Extended Memory of the Initial Conditions in Long-Range Forecasts of the January 1983 Atmospheric Circulation

1990 ◽  
Vol 3 (4) ◽  
pp. 461-482 ◽  
Author(s):  
Réjean Michaud
Keyword(s):  
Atmospheric Circulation ◽  
Long Range ◽  
Initial Conditions ◽  
Range Forecasts

ASSESSMENT OF LONG-RANGE FORECASTS

Weather ◽  
1975 ◽  
Vol 30 (6) ◽  
pp. 172-181 ◽  
Author(s):  
I. T. Jolliffe ◽  
J. F. Foord
Keyword(s):  
Long Range ◽  
Range Forecasts

Tropical Atlantic Influence on European Heat Waves

2005 ◽  
Vol 18 (15) ◽  
pp. 2805-2811 ◽  
Author(s):  
Christophe Cassou ◽  
Laurent Terray ◽  
Adam S. Phillips
Keyword(s):  
Atmospheric Circulation ◽  
Long Range ◽  
Heat Waves ◽  
Extreme Weather ◽  
Temperature Data ◽  
Tropical Atlantic ◽  
Caribbean Basin ◽  
Climate Anomalies ◽  
Warm Summer ◽  
The Caribbean

Abstract Diagnostics combining atmospheric reanalysis and station-based temperature data for 1950–2003 indicate that European heat waves can be associated with the occurrence of two specific summertime atmospheric circulation regimes. Evidence is presented that during the record warm summer of 2003, the excitation of these two regimes was significantly favored by the anomalous tropical Atlantic heating related to wetter-than-average conditions in both the Caribbean basin and the Sahel. Given the persistence of tropical Atlantic climate anomalies, their seasonality, and their associated predictability, the suggested tropical–extratropical Atlantic connection is encouraging for the prospects of long-range forecasting of extreme weather in Europe.

An application of chaotic theory in long range forecasts

1992 ◽  
Vol 50 (4) ◽  
pp. 231-236 ◽  
Author(s):  
Zhen-Shan Lin ◽  
Shi-Da Liu
Keyword(s):  
Long Range ◽  
Chaotic Theory ◽  
Range Forecasts

Simple model for long-range forecasts of AT500 hPa level fluctuations

1988 ◽  
Vol 32 (4) ◽  
pp. 392-415
Author(s):  
František Pechala ◽  
O. Zikmunda
Keyword(s):  
Simple Model ◽  
Long Range ◽  
Range Forecasts

scholarly journals An Analysis of the Nonstationarity in the Bias of Sea Surface Temperature Forecasts for the NCEP Climate Forecast System (CFS) Version 2

2012 ◽  
Vol 140 (9) ◽  
pp. 3003-3016 ◽  
Author(s):  
A. Kumar ◽  
M. Chen ◽  
L. Zhang ◽  
W. Wang ◽  
Y. Xue ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Long Range ◽  
Initial Conditions ◽  
Sea Surface ◽  
Climate Forecast ◽  
Prediction System ◽  
Forecast System ◽  
Climate Forecast System ◽  
Forecast Bias

Abstract For long-range predictions (e.g., seasonal), it is a common practice for retrospective forecasts (also referred to as the hindcasts) to accompany real-time predictions. The necessity for the hindcasts stems from the fact that real-time predictions need to be calibrated in an attempt to remove the influence of model biases on the predicted anomalies. A fundamental assumption behind forecast calibration is the long-term stationarity of forecast bias that is derived based on hindcasts. Hindcasts require specification of initial conditions for various components of the prediction system (e.g., ocean, atmosphere) that are generally taken from a long reanalysis. Trends and discontinuities in the reanalysis that are either real or spurious can arise due to several reasons, for example, the changing observing system. If changes in initial conditions were to persist during the forecast, there is a potential for forecast bias to depend over the period it is computed, making calibration even more of a challenging task. In this study such a case is discussed for the recently implemented seasonal prediction system at the National Centers for Environmental Prediction (NCEP), the Climate Forecast System version 2 (CFS.v2). Based on the analysis of the CFS.v2 for 1981–2009, it is demonstrated that the characteristics of the forecast bias for sea surface temperature (SST) in the equatorial Pacific had a dramatic change around 1999. Furthermore, change in the SST forecast bias, and its relationship to changes in the ocean reanalysis from which the ocean initial conditions for hindcasts are taken is described. Implications for seasonal and other long-range predictions are discussed.

Quantifying Uncertainty for Climate Change and Long-Range Forecasting Scenarios with Model Errors. Part I: Gaussian Models

2012 ◽  
Vol 25 (13) ◽  
pp. 4523-4548 ◽  
Author(s):  
Boris Gershgorin ◽  
Andrew J. Majda
Keyword(s):  
Climate Change ◽  
Long Range ◽  
Initial Conditions ◽  
Climate Science ◽  
Coarse Graining ◽  
External Perturbation ◽  
Climate Change Scenarios ◽  
Model Errors ◽  
Gaussian Models ◽  
Time Periodic

Abstract Information theory provides a concise systematic framework for measuring climate consistency and sensitivity for imperfect models. A suite of increasingly complex physically relevant linear Gaussian models with time periodic features mimicking the seasonal cycle is utilized to elucidate central issues that arise in contemporary climate science. These include the role of model error, the memory of initial conditions, and effects of coarse graining in producing short-, medium-, and long-range forecasts. In particular, this study demonstrates how relative entropy can be used to improve climate consistency of an overdamped imperfect model by inflating stochastic forcing. Moreover, the authors show that, in the considered models, by improving climate consistency, this simultaneously increases the predictive skill of an imperfect model in response to external perturbation, a property of crucial importance in the context of climate change. The three models range in complexity from a scalar time periodic model mimicking seasonal fluctuations in a mean jet to a spatially extended system of turbulent Rossby waves to, finally, the behavior of a turbulent tracer with a mean gradient with the background turbulent field velocity generated by the first two models. This last model mimics the global and regional behavior of turbulent passive tracers under various climate change scenarios. This detailed study provides important guidelines for extending these strategies to more complicated and non-Gaussian physical systems.

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