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

In this paper we discuss the construction of ensemble predictions for anomalies associated with El-Nino/Southern Oscillations (ENSOs), which depend on the coupling of the atmosphere and ocean. First we distinguish between two types of ensembles. In the boundary forcing ensembles, which have been used so far in seasonal to interannual predictions/simulations, the ensemble approach is applied only on the atmosphere ; the boundary SST is assumed to be known (or perfectly predicted), In the forecasts no information is retained from the atmospheric initial state ; the system is driven by the anomalous boundary forcing only. In the initial value ensemble that we propose for the coupled ocean-atmosphere system on the ENSO time scales, an ensemble of possible oceanic initial conditions are integrated forward, coupled with the atmosphere. The atmospheric initial conditions are still forgotten in this approach, but the oceanic predictions can be improved due to the nonlinear filtering process that the ensemble offers. We propose the use of the breeding method, successfully applied in atmospheric ensemble predictions, for generating the flow dependent, growing type of errors present in oceanic analyses. The bred growing vectors represent an extension into nonlinear systems of the leading local Lyapunov vectors that describe maximum sustainable growth in a linear sense. We argue that the breeding method can not only offer a good description of initial uncertainty but can also serve as a new diagnostic tool, describing the leading instabilities associated with different ENSO episodes.

Climate ensemble forecasts : how to create them ?

This study assesses the skill of forecasts of precipitation and surface temperature by the National Meteorological Center's (NMC) global model in the 108 consecutive 30-day forecasts (known as Dynamical Extended Range Forecast II (DERF II)) that were made from initial conditions 24 h apart between 14 December 1986 and 31 March 1987. Model precipitation accumulated during the first 24 h of each 30-day forecast was used for verification. Anomalies were calculated by averaging the precipitation for a given forecast length over all 108 forecasts and subtracting the resulting mean from the precipitation for that forecast length. A similar procedure was used for surface skin temperature. The skill of the model's forecasts of precipitation and surface temperature anomalies was assessed, using anomalies from day-1 forecasts as verification. Precipitation forecasts for all regions of the globe exhibit more skill than persistence. Precipitation forecasts for the Northern Hemisphere (NH) extratropics show skill 1.5 days further into the forecasts than forecasts for the tropics and exceed the mean skill of 1-day persistence forecasts until day 7. Even the worst individual forecast for the NH extratropics exceeds the mean skill of persistence through day 5. Time-mean precipitation forecasts for the NH extratropics display an anomalymore » correlation of 0.69 for forecast days 2-5 and 0.53 for forecast days 2-10 when verified against day-1 precipitation anomalies. Surface skin-temperature anomalies are more persistent than precipitation anomalies; forecasts of surface temperature anomalies have higher skill than forecasts of precipitation anomalies. Forecasts of time-mean surface temperature anomalies for the NH extratropics for forecast days 2-30 and 11-30 exhibit levels of skill similar to forecasts of time-mean precipitation anomalies. This implies that forecast skill for such long forecast periods reflects skill in predicting planetary-scale variations. 26 refs., 7 figs., 1 tab.« less

The Skill of Precipitation and Surface Temperature Forecasts by the NMC Global Model during DERF II

. We developed a Carbon data assimilation system to estimate the surface carbon fluxes using the Local Ensemble Transform Kalman Filter and atmospheric transfer model of GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological fields based on the Goddard Earth Observing System Model, Version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. [2011, 2012], who estimated the surface carbon fluxes in an Observing System Simulation Experiment (OSSE) mode, as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 hours. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as variable localization , and increased observation weights near the surface, they obtained accurate carbon fluxes at grid point resolution. We developed a new version of the LETKF related to the Running-in-Place (RIP) method used to accelerate the spin-up of EnKF data assimilation [Kalnay and Yang, 2010; Wang et al., 2013, Yang et al., 2014]. Like RIP, the new assimilation system uses the no-cost smoothing algorithm for the LETKF [Kalnay et al., 2007b], which allows shifting at no cost the Kalman Filter solution forward or backward within an assimilation window. In the new scheme a long observation window (e.g., 7-days or longer) is used to create an LETKF ensemble at 7-days. Then, the RIP smoother is used to obtain an accurate final analysis at 1-day. This analysis has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7-days observations, which accelerates the spin up. The assimilation and observation windows are then shifted forward by one day, and the process is repeated. This reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems.

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Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window

For the first time, a four-dimensional stratospheric/tropospheric data assimilation system with a top analysis level at 0.4 mb has been developed and used to produce physically consistent gridded analyses for the stratosphere as well as the troposphere for a period during the First GARP Global Experiment (FGGE) and Limb Infrared Monitor of the Stratosphere (LIMS) (November 1978-May 1979). The system consists of a two-dimensional optimum interpolation analysis with 18 mandatory pressure levels and a 19-level fourth order stratospheric/tropospheric general circulation model with a horizontal resolution of 4 (latitude) by 5 deg (longitude) and a top at 0.3 mb. The system allows the utilization of stratospheric data including LIMS, Tiros-N retrievals, rocketsondes and vertical temperature profile radiometer soundings in addition to the other FGGE level 2b data. These data are analyzed every six hours. In order to examine the quality of the analyzed data, forecast experiments starting from different analyses are performed for the period of the stratospheric sudden warming of late February 1979. The results indicate that by employing the present four-dimensional assimilation approach, the medium-range forecast skill for this event is improved.

Eugenia Kalnay

Papers

Climate ensemble forecasts : how to create them ?

The Skill of Precipitation and Surface Temperature Forecasts by the NMC Global Model during DERF II

Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window

Forecast experiments with the NASA/GLA stratospheric/tropospheric data assimilation system

Overview of Weather and Climate Systems