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Showing papers by "Eugenia Kalnay published in 2005"



Journal ArticleDOI
TL;DR: In this article, the sensitivity of surface climate change to land types is investigated for the Northern Hemisphere by subtracting the reanalysis from the observed surface temperature (OMR) using a regression model, showing that while reanalysis represents the large-scale climate changes due to greenhouse gases and atmospheric circulation, it is less sensitive to regional surface processes associated with land types.
Abstract: [1] Sensitivity of surface climate change to land types is investigated for the Northern Hemisphere by subtracting the reanalysis from the observed surface temperature (OMR). The basis of this approach is that while reanalysis represents the large-scale climate changes due to greenhouse gases and atmospheric circulation, it is less sensitive to regional surface processes associated with land types. OMR trends derived from two independent reanalyses (ERA40 and NNR) and two observations (CRU and GHCN) show similar dependence upon land types, suggesting the attribution of OMRs to different land types is robust. OMR trends reveal 1) Warming over barren areas is larger than most other land types. 2) Urban areas show large warming second only to barren areas. 3) Croplands with agricultural activity show a larger warming than natural broadleaf forests. The overall assessment indicates surface warming is larger for areas that are barren, anthropogenically developed, or covered with needle-leaf forests.

117 citations


Journal ArticleDOI
01 Aug 2005-Tellus A
TL;DR: It is shown that a modest size (40-member) ensemble is sufficient to track the evolution of the atmospheric state with high accuracy and the importance of testing the ensemble-based Kalman filter data assimilation systems on simulated observations is stressed.
Abstract: The accuracy and computational efficiency of the recently proposed local ensemble Kalman filter (LEKF) data assimilation scheme is investigated on a state-of-the-art operational numerical weather prediction model using simulated observations The model selected for this purpose is the T62 horizontal- and 28-level vertical-resolution version of the Global Forecast System (GFS) of the National Center for Environmental Prediction The performance of the data assimilation system is assessed for different configurations of the LEKF scheme It is shown that a modest size (40-member) ensemble is sufficient to track the evolution of the atmospheric state with high accuracy For this ensemble size, the computational time per analysis is less than 9 min on a cluster of PCs The analyses are extremely accurate in the mid-latitude storm track regions The largest analysis errors, which are typically much smaller than the observational errors, occur where parametrized physical processes play important roles Because these are also the regions where model errors are expected to be the largest, limitations of a real-data implementation of the ensemble-based Kalman filter may be easily mistaken for model errors In light of these results, the importance of testing the ensemble-based Kalman filter data assimilation systems on simulated observations is stressed

85 citations


Journal ArticleDOI
TL;DR: In this article, a reanalysis made with a frozen model can detect the warming trend due to an increase of greenhouse gases within the atmosphere at its full strength after a short transient (less than 100 analysis cycles).
Abstract: This paper shows analytically that a reanalysis made with a frozen model can detect the warming trend due to an increase of greenhouse gases within the atmosphere at its full strength (at least 95% level) after a short transient (less than 100 analysis cycles). The analytical proof is obtained by taking into consideration the following three possible deficiencies in the model used to create first-guess fields: (i) the physical processes responsible for the observed trend (e.g., an increase of greenhouse gases) are completely absent from the model, (ii) the first-guess fields are affected by an initial drift caused by the imbalance between the model equilibrium and the analysis that contains trends due to the observations, and (iii) the model used in the reanalysis has a constant model bias. The imbalance contributes to a systematic reduction in the reanalysis trend compared to the observations. The analytic derivation herein shows that this systematic reduction can be very small (less than 5%) wh...

36 citations


Journal ArticleDOI
TL;DR: The error growth, that is, the growth in the distance E between two typical solutions of a weather model, is investigated and it is argued this behavior is quite different from other dynamics problems with saturation values, which yield concave graphs.
Abstract: We investigate the error growth, that is, the growth in the distance E between two typical solutions of a weather model. Typically E grows until it reaches a saturation value E(s). We find two distinct broad log-linear regimes, one for E below 2% of E(s) and the other for E above. In each, log (E/E(s)) grows as if satisfying a linear differential equation. When plotting d log(E)/dt vs log(E), the graph is convex. We argue this behavior is quite different from other dynamics problems with saturation values, which yield concave graphs.

31 citations


Book ChapterDOI
28 Jan 2005
TL;DR: In this paper, the authors present the longest available record of the skill of numerical weather prediction using the S 1 score (Teweles and Wobus, 1954), which measures the relative error in the horizontal gradient of the height of the constant pressure surface of 500 hPa (in the middle of the atmosphere) for 36-h forecasts over North America.
Abstract: Introduction In general, the public is not aware that our daily weather forecasts start out as initial-value problems on the major national weather services supercomputers. Numerical weather prediction provides the basic guidance for weather forecasting beyond the first few hours. For example, in the USA, computer weather forecasts issued by the National Center for Environmental Prediction (NCEP) in Washington, DC, guide forecasts from the US National Weather Service (NWS). NCEP forecasts are performed by running (integrating in time) computer models of the atmosphere that can simulate, given one day's weather observations, the evolution of the atmosphere in the next few days. Because the time integration of an atmospheric model is an initial-value problem , the ability to make a skillful forecast requires both that the computer model be a realistic representation of the atmosphere , and that the initial conditions be known accurately . NCEP (formerly the National Meteorological Center or NMC) has performed operational computer weather forecasts since the 1950s. From 1955 to 1973, the forecasts included only the Northern Hemisphere; they have been global since 1973. Over the years, the quality of the models and methods for using atmospheric observations has improved continuously, resulting in major forecast improvements. Figure 1.1.1(a) shows the longest available record of the skill of numerical weather prediction. The “ S 1” score (Teweles and Wobus, 1954) measures the relative error in the horizontal gradient of the height of the constant pressure surface of 500 hPa (in the middle of the atmosphere, since the surface pressure is about 1000 hPa) for 36-h forecasts over North America.

9 citations