The tendency for more frequent El Niño events and fewer La Niña events since the late 1970's has been linked to decadal changes in climate throughout the Pacific basin. Aspects of the most recent warming in the tropical Pacific from 1990 to 1995, which are connected to but not synonymous with El Niño, are unprecedented in the climate record of the past 113 years. There is a distinction between El Niño (EN), the Southern Oscillation (SO) in the atmosphere, and ENSO, where the two are strongly linked, that emerges clearly on decadal time scales. In the traditional El Niño region, sea surface temperature anomalies (SSTAs) have waxed and waned, while SSTAs in the central equatorial Pacific, which are better linked to the SO, remained positive from 1990 to June 1995. We carry out several statistical tests to assess the likelihood that the recent behavior of the SO is part of a natural decadal‐timescale variation. One test fits an autoregressive‐moving average (ARMA) model to a measure of the SO given by the first hundred years of the pressures at Darwin, Australia, beginning in 1882. Both the recent trend for more ENSO events since 1976 and the prolonged 1990–1995 ENSO event are unexpected given the previous record, with a probability of occurrence about once in 2,000 years. This opens up the possibility that the ENSO changes may be partly caused by the observed increases in greenhouse gases.

No abstract

No abstract

The Community Atmosphere Model (CAM) has been interfaced to the Data Assimilation Research Testbed (DART), a community facility for ensemble data assimilation. This provides a large set of data assimilation tools for climate model research and development. Aspects of the interface to the Community Earth System Model (CESM) software are discussed and a variety of applications are illustrated, ranging from model development to the production of long series of analyses. CAM output is compared directly to real observations from platforms ranging from radiosondes to global positioning system satellites. Such comparisons use the temporally and spatially heterogeneous analysis error estimates available from the ensemble to provide very specific forecast quality evaluations. The ability to start forecasts from analyses, which were generated by CAM on its native grid and have no foreign model bias, contributed to the detection of a code error involving Arctic sea ice and cloud cover. The potential of parameter estimation is discussed. A CAM ensemble reanalysis has been generated for more than 15 yr. Atmospheric forcings from the reanalysis were required as input to generate an ocean ensemble reanalysis that provided initial conditions for decadal prediction experiments. The software enables rapid experimentation with differing sets of observations and state variables, and the comparison of different models against identical real observations, as illustrated by a comparison of forecasts initialized by interpolated ECMWF analyses and by DART/CAM analyses.

The magnitude and persistence of land carbon (C) pools influence long-term climate feedbacks. Interactive ecological processes influence land C pools and our understanding of these processes is imperfect so land surface models have errors and biases when compared to each other and to real observations. Here we implement an Ensemble Adjustment Kalman Filter (EAKF), a sequential state data assimilation technique to reduce these errors and biases. We implement the EAKF using the Data Assimilation Research Testbed coupled with the Community Land Model (CLM 4.5 in CESM 1.2). We assimilated simulated and real satellite observations for a site in central New Mexico, United States. A series of observing system simulation experiments allowed assessment of the data assimilation system without model error. This showed that assimilating biomass and leaf area index observations decreased model error in C dynamics forecasts (29% using biomass observations and 40% using leaf area index observations) and that assimilation in combination shows greater improvement (51% using both observation streams). Assimilating real observations highlighted likely model structural errors and we implemented an adaptive model-variance-inflation technique to allow the model to track the observations. Monthly and longer model forecasts using real observations were improved relative to forecasts without data assimilation. The reliable forecast lead-time varied by model pool and is dependent on how tightly the C pool is coupled to meteorologically driven processes. The EAKF and similar state data assimilation techniques could reduce errors in projections of the land C sink and provide more robust forecasts of C pools and land-atmosphere exchanges. Plain Language Summary The amount of carbon stored in vegetation and soils is an importantcontrol on how much carbon dioxide is in the atmosphere, and that influences future climate. Land surface models are used to simulate where this carbon is, but they are imperfect and there are often differences between model predictions and observations of the carbon stores. Here we describe a system that combines model predictions and observations and updates the modeled carbon stores so they are closer to the observations, considering uncertainty in both the model and the observations. We test our system at a location in New Mexico, United States, where we use observations from satellites of the amount of leaves on the vegetation and the amount of carbon stored in the vegetation. When we combine these observations with our land surface model there are large changes in the predicted amounts of stored carbon and the times of the year when the vegetation has the most leaves. These changes persist in the model after we stop updating it with observations, improving the model forecast.FOX ET AL.