Research towards the next generation of NOAA Climate

The fidelity of new reanalysis datasets (MERRA, 20CR, CFSR, ERA-Interim) at representing climate variability of the 20th century has enabled significant advances in climate research. In this research proposal, we will investigate known shortcomings of these datasets, while developing a framework for a new NOAA Climate Reanalysis (NCR) system to ameliorate them. The NCR will eventually have four “streams” to meet the various user needs for reanalysis information: Stream 0: Boundary-forced, 1850-present “AMIP” simulation with large ensemble Stream 1: Historical, 1850-present using only surface data Stream 2: Modern, 1946-present using only surface and conventional upper air data Stream 3: Satellite, 1973-present using quality-controlled satellites, Global Positioning System Radio Occultation, and surface and conventional upper air data. One of the foci of this research will be to use observing system experiments. In these, the 20002010 observing system is reduced to that of selected historical periods to investigate the impact to the time-varying quality and density of the observing system and determine ways to reduce this impact. We will use innovative methods to assess the relative importance and impact of model errors and observational errors on the quality and homogeneity of the reanalysis fields, with particular attention to reducing or eliminating spurious jumps and trends. The framework for the NCR system will leverage recent advances in operational data assimilation for global weather prediction, as well as newly digitized observational datasets and global model improvements. While initially focusing on the atmosphere to develop the NCR framework, this project will serve as the basis for further NCR efforts, incorporating advancements generated by other projects supported by MAPP, such as integration of ocean, chemistry, and land components and the treatment of observational and model biases. International coordination and data sharing with NOAA's reanalysis partners at NASA, ACRE, ECWMF, and JMA and synergies from the NOAA Reanalysis Task Force will be crucial in achieving the project's goals on a limited budget. This project is directly related to foci 1 of Priority 1 of the MAPP call for proposals. It is directly relevant to NOAA's Next-Generation Strategic Plan goals for climate adaptation and mitigation. As noted by the WMO Global Framework on Climate Services, reanalyses are a key component of the climate information needed for informed decision making for climate change mitigation and adaption. The NGSP recognizes that a strong scientific basis is needed for developing “climate adaptation and mitigation” strategies, which will require “improved scientific understanding of the changing climate system and its impacts” and “assessments of current and future states of the climate system.” For NOAA to achieve these objectives, we must develop climate reanalysis products that are free of artificial trends and that provide reliable information about the frequency of weather and climate extremes. This proposal directly addresses the MAPP call to pursue research on “Outstanding issues in atmospheric reanalysis”, in particular by attempting to “overcome the impact of data inhomogeneities due to changes in the observing system and data biases”, “overcome the impact of model bias”, “better quantify uncertainties in reanalysis data including the impacts of data and model error”, and “exploit new data”. Strategies to Improve Stratospheric Processes in Climate Reanalysis Principal Investigator: Craig Long NOAA/NWS/NCEP/Climate Prediction Center Co-Investigators: Judith Perlwitz Univ. of Colorado/CIRES and NOAA/ESRL/Physical Sciences Division Fabrizio Sassi NRL/Space Science Division/Near Space Environments Geospace Science and Technology Branch 1) Abstract The primary purpose of the reanalysis effort is to advance climate studies by eliminating fictitious trends caused by model and data assimilation changes that occurred in real time. Reanalyses are to represent the observations as closely as possible and could be used as surrogates where observations are not available. Each generation of reanalyses has improved upon its predecessor in many ways by: reduction of errors, increased spatial and vertical resolution, and addition of more variables. The current generation of reanalyses provides more information about the stratosphere than previous versions. This is important for monitoring the impacts of climate change and ozone depletion on the stratospheric circulation and the stratospheric interactions with the troposphere. Assessments of the stratosphere in the latest generation of reanalyses revealed several issues that may hinder the full use of these reanalyses for climate studies. This was particularly true for the NOAA Climate Forecast System Reanalysis (CFSR). This reanalysis contains jumps in data records during stream transitions, warming trends in the upper stratosphere between streams, poor representation of the Quasibiennial Oscillation (QBO) winds, ozone observations not being assimilated in the upper stratosphere, and poor representation of water vapor above the tropopause. It is important to rectify these issues before the next NOAA reanalysis effort. We propose to address the climate objectives outlined in the NOAA Next Generation Strategic Plan (NGSP) and a major CPO/Modeling, Analysis, Predictions, and Projections (MAPP) Program priority: Research to Advance Climate Reanalysis, particularly “issues with the quality of reanalyses in the stratosphere” by improving the characterization of the stratosphere in reanalysis by building upon research conducted following the CFSR. We propose to: reduce the impacts of data inhomogenuity on temperature and ozone, to improve the thermal structure of the upper stratosphere, improve the representation of the QBO winds and residual circulation in the tropics, and improve the depiction of ozone and water vapor in the stratosphere. Success in providing these improvements will lead to a better characterization of the stratosphere. A well characterized stratosphere may enable better weather and climate research and services by: 1) providing a more accurate depiction of past weather and climate conditions, 2) improving the monitoring of current climate conditions, and 3) enabling the attribution of climate variations and change by comparison with past conditions. Exploration of advanced ocean data assimilation schemes at NCEP Principal Investigator: James A. Carton (UMD) Co-Principal investigators: Eugenia Kalnay (UMD), David Behringer (NOAA/NCEP), and Hendrik Tolman (NOAA/NCEP) (2) Abstract The first task will be to upgrade NCEP’s Global Ocean Data Assimilation System (GODAS) from the current 3DVar system implemented in 2003 to the ensemble Local Ensemble Transform Kalman Filter (LETKF). GODAS serves as the ocean component of the integrated atmosphereocean analysis system, in turn providing the initial conditions for NCEP’s atmosphere/ocean Climate Forecast System, version 2, (CFSv2). The second task will be to combine the 3DVar and LETKF systems to form a hybrid version of GODAS. We will explore the effectiveness of this hybrid system to represent timeevolving local correlations due, for example to fronts or currents, while at the same time maintaining large-scale correlations. An additional task will be to examine the computational efficiency of the hybrid filter relative to its 3DVar and LETKF alternatives. The proposal will bring together researchers from the University of Maryland experienced in the development and use of LETKF with NCEP researchers who have overseen development of 3DVar-GODAS and the first two versions of the CFS. The rationale for proposed work is: 1) It will result in an upgrade of the ocean analysis system to one that will be analogous to NCEP’s 3DVar-hybrid Gridpoint Statistical Interpolation system (GSI) used for atmospheric analysis. This upgrade will allow the next generation CFS to gain the benefit of a more integrated atmosphere-ocean-land analysis system in which both ocean and atmosphere components use coupled ensemble-based estimates of flux error at the interface. 2) The implementation of LETKF in GODAS will provide NCEP with a more flexible ocean analysis system, for example simplifying the inclusion of new observational data sets like sea surface salinity and providing an error estimate for the ocean state. This flexibility is important to allow NCEP to implement assimilation upgrades to both GODAS (using a MOM-based model) and the eddy-resolving Real-Time Ocean Forecast System (RTOFS) (using a HYCOM-based ocean model). The new development work proposed here will be carried out in NCEP’s computing environment thus facilitating the integration of the resulting system into operations. This proposal addresses the NOAA Next Generation Strategic Plan’s call to ‘create accurate and reliable estimates of the state of the ocean, including its temperature, salinity, and motion fields for accurate forecasts and assessments’. Likewise it addresses the Climate Program Office Strategic Climate Objective: I Improve Scientific Understanding, specifically the goals of improved monitoring and improving initial conditions to explore ‘useful predictions of climate variability and change for the next one to three decades’. Within the Modeling, Analysis, Predictions, and Projections (MAPP) program 2013 Call the developments described in this proposal address Priority Area 1: Research to Advance Climate Reanalysis and Research Focus 2: Integration among Earth System reanalysis components by improving the integration of the atmospheric and ocean analysis systems. Evaluating CFSR Air-Sea Heat, Freshwater, and Momentum Fluxes in the context of the Global Energy and Freshwater Budgets Lead Principal Investigator: Dr. Lisan Yu, Senior Scientist, Department of Physical Oceanography, MS 21, Woods Hole Oceanographic Institution, Woods Hole, MA 02543-1535, Tel: 508-289-2504, Email: [email protected] Co-Investigator: Dr. Yan Xue, National Centers for Environmental Prediction, Climate Prediction Center, 5830 University Research Court, College Park, MD 20740, Tel: 301-6833390, Email: [email protected] 2. Abstract-Priority Area 1, Research to Advance Climate Reanalysis Type II This proposed research aims at providing a comprehensive assessment of the partially coupled Climate Forecast System Reanalysis (CFSR) by NOAA NCEP in representing air-sea heat, freshwater, and momentum fluxes in the context of the global energy and water budgets. The proposed research addresses the MAPP call on improving our ability to “better quantify uncertainties in reanalysis data including the impacts of data and model error”, and addresses the climate objectives of NOAA’s Next Generation Strategic Plan (NGSP) with particular focus on providing quantitative assessments of current state of the climate system. The CFSR is the first and only reanalysis that incorporates a coupled atmosphereoceanland climate system with an interactive sea-ice component, and the one that has the finest spatial resolution (~0.5°) ever produced by any reanalysis. Evidence has clearly pointed to the advantages and strengths of the finer-resolution coupled CFSR reanalysis in characterizing airsea fluxes at regional and global scales, but biases/errors in the CFSR flux components at various temporal scales have also been reported. The biases/errors appear to have significant impact on the estimates of the energy and water budgets over the global oceans. Currently, the CFSR produces a global energy imbalance of 15 Wm-2, which is about 10 Wm-2 higher than the estimates from the earlier NCEP reanalyses. We recognize that balancing the global energy/water budgets has long been a challenging issue, with global energy budgets differing considerably, from 2 to 30 Wm-2, when computed using reanalyzed, ship-, and satellite-based flux products. However, the global energy/water budgets are central to the understanding of climate variability and climate changes produced by the reanalyses. A good knowledge of the impact of biases/errors in surface flux components on the global budget estimates will be highly beneficial to not only the users of CFSR products but also the developers for the next-generation Earth System reanalysis. Therefore, this proposed assessment study will analyze the biases/errors in the CFSR surface fluxes in the context of the global energy/water budget and will also compare the CFSR with the earlier and the latest reanalyses as value-added evaluation. The proposed approaches include: (i) in situ validation, in which a database consisting of more than 130 flux buoys is used as ground truth for identifying and quantifying biases/errors in flux products; (ii) spectral analysis, in which shipand satellite-based global flux analyses are used as reference to evaluate and characterize the regional and global spectral structures of flux products, and (iii) dynamical diagnosis, in which dynamic constraints (such as energy and freshwater budgets in an enclosed volume) are used to test the physical consistency of flux products with ocean state variables (temperature and salinity). The primary objectives of the proposed research are to (i) identify the strength and weakness of the CFSR surface flux components by comparison with in situ flux measurement, satellite-based analyses and other reanalyses products and understand the sources of biases, (ii) examine the effect of spatial resolution in improving the accuracy and spatial structure of CFSR fluxes on regional and global scales, (iii) investigate the use of physical constraints together with ocean state variables to diagnose and understand the uncertainties in CFSR air-sea fluxes. The significance of the proposed research is in the potential to (i) establish a baseline that can be used to help determine the scope and extent of the CFSR surface fluxes to be applied; (ii) improve our understanding of the state-of-estimation of air-sea fluxes in latest reanalyses; (iii) obtain new insights on the cause of the discrepancies in global energy/freshwater budget estimates based on air-sea fluxes; and (iv) obtain practical recommendations for future improvement of air-sea flux estimation in reanalyses.