The NCEP and NCAR are cooperating in a project (denoted “reanalysis”) to produce a 40-year record of global analyses of atmospheric fields in support of the needs of the research and climate monitoring communities. This effort involves the recovery of land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data; quality controlling and assimilating these data with a data assimilation system that is kept unchanged over the reanalysis period 1957–96. This eliminates perceived climate jumps associated with changes in the data assimilation system. The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible. The data assimilation and the model used are identical to the global system implemented operationally at the NCEP on 11 January 1995, except that the horizontal resolution is T62 (about 210 km). The database has been enhanced with many sources of observations not available in real time for operations, provided b...

The NCEP/NCAR 40-Year Reanalysis Project

ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright © 2011 Royal Meteorological Society

The ERA-Interim reanalysis: configuration and performance of the data assimilation system

Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet’s resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.

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Global Consequences of Land Use

世界経済・社会統計 = World development indicators

Drafting Authors: Neil Adger, Pramod Aggarwal, Shardul Agrawala, Joseph Alcamo, Abdelkader Allali, Oleg Anisimov, Nigel Arnell, Michel Boko, Osvaldo Canziani, Timothy Carter, Gino Casassa, Ulisses Confalonieri, Rex Victor Cruz, Edmundo de Alba Alcaraz, William Easterling, Christopher Field, Andreas Fischlin, Blair Fitzharris, Carlos Gay García, Clair Hanson, Hideo Harasawa, Kevin Hennessy, Saleemul Huq, Roger Jones, Lucka Kajfež Bogataj, David Karoly, Richard Klein, Zbigniew Kundzewicz, Murari Lal, Rodel Lasco, Geoff Love, Xianfu Lu, Graciela Magrín, Luis José Mata, Roger McLean, Bettina Menne, Guy Midgley, Nobuo Mimura, Monirul Qader Mirza, José Moreno, Linda Mortsch, Isabelle Niang-Diop, Robert Nicholls, Béla Nováky, Leonard Nurse, Anthony Nyong, Michael Oppenheimer, Jean Palutikof, Martin Parry, Anand Patwardhan, Patricia Romero Lankao, Cynthia Rosenzweig, Stephen Schneider, Serguei Semenov, Joel Smith, John Stone, Jean-Pascal van Ypersele, David Vaughan, Coleen Vogel, Thomas Wilbanks, Poh Poh Wong, Shaohong Wu, Gary Yohe

Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change

A statistic, the BV (bred vector) dimension, is introduced to measure the effective local finite‐time dimensionality of a spatiotemporally chaotic system It is shown that the Earth’s atmosphere often has low BV‐dimension The implications for improving weather forecasting through data assimilation and targeted observations are discussed

http://www.weatherchaos.umd.edu/papers/pub_version.pdf

Local Low Dimensionality and Relation to Effects Of Targeted Weather Observations

Abstract. Atmospheric inversion of carbon dioxide (CO2) measurements to better understand carbon sources and sinks has made great progress over the last 2 decades. However, most of the studies, including a four-dimensional variational ensemble Kalman filter and Bayesian synthesis approaches, directly obtain only fluxes, while CO2 concentration is derived with the forward model as part of a post-analysis. Kang et al. (2012) used the local ensemble transform Kalman filter (LETKF), which updates the CO2, surface carbon flux (SCF), and meteorology fields simultaneously. Following this track, a system with a short assimilation window and a long observation window was developed (Liu et al., 2019). However, this data assimilation system faces the challenge of maintaining carbon mass conservation. To overcome this shortcoming, here we apply a constrained ensemble Kalman filter (CEnKF) approach to ensure the conservation of global CO2 mass. After a standard LETKF procedure, an additional assimilation is used to adjust CO2 at each model grid point and to ensure the consistency between the analysis and the first guess of the global CO2 mass. Compared to an observing system simulation experiment without mass conservation, the CEnKF significantly reduces the annual global SCF bias from ∼ 0.2 to less than 0.06 Gt and slightly improves the seasonal and annual performance over tropical and southern extratropical regions. We show that this system can accurately track the spatial distribution of annual mean SCF. And the system reduces the seasonal flux root mean square error from a priori to analysis by 48 %–90 %, depending on the continental region. Moreover, the 2015–2016 El Niño impact is well captured with anomalies mainly in the tropics. 

/pdf/improving-the-joint-estimation-of-co-sub-2-sub-and-surface-3caow350.pdf

Improving the joint estimation of CO<sub>2</sub> and surface carbon fluxes using a constrained ensemble Kalman filter in COLA (v1.0)

Ensemble Kalman Filter (EnKF) methods have been shown to be effective data assimilation schemes: Houtekamer et al. (2005) found the performance of an EnKF scheme to be comparable to that of an operational 3D-Var scheme when assimilating real observations into the CMC GEM grid point model. Using another EnKF scheme, Whitaker et al. (2004) obtained a better mid-troposphere reanalysis from surface pressure observations than with the NCEP 3D-Var. The Local Ensemble Kalman Filter (LEKF), another variant of the EnKF, was introduced in Ott et al. (2002; 2004) and was shown to be accurate and efficient when assimilating simulated observations of model variables on the NCEP GFS model in Szunyogh et al. (2005). In this study, the LEKF is replaced by a more efficient but equivalent implementation (Hunt, 2005), the Local Ensemble Transform Kalman Filter (LETKF). Here, we assimilate simulated model grid point observations and simulated rawinsonde observations into the NASA fvGCM model. The LETKF algorithm is briefly described in Section 2, while its implementation on the fvGCM model is explained in Section 3. Section 4 present data assimilation results for the “perfect model” scenario. In these experiments, the NASA fvGCM is run for two months without assimilating observations to obtain a time series of "true" atmospheric state. Simulated noisy grid-point and rawinsonde observations of this truth are then assimilated with the LETKF. The performance of data assimilation system is evaluated by comparing the analysis state to the truth. Further verification of the scheme is obtained by comparing the performance of the LETKF to that of

Application of local ensemble transform Kalman filter: Perfect model experiments with NASA FVGCM model

The present paper aims to provide a brief review on several deterministic ensemble Kalman filtering (EnKF) methods and the related practical techniques to prevent filter divergence. Since Evensen (1994), several formulations of EnKF have been proposed, and Whitaker and Hamill (2002) suggested that a deterministic method, a.k.a. an ensemble square root filter (EnSRF, Andrews 1968), is expected to outperform the classical perturbed observation methods (e.g., Houtekamer and Mitchell 1998) especially in a limited ensemble size, which is usually the case in realistic atmospheric models. There are several EnSRFs proposed for atmospheric data assimilation, including an ensemble transform Kalman filter (ETKF) by Bishop et al. (2001), an ensemble adjustment Kalman filter (EAKF) by Anderson (2001), an EnSRF by Whitaker and Hamill (2002), all of which are effective when observational data are assimilated serially, and a local ensemble Kalman filter (LEKF) by Ott et al. (2002; 2004), where observations are assimilated simultaneously in a local patch. As Tippett et al. (2003) indicated, ensemble formulations in the deterministic method are not theoretically unique, and it is not clear if there is a unique preferable choice in the various possible implementations. In addition, there are several practical techniques to avoid filter divergence such as localization of the forecast error correlations, hybrid combinations with three-dimensional variational (3DVAR) method, a double ensemble method, covariance inflation, and random noise addition to analysis ensemble.

Eugenia Kalnay

Papers

Local Low Dimensionality and Relation to Effects Of Targeted Weather Observations

Improving the joint estimation of CO<sub>2</sub> and surface carbon fluxes using a constrained ensemble Kalman filter in COLA (v1.0)

Application of local ensemble transform Kalman filter: Perfect model experiments with NASA FVGCM model

A review of the deterministic ensemble Kalman filtering methods and related techniques

Atmospheric modeling, data assimilation and predictability: Numerical discretization of the equations of motion