scholarly journals On the Interpretation and Utility of Skill Information for Seasonal Climate Predictions

2007 ◽  
Vol 135 (5) ◽  
pp. 1974-1984 ◽  
Author(s):  
Arun Kumar
Keyword(s):  
Time Series ◽  
General Circulation ◽  
Circulation Model ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Steady Increase ◽  
Seasonal Forecasts ◽  
Past Performance ◽  
Seasonal Climate ◽  
The Past

Abstract In recent years, there has been a steady increase in the emphasis on routine seasonal climate predictions and their potential for enhancing societal benefits and mitigating losses related to climate extremes. It is also suggested by the users, as well as by the producers of climate predictions, that for informed decision making, real-time seasonal climate predictions should be accompanied by a corresponding level of skill estimated from a sequence of the past history of forecasts. In this paper it is discussed whether conveying skill information to the user community can indeed deliver the promised benefits or whether issues inherent in the estimates of seasonal prediction skill may still lead to potential misinterpretation of the information content associated with seasonal predictions. Based on the analysis of atmospheric general circulation model simulations, certain well-known, but often underappreciated, issues inherent in the estimates of seasonal prediction skill from the past performance of seasonal forecasts are highlighted. These include the following: 1) the stability of estimated skill depends on the length of the time series over which seasonal forecasts are verified, leading to scenarios where error bars on the estimated skill could be of the same magnitude as the skill itself; 2) a single estimate of skill obtained from the verification over a given forecast time series, because of variation in the signal-to-noise ratio from one year to another, is generally not representative of seasonal prediction skill conditional to sea surface temperature anomalies on a case-by-case basis. These issues raise questions on the interpretation, presentation, and utilization of skill information for seasonal prediction efforts and present opportunities for increased dialogue and the exploration of ways for their optimal utilization by decision makers.

scholarly journals Analog Downscaling of Seasonal Rainfall Forecasts in the Murray Darling Basin

2013 ◽  
Vol 141 (3) ◽  
pp. 1099-1117 ◽  
Author(s):  
Andrew Charles ◽  
Bertrand Timbal ◽  
Elodie Fernandez ◽  
Harry Hendon
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Seasonal Prediction ◽  
General Circulation Models ◽  
Meridional Wind ◽  
Seasonal Forecasts ◽  
Local Climate ◽  
Continental Scale ◽  
Predictive Skill ◽  
Murray Darling Basin

Abstract Seasonal predictions based on coupled atmosphere–ocean general circulation models (GCMs) provide useful predictions of large-scale circulation but lack the conditioning on topography required for locally relevant prediction. In this study a statistical downscaling model based on meteorological analogs was applied to continental-scale GCM-based seasonal forecasts and high quality historical site observations to generate a set of downscaled precipitation hindcasts at 160 sites in the South Murray Darling Basin region of Australia. Large-scale fields from the Predictive Ocean–Atmosphere Model for Australia (POAMA) 1.5b GCM-based seasonal prediction system are used for analog selection. Correlation analysis indicates modest levels of predictability in the target region for the selected predictor fields. A single best-match analog was found using model sea level pressure, meridional wind, and rainfall fields, with the procedure applied to 3-month-long reforecasts, initialized on the first day of each month from 1980 to 2006, for each model day of 10 ensemble members. Assessment of the total accumulated rainfall and number of rainy days in the 3-month reforecasts shows that the downscaling procedure corrects the local climate variability with no mean effect on predictive skill, resulting in a smaller magnitude error. The amount of total rainfall and number of rain days in the downscaled output is significantly improved over the direct GCM output as measured by the difference in median and tercile thresholds between station observations and downscaled rainfall. Confidence in the downscaled output is enhanced by strong consistency between the large-scale mean of the downscaled and direct GCM precipitation.

scholarly journals Lagged Ensembles, Forecast Configuration, and Seasonal Predictions

2013 ◽  
Vol 141 (10) ◽  
pp. 3477-3497 ◽  
Author(s):  
Mingyue Chen ◽  
Wanqiu Wang ◽  
Arun Kumar
Keyword(s):  
Lead Time ◽  
Geographical Location ◽  
Seasonal Prediction ◽  
Skill Score ◽  
Negative Influence ◽  
Positive Influence ◽  
Prediction Skill ◽  
Ensemble Size ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts

Abstract An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), is presented. The focus of the analysis is on the construction of lagged ensemble forecasts with increasing lead time (thus allowing use of larger ensemble sizes) and its influence on seasonal prediction skill. Predictions of seasonal means of sea surface temperature (SST), 200-hPa height (z200), precipitation, and 2-m air temperature (T2m) over land are analyzed. Measures of prediction skill include deterministic (anomaly correlation and mean square error) and probabilistic [rank probability skill score (RPSS)]. The results show that for a fixed lead time, and as one would expect, the skill of seasonal forecast improves as the ensemble size increases, while for a fixed ensemble size the forecast skill decreases as the lead time becomes longer. However, when a forecast is based on a lagged ensemble, there exists an optimal lagged ensemble time (OLET) when positive influence of increasing ensemble size and negative influence due to an increasing lead time result in a maximum in seasonal prediction skill. The OLET is shown to depend on the geographical location and variable. For precipitation and T2m, OLET is relatively longer and skill gain is larger than that for SST and tropical z200. OLET is also dependent on the skill measure with RPSS having the longest OLET. Results of this analysis will be useful in providing guidelines on the design and understanding relative merits for different configuration of seasonal prediction systems.

scholarly journals Climate of Bangladesh: An Analysis of Northwestern and Southwestern Part Using High Resolution Atmosphere-Ocean General Circulation Model (AOGCM)

1970 ◽  
Vol 9 (1-2) ◽  
pp. 143-154 ◽  
Author(s):  
MA Rouf ◽  
MK Uddin ◽  
SK Debsarma ◽  
M Mizanur Rahman
Keyword(s):  
High Resolution ◽  
General Circulation Model ◽  
General Circulation ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Southwestern Part ◽  
The Past ◽  
Average Rainfall ◽  
Ocean General Circulation ◽  
Near Future

The past, present and future climatic pattern (temperature and rainfall) of northwestern and southwestern part of Bangladesh was assessed based on the High Resolution Atmospheric-Ocean General Circulation Model (AOGCM) using the present rainfall and temperature data of the Bangladesh Meteorological Department (BMD). Climatology in Bangladesh is derived from 20 km mesh MRI-AGCM (Atmospheric General Circulation Model) calibrated with reference to the observed data for the period of 1979-2006. Then, projections for rainfall and temperature are made for near future (2015-2034) and future (2075-99). Two disaster prone areas (i) northwestern part (Shapahar & Porsha) and (ii) southwestern part (Kalapara & Amtoli) were selected as the study areas. AOGCM model was run for Bangladesh and also for study areas separately. The present mean temperature for Bangladesh was found to rise from the past, rises slightly, but in near future and future the rate of mean temperature rise is projected to be much more than the present rate (increase up to 4.34 °C/100 years), the rate is projected to be 5.39 °C/100 years in case of Shapahar and Porsha a while 4.37 °C/100 years in case of Kalapara and Amtoli. The present, near future and future average rainfall of Bangladesh appeared to fluctuate, but have shown a decreasing trend (decreases up to 1.96 mm/100 years). The mean average rainfall of Shapahar and Porsha presently decreases very slowly (not significant), but in near future and future will decrease slowly (0.66mm/100 years). In case of Kalapara, the average rainfall appears to decrease presently, near future and future will decrease up to 3.62 mm/100 years. The average rainfall of Amtoli appears to decrease @ 1.92mm/100 years but in near future appears to increase slightly and again decrease @ 3.27mm/100years in future. Keywords: Atmosphere-Ocean General Circulation Model (AOGCM); climatology; simulation; temperature; rainfall DOI: http://dx.doi.org/10.3329/agric.v9i1-2.9489 The Agriculturists 2011; 9(1&2): 143-154

Influence of gravity wave drag parametrisations on the stratospheric circulation of seasonal ICON-NWP experiments

2020 ◽  
Author(s):  
Raphael Köhler ◽  
Dörthe Handorf ◽  
Ralf Jaiser ◽  
Klaus Dethloff ◽  
Günther Zängl ◽  
...  
Keyword(s):  
Gravity Wave ◽  
General Circulation ◽  
Weather Prediction ◽  
Polar Vortex ◽  
Circulation Model ◽  
Seasonal Prediction ◽  
Wave Drag ◽  
Stratospheric Polar Vortex ◽  
Sensitivity Experiments ◽  
Hemisphere Winter

<p>The stratospheric polar vortex is highly variable in winter and thus, models often struggle to capture its variability and strength. Yet, the influence of the stratosphere on the tropospheric circulation becomes highly important in Northern Hemisphere winter and is one of the main potential sources for subseasonal to seasonal prediction skill in mid latitudes. Mid-latitude extreme weather patterns in winter are often preceded by sudden stratospheric warmings (SSWs), which are the strongest manifestation of the coupling between stratosphere and troposphere. Misrepresentation of the SSW-frequency and stratospheric biases in models can therefore also cause biases in the troposphere.</p><p>In this context this work comprises the analysis of four seasonal ensemble experiments with a high-resolution, nonhydrostatic global atmospheric general circulation model in numerical weather prediction mode (ICON-NWP). The main focus thereby lies on the variability and strength of the stratospheric polar vortex. We identified the gravity wave drag parametrisations as one important factor influencing stratospheric dynamics. As the control experiment with default gravity wave drag settings exhibits an overestimated amount of SSWs and a weak stratospheric polar vortex, three sensitivity experiments with adjusted drag parametrisations were generated. Hence, the parametrisations for the non-orographic gravity wave drag and the subgrid‐scale orographic (SSO) drag were chosen with the goal of strengthening the stratospheric polar vortex. Biases to ERA-Interim are reduced with both adjustments, especially in high latitudes. Whereas the positive effect of the reduced non-orographic gravity wave drag is strongest in the mid-stratosphere in winter, the adjusted SSO-scheme primarily affects the troposphere by reducing mean sea level pressure biases in all months. A fourth experiment using both adjustments exhibits improvements in the troposphere and stratosphere. Although the stratospheric polar vortex in winter is strengthened in all sensitivity experiments, it is still simulated too weak compared to ERA-Interim. Further mechanisms causing this weakness are also investigated in this study.</p>

scholarly journals Use of APHRODITE Rain Gauge–Based Precipitation and TRMM 3B43 Products for Improving Asian Monsoon Seasonal Precipitation Forecasts by the Superensemble Method

2014 ◽  
Vol 27 (3) ◽  
pp. 1062-1069 ◽  
Author(s):  
Akiyo Yatagai ◽  
T. N. Krishnamurti ◽  
Vinay Kumar ◽  
A. K. Mishra ◽  
Anu Simon
Keyword(s):  
Asian Monsoon ◽  
Rain Gauge ◽  
Training Data ◽  
Monthly Precipitation ◽  
Observation Data ◽  
Training Phase ◽  
Multiple Model ◽  
Seasonal Forecasts ◽  
Past Performance ◽  
Seasonal Climate

Abstract A multimodel superensemble developed by the Florida State University combines multiple model forecasts based on their past performance (training phase) to make a consensus forecast. Because observed precipitation reflects local characteristics such as orography, quantitative high-resolution precipitation products are useful for downscaling coarse model outputs. The Asian Precipitation–Highly-Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) and Tropical Rainfall Measuring Mission (TRMM) 3B43 products are used for downscaling and as training data in the superensemble training phase. Seven years (1998–2004) of monthly precipitation (June–August) over the Asian monsoon region (0°–50°N, 60°–150°E) and results of four coupled climate models were used. TRMM 3B43 was adjusted by APHRODITE (m-TRMM). For seasonal climate forecasts, a synthetic superensemble technique was used. A cross-validation technique was adopted, in which the year to be forecast was excluded from the calculations for obtaining the regression coefficients. The principal results are as follows: 1) Seasonal forecasts of Asian monsoon precipitation were considerably improved by use of APHRODITE rain gauge–based data or the m-TRMM product. These forecasts are much superior to those from the best model of the suite and ensemble mean. 2) Use of a statistical downscaling and synthetic superensemble method for multimodel forecasts of seasonal climate significantly improved precipitation prediction at higher resolution. This is confirmed by cross-evaluation of superensemble with using other observation data than the data used in the training phase. 3) Availability of a dense rain gauge network–based analysis was essential for the success of this work.

scholarly journals Impact of solar variability on the frequency variability of the Indian summer monsoon

MAUSAM ◽  
2021 ◽  
Vol 52 (1) ◽  
pp. 67-82
Author(s):  
J. R. KULKARNI ◽  
M. MUJUMDAR ◽  
S. P. GHARGE ◽  
V. SATYAN ◽  
G. B. PANT
Keyword(s):  
Time Series ◽  
Summer Monsoon ◽  
Solar Irradiance ◽  
General Circulation ◽  
Large Scale ◽  
Monsoon Season ◽  
Circulation Model ◽  
Solar Constant ◽  
Good Correspondence ◽  
The Impact

Earlier investigations into the epochal behavior of fluctuations in All India Summer Monsoon Rainfall (AISMR) have indicated the existence of a Low Frequency Mode (LFM) in the 60-70 years range. One of the probable sources of this variability may be due to changes in solar irradiance. To investigate this, time series of 128-year solar irradiance data from 1871-1998 has been examined. The Wavelet Transform (WT) method is applied to extract the LFM from these time series, which show a very good correspondence. A case study has been carried out to test the sensitivity of AISMR to solar irradiance. The General Circulation Model (GCM) of the Center of Ocean-Land-Atmosphere (COLA) has been integrated in the control run (using the climatological value of solar constant i.e., 1365 Wm-2) and in the enhanced solar constant condition (enhanced by 10 Wm-2) for summer monsoon season of 1986. The study shows that the large scale atmospheric circulation over the Indian region, in the enhanced solar constant scenario is favorable to good monsoon activity. A conceptual model for the impact of solar irradiance on the AISMR at LFM is also suggested.

scholarly journals Temperature and rainfall estimates for past 18 000 years in Owens Valley, California with a coupled catchment–lake model

2015 ◽  
Vol 12 (7) ◽  
pp. 6505-6539 ◽  
Author(s):  
Z. Yu ◽  
W. Dong ◽  
P. Jiang
Keyword(s):  
General Circulation ◽  
Regional Climate ◽  
Circulation Model ◽  
Climate Data ◽  
Owens Valley ◽  
Climatic Fluctuations ◽  
Closed Basin ◽  
The Past ◽  
Lake Model ◽  
Physically Based

Abstract. Closed-basin lakes are intricately linked to the hydrological systems and are very sensitive recorders of local hydro-climatic fluctuations. Lake records in closed-basins are usually used to investigate the paleoclimate condition which is critical for understanding the past and predicting the future. In this study, a physically based catchment–lake model was developed to extract quantitative paleoclimate information including temperature and rainfall over the past 18 000 years (ka) from lake records in a hydrologically closed basin in the Owens River Valley, California, US. The initial model inputs were prepared based on current regional climate data, boundary conditions from the General Circulation Model, and fossil proxy data. The inputs subsequently were systematically varied in order to produce the observed lake levels. In this way, a large number of possible paleoclimatic combinations can quickly narrow the possible range of paleoclimatic combinations that could have produced the paleolake level and extension. Finally, a quantitative time-series of paleoclimate information for those key times was obtained.

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