and compare results obtained using three different model kernels to demonstrate the robustness of the methodology. The results confirm that models typically generate globally averaged cloud feedbacks that are substantially positive or near neutral, unlike the change in cloud forcing itself, which is as often negative as positive.

Figure caption: The annual-mean, zonal-mean temperature K^T and water vapor K^ω kernels under total-sky conditions for the (top) GFDL, (middle) CAWCR, and (bottom) NCAR models in units of W m^-2 K^-1 /100 hPa.

Support: Shell, supported by NCAR ASP. Kiehl, supported by DOE, Science of Climate Change Predicition Program. Shields, supported by NOAA GFDL and DOE.