CGD 2011 Profiles in Science: Stephen Yeager
Summary
Steve is a Project Scientist in the Oceanography section of NCAR’s Earth System Laboratory whose research is currently focused on the overturning circulation of the ocean, its climactic significance, and its potential predictability. He has over ten years of experience contributing to the development and application of the ocean component of the Community Earth System Model, and served as the community liaison for the Ocean Model Working Group for several years. Steve has authored and co-authored research papers on a variety of topics in ocean modeling and climate science, including air-sea fluxes, mechanisms of ocean variability, and model sensitivity to configuration and surface boundary conditions.
Prior to joining NCAR in 1998, Steve taught secondary-level physics and math in Fiji as a Peace Corps volunteer and got his MSc in physics from Brown University. He is currently pursuing a doctorate in physical oceanography from the University of Colorado in his spare time.
Publications
Rosenbloom, N., C. Shields, E. Brady, E. Levis, and S. Yeager. 2011: Using CCSM3 for paleoclimate applications. NCAR/TN-483+STR, doi:10.1126/science.1171041.
Figure 1: High resolution figure
Abstract: This document describes the procedures for creating a CCSM3 paleoclimate simulation in the fully coupled (all active components) configuration. We provide tools and examples of the process used to create paleoclimate simulations using the computing resources at the National Center for Atmospheric Research (NCAR). This document is to be used as a guide; researchers are ultimately responsible for modifying the process to accommodate their time period of interest as well as adapting the tools to their available computer resources. Throughout this User’s Guide we differentiate between the procedures required to create (1) near-modern (e.g., Quaternary, Pliocene) or (2) Deep-Time (pre-Quaternary) model simulations. In near-modern simulations, the continents are in their present-day positions, and the land/sea masks do not require significant modification. Quaternary modelers are often able to use existing forcing files to simulate past climate. By contrast, deep-time simulations require drastic modifications to the land/sea mask, and the modeler is responsible for providing the orographic/bathymetric maps for their geologic period of interest. This document assumes a default fully coupled CCSM3 configuration. We do not describe the creation of the forcing files used in Data Model components or in standalone component model runs. - from Introduction p.8.
Figure caption: Sea level lowstand on the ocean grid. Lowering sea level to expose the continental shelf during a sea level low stand requires changing the land/sea mask by modifying the ocean bathymetry (KMT) file. Be sure to open closed basins, and widen or eliminate narrow channels; for better model results remove mid-Pacific islands.
Stevenson, S., B. Fox-Kemper, M. Jochum, B. Ragagopalan, and S.G. Yeager. 2010: ENSO Model Validation Using Wavelet Probability Analysis. Journal of Climate, 23, 5540-5547, doi:10.1175/2010JCLI3609.1 .
Figure 2: High resolution figure
Abstract: A new method to quantify changes in El Nino-Southern Oscillation (ENSO) variability is presented, using the overlap between probability distributions of the wavelet spectrum as measured by the wavelet probability index (WPI). Examples are provided using long integrations of three coupled climate models. When subsets of Nino-3.4 time series are compared, the width of the confidence interval on WPI has an exponential dependence on the length of the subset used, with a statistically identical slope for all three models. This exponential relationship describes the rate at which the system converges toward equilibrium and may be used to determine the necessary simulation length for robust statistics. For the three models tested, a minimum of 250 model years is required to obtain 90% convergence for Nino-3.4, longer than typical Intergovernmental Panel on Climate Change (IPCC) simulations. Applying the same decay relationship to observational data indicates that measuring ENSO variability with 90% confidence requires approximately 240 years of observations, which is substantially longer than the modern SST record. Applying hypothesis testing techniques to the WPI distributions from model subsets and from comparisons of model subsets to the historical Nino-3.4 index then allows statistically robust comparisons of relative model agreement with appropriate confidence levels given the length of the data record and model simulation.
Figure caption: Probability distribution functions for CCSMcontrol Niño-3.4 wavelet power. The gray line represents the median value for the model simulation, while the white line is the mean value generated using the CORE hindcast. Dashed black lines correspond to the 25th and 75th percentile values for the model simulation (interquartile range).
Han, W.Q., G.A. Meehl, B. Rajagopalan, J.T. Fasullo, A.X. Hu, J.L. Lin, W.G. Large, J.W. Wang, X.W., Quan, L.L. Trenary, A. Wallcraft, T. Shinoda, and S. Yeager. 2010: Patterns of Indian Ocean sea-level change in a warming climate. Nature Geoscience 3, 546-550, doi:10.1038/ngeo901.
Figure 3: High resolution figure
Global sea level has risen during the past decades as a result of thermal expansion of the warming ocean and freshwater addition from melting continental ice(1). However, sea-level rise is not globally uniform(1-5). Regional sea levels can be affected by changes in atmospheric or oceanic circulation. As long-term observational records are scarce, regional changes in sea level in the Indian Ocean are poorly constrained. Yet estimates of future sea-level changes are essential for effective risk assessment(2). Here we combine in situ and satellite observations of Indian Ocean sea level with climate-model simulations, to identify a distinct spatial pattern of sea-level rise since the 1960s. We find that sea level has decreased substantially in the south tropical Indian Ocean whereas it has increased elsewhere. This pattern is driven by changing surface winds associated with a combined invigoration of the Indian Ocean Hadley and Walker cells, patterns of atmospheric overturning circulation in the north-south and east-west direction, respectively, which is partly attributable to rising levels of atmospheric greenhouse gases. We conclude that-if ongoing anthropogenic warming dominates natural variability-the pattern we detected is likely to persist and to increase the environmental stress on some coasts and islands in the Indian Ocean.
Figure caption: The 10 tide-gauge stations with records longer than 30 years (20 years for Zanzibar) are shown. All trends exceed 95% significance except for stations 6 and 9 tide-gauge data. The middle colour panel shows the Kendall Theil trend of HYCOM SLA for 1961–2008. The light blue/green regions are below and the rest are above 95% significance. Tide-gauge locations are marked 1–10. The rectangles labelled A–D mark regions discussed in Fig. 2.
Jochum, M., S.G. Yeager, K. Lindsay, K. Moore, and R. Murtugudde. 2010: Quantification of the feedback between phytoplankton and ENSO in the Community Climate System Model. Journal of Climate, doi:10.1175/2010JCLI3254.1, early online release
Figure 4: High resolution figure
Abstract: The current coarse resolution version of the Community Climate System Model (CCSM) is used to assess the impact of phytoplankton on El Niño/Southern Oscillation (ENSO). The experimental setup allows for the separation of the effects of climatological annual cycle chlorophyll distribution from its interannually varying part. The main finding is that the chlorophyll production by phytoplankton is important beyond modifying the mean and seasonal cycle of shortwave absorption; interannual modifications to the absorption have an impact as well and they dampen ENSO variability by 9%. The magnitude of damping is the same in the experiment with smaller and in the experiment with larger than observed chlorophyll distribution. This suggests that to accurately represent ENSO in GCMs, it is not sufficient to use a prescribed chlorophyll climatology. Instead, some form of an ecosystem model will be necessary to capture the effects of phytoplankton coupling and feedback.
Figure caption: Difference between mean SST (in color) and precipitation (in mm/day) between HIGHCLIM and LOWCLIM.
Gent, P.R., S.G. Yeager, R.B. Neale, S. Levis and D.A. Bailey. 2010: Improvements in a half degree atmosphere/land version of the CCSM. Climate Dynamics, 79, 25-58, doi:10.1007/s00382-009-0614-8.
Figure 5: High resolution figure
Abstract: A decadal climate projection between 1980 and 2030 using a nominal 0.5° resolution in the atmosphere and land components has been performed using the Community Climate System Model, version 3.5. The mean climate is compared to a companion simulation using a nominal 2° resolution in the atmosphere and land components. The increased atmosphere resolution has several benefits, and produces a significantly better mean climate. The maximum sea surface temperature biases in the major upwelling regions, including the West Coast of the USA, are reduced by more than 60%. Precipitation patterns are improved in the summer Asian monsoon, mostly due to the better resolved orography, and in the eastern tropical Pacific Ocean south of the equator. The improved precipitation patterns lead to better river flows in many rivers worldwide. The atmospheric circulation in the Arctic also improves, which leads to a better regional sea ice thickness distribution in the Arctic Ocean.
Figure caption: Annual mean Arctic sea ice thickness in meter from (a) 0.5° run, and (b) 2° run.