scholarly journals Sources of Subseasonal Predictability over CONUS during Boreal Summer

2021 ◽  
pp. 1-52
Author(s):  
V. Krishnamurthy ◽  
Jessica Meixner ◽  
Lydia Stefanova ◽  
Jiande Wang ◽  
Denise Worthen ◽  
...  
Keyword(s):  
Time Scale ◽  
Southern Oscillation ◽  
Intraseasonal Oscillation ◽  
Coupled Model ◽  
Warming Trend ◽  
Adaptive Method ◽  
Horizontal Wind ◽  
Boreal Summer ◽  
Forecast Errors ◽  
Modes Of Variability

AbstractThe predictability of the Unified Forecast System (UFS) Coupled Model Prototype 2 developed by the National Centers for Environmental Prediction is assessed for the boreal summer over the continental United States (CONUS). The retrospective forecasts of low-level horizontal wind, precipitation and 2m temperature for 2011–2017 are examined to determine the predictability at subseasonal time scale. Using a data-adaptive method, the leading modes of variability are obtained and identified to be related to El Niño-Southern Oscillation (ENSO), intraseasonal oscillation (ISO) and warming trend. In a new approach, the sources of enhanced predictability are identified by examining the forecast errors and correlations in the weekly averages of the leading modes of variability. During the boreal summer, the ISO followed by the trend in UFS are found to provide better predictability in weeks 1–4 compared to the ENSO mode and the total anomaly. The western CONUS seems to have better predictability on weekly time scale in all the three modes.

A Study of Impacts of Coupled Model Initial Shocks and State–Parameter Optimization on Climate Predictions Using a Simple Pycnocline Prediction Model

2011 ◽  
Vol 24 (23) ◽  
pp. 6210-6226 ◽  
Author(s):  
S. Zhang
Keyword(s):  
Time Scales ◽  
Parameter Optimization ◽  
Time Scale ◽  
Model Simulation ◽  
Coupled Model ◽  
State Parameter ◽  
Model Parameters ◽  
Forecast Errors ◽  
Long Time ◽  
The Impact

Abstract A skillful decadal prediction that foretells varying regional climate conditions over seasonal–interannual to multidecadal time scales is of societal significance. However, predictions initialized from the climate-observing system tend to drift away from observed states toward the imperfect model climate because of the model biases arising from imperfect model equations, numeric schemes, and physical parameterizations, as well as the errors in the values of model parameters. Here, a simple coupled model that simulates the fundamental features of the real climate system and a “twin” experiment framework are designed to study the impact of initialization and parameter optimization on decadal predictions. One model simulation is treated as “truth” and sampled to produce “observations” that are assimilated into other simulations to produce observation-estimated states and parameters. The degree to which the model forecasts based on different estimates recover the truth is an assessment of the impact of coupled initial shocks and parameter optimization on climate predictions of interests. The results show that the coupled model initialization through coupled data assimilation in which all coupled model components are coherently adjusted by observations minimizes the initial coupling shocks that reduce the forecast errors on seasonal–interannual time scales. Model parameter optimization with observations effectively mitigates the model bias, thus constraining the model drift in long time-scale predictions. The coupled model state–parameter optimization greatly enhances the model predictability. While valid “atmospheric” forecasts are extended 5 times, the decadal predictability of the “deep ocean” is almost doubled. The coherence of optimized model parameters and states is critical to improve the long time-scale predictions.

Predictability of extreme events in UFS at subseasonal time scale

2021 ◽  
Author(s):  
Christiana Stan
Keyword(s):  
Extreme Events ◽  
Time Scale ◽  
Initial Conditions ◽  
Coupled Model ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Forecast System ◽  
Cold Events ◽  
Second Category

<p>The predictability of extreme events over the continental United States (CONUS) in the Unified Forecast System (UFS) Couple Model is studied at subseasonal time scale. The benchmark runs of UFS (GFSv15), a coupled model consisting of atmospheric component (FV3GFS) with 28 km resolution and ocean (MOM6) and sea ice (CICE5) components with global 0.25° resolution, for the period April 2011–December 2017 have been assessed. The model’s month-long forecasts initiated on the first and fifteenth of each month are used to examine the predictability of extreme events in precipitation and 2m temperature. The atmospheric and ice initial conditions are from CFSR data, and the ocean initial conditions are from 3Dvar CPC. The errors in the week 1–4 predictions and the corresponding spatial correlation between model and observation over CONUS are presented. The differences in the predictability of the extreme events between the boreal summer and winter are discussed. Two categories of extreme events are evaluated: 95<sup>th</sup> and 99<sup>th</sup> percentile, respectively. The forecast skill of extreme events in the 95<sup>th</sup> percentile is higher than the forecast skill of events in the second category. The forecast skill of warm and cold events in the 95<sup>th</sup> percentile shows seasonal dependence and is higher during the boreal winter.</p>

scholarly journals Aerosol meteorology of the Maritime Continent for the 2012 7SEAS southwest monsoon intensive study – Part 1: regional-scale phenomena

2016 ◽  
Vol 16 (22) ◽  
pp. 14041-14056 ◽  
Author(s):  
Jeffrey S. Reid ◽  
Peng Xian ◽  
Brent N. Holben ◽  
Edward J. Hyer ◽  
Elizabeth A. Reid ◽  
...  
Keyword(s):  
Biomass Burning ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Regional Scale ◽  
Intraseasonal Oscillation ◽  
Southwest Monsoon ◽  
Boreal Summer ◽  
Maritime Continent ◽  
Fire Activity

Abstract. The largest 7 Southeast Asian Studies (7SEAS) operation period within the Maritime Continent (MC) occurred in the August–September 2012 biomass burning season. Included was an enhanced deployment of Aerosol Robotic Network (AERONET) sun photometers, multiple lidars, and field measurements to observe transported smoke and pollution as it left the MC and entered the southwest monsoon trough. Here we describe the nature of the overall 2012 southwest monsoon (SWM) and biomass burning season to give context to the 2012 deployment. The MC in 2012 was in a slightly warm El Niño/Southern Oscillation (ENSO) phase and with spatially typical burning activity. However, overall fire counts for 2012 were 10 % lower than the Reid et al. (2012) baseline, with regions of significant departures from this norm, ranging from southern Sumatra (+30 %) to southern Kalimantan (−42 %). Fire activity and monsoonal flows for the dominant burning regions were modulated by a series of intraseasonal oscillation events (e.g., Madden–Julian Oscillation, or MJO, and boreal summer intraseasonal oscillation, or BSISO). As is typical, fire activity systematically progressed eastward over time, starting with central Sumatran fire activity in June related to a moderately strong MJO event which brought drier air from the Indian Ocean aloft and enhanced monsoonal flow. Further burning in Sumatra and Kalimantan Borneo occurred in a series of significant events from early August to a peak in the first week of October, ending when the monsoon started to migrate back to its wintertime northeastern flow conditions in mid-October. Significant monsoonal enhancements and flow reversals collinear with tropical cyclone (TC) activity and easterly waves were also observed. Islands of the eastern MC, including Sulawesi, Java, and Timor, showed less sensitivity to monsoonal variation, with slowly increasing fire activity that also peaked in early October but lingered into November. Interestingly, even though fire counts were middling, resultant AERONET 500 nm aerosol optical thickness (AOT) from fire activity was high, with maximums of 3.6 and 5.6 in the Sumatra and Kalimantan source regions at the end of the burning season and an average of ∼ 1. AOTs could also be high at receptor sites, with a mean and maximum of 0.57 and 1.24 in Singapore and 0.61 and 0.8 in Kuching Sarawak. Ultimately, outside of the extreme 2015 El Niño event, average AERONET AOT values were higher than any other time since sites were established. Thus, while satellite fire data, models, and AERONET all qualitatively agree on the nature of smoke production and transport, the MC's complex environment resulted in clear differences in quantitative interpretation of these datasets.

Interdecadal Change in the Relationship between ENSO and the Intraseasonal Oscillation in East Asia

2010 ◽  
Vol 23 (13) ◽  
pp. 3599-3612 ◽  
Author(s):  
Kyung-Sook Yun ◽  
Kyong-Hwan Seo ◽  
Kyung-Ja Ha
Keyword(s):  
Indian Ocean ◽  
North Pacific ◽  
Southern Oscillation ◽  
Asian Summer Monsoon ◽  
Intraseasonal Oscillation ◽  
Longwave Radiation ◽  
Strong Relationship ◽  
Early Summer ◽  
Boreal Summer ◽  
Interdecadal Change

Abstract The northward-propagating intraseasonal oscillation (NPISO) during the boreal summer is closely linked to the onset/retreat and intensity of the East Asian summer monsoon (EASM). In this study, interdecadal variability in the relationships between the NPISO and El Niño–Southern Oscillation (ENSO) was investigated using long-term outgoing longwave radiation data obtained from the 40-yr ECMWF Re-Analysis (ERA-40) for a 44-yr period (1958 to 2001). It was found that before the late 1970s, the preceding winter ENSO influenced the early summer (i.e., May to June) NPISO activity, whereas after the late 1970s a strong relationship appeared during the later summertime (i.e., July to August). The May–June NPISO before the late 1970s was modulated by springtime Indian Ocean sea surface temperature warming and central North Pacific suppressed convection anomalies and was consequently related to the ENSO-induced west Pacific (WP) pattern, which shows a north–south dipole structure over the North Pacific from winter through spring. After the late 1970s, because of an anomalously strengthened Walker–Hadley circulation, Indian Ocean SST warming was significantly maintained until summer, which promoted a strong suppressed convection anomaly over the Philippine Sea during summer and consequently an enhanced western North Pacific subtropical high and Pacific–Japan (PJ) pattern.

scholarly journals Assessment of Modes of Interannual Variability of Southern Hemisphere Atmospheric Circulation in CMIP5 Models

2014 ◽  
Vol 27 (21) ◽  
pp. 8107-8125 ◽  
Author(s):  
Simon Grainger ◽  
Carsten S. Frederiksen ◽  
Xiaogu Zheng
Keyword(s):  
Interannual Variability ◽  
Southern Hemisphere ◽  
Southern Oscillation ◽  
Slow Component ◽  
Coupled Model ◽  
Reanalysis Data ◽  
Cmip5 Models ◽  
Noise Component ◽  
Modes Of Variability ◽  
Multimodel Ensembles

Abstract An assessment is made of the modes of interannual variability in the seasonal mean summer and winter Southern Hemisphere (SH) 500-hPa geopotential height in the twentieth century in models from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) dataset. Modes of variability of both the slow (signal) and intraseasonal (noise) components in the CMIP5 models are evaluated against those estimated from reanalysis data. There is general improvement in the leading modes of the slow (signal) component in CMIP5 models compared with the CMIP phase 3 (CMIP3) dataset. The largest improvement is in the spatial structures of the modes related to El Niño–Southern Oscillation variability in SH summer. An overall score metric is significantly higher for CMIP5 over CMIP3 in both seasons. The leading modes in the intraseasonal noise component are generally well reproduced in CMIP5 models, and there are few differences from CMIP3. A new total overall score metric is used to rank the CMIP5 models over both seasons. Weighting the seasons by the relative spread of overall scores is shown to be suitable for generating multimodel ensembles for further analysis of interannual variability. In multimodel ensembles, it is found that an ensemble of size 5 or 6 is sufficient in SH summer to reproduce well the dominant modes. In contrast, about 13 models are typically are required in SH winter. It is shown that it is necessary that the selected models individually reproduce well the leading modes of the slow component.

scholarly journals Prediction of Arctic temperature and sea ice using a high-resolution coupled model

2021 ◽  
pp. 1-62
Author(s):  
Le Chang ◽  
Jing-Jia Luo ◽  
Jiaqing Xue ◽  
Haiming Xu ◽  
Nick Dunstone
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Air Temperature ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Arctic Region ◽  
Warming Trend ◽  
Lower Atmosphere ◽  
The Arctic

AbstractUnder global warming, surface air temperature has risen rapidly and sea ice decreased markedly in the Arctic. These drastic climate changes have brought about various severe impacts on the vulnerable environment and ecosystem there. Thus, accurate prediction of Arctic climate becomes more important than before. Here we examine the seasonal to interannual predictive skills of 2-meter air temperature (2-m T) and sea ice cover (SIC) over the Arctic region (70°∼90°N) during 1980–2014 with a high-resolution global coupled model called the Met Office Decadal Prediction System version 3 (DePreSys3). The model captures well both the climatology and interannual variability of the Arctic 2-m T and SIC. Moreover, the anomaly correlation coefficient (ACC) of Arctic-averaged 2-m T and SIC shows statistically significant skills at lead times up to 16 months. This is mainly due to the contribution of strong decadal trends. In addition, it is found that the peak warming trend of Arctic 2-m T lags the maximum decrease trend of SIC by one month, in association with the heat flux forcing from the ocean surface to lower atmosphere. While the predictive skill is generally much lower for the detrended variations, we find a close relationship between the tropical Pacific El Niño–Southern Oscillation and the Arctic detrended 2-m T anomalies. This indicates potential seasonal to interannual predictability of the Arctic natural variations.

scholarly journals The Met Office Global Coupled model 2.0 (GC2) configuration

2015 ◽  
Vol 8 (5) ◽  
pp. 1509-1524 ◽  
Author(s):  
K. D. Williams ◽  
C. M. Harris ◽  
A. Bodas-Salcedo ◽  
J. Camp ◽  
R. E. Comer ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Coupled Model ◽  
Diagnostic Techniques ◽  
Seasonal Forecast ◽  
West African ◽  
Madden Julian Oscillation ◽  
Forecast System ◽  
Projection System ◽  
Modes Of Variability ◽  
Model Components

Abstract. The latest coupled configuration of the Met Office Unified Model (Global Coupled configuration 2, GC2) is presented. This paper documents the model components which make up the configuration (although the scientific description of these components is detailed elsewhere) and provides a description of the coupling between the components. The performance of GC2 in terms of its systematic errors is assessed using a variety of diagnostic techniques. The configuration is intended to be used by the Met Office and collaborating institutes across a range of timescales, with the seasonal forecast system (GloSea5) and climate projection system (HadGEM) being the initial users. In this paper GC2 is compared against the model currently used operationally in those two systems. Overall GC2 is shown to be an improvement on the configurations used currently, particularly in terms of modes of variability (e.g. mid-latitude and tropical cyclone intensities, the Madden–Julian Oscillation and El Niño Southern Oscillation). A number of outstanding errors are identified with the most significant being a considerable warm bias over the Southern Ocean and a dry precipitation bias in the Indian and West African summer monsoons. Research to address these is ongoing.

scholarly journals Improving EnKF-Based Initialization for ENSO Prediction Using a Hybrid Adaptive Method

2016 ◽  
Vol 29 (20) ◽  
pp. 7365-7381 ◽  
Author(s):  
Xinrong Wu
Keyword(s):  
Southern Oscillation ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Coupled Model ◽  
Adaptive Method ◽  
Sampling Errors ◽  
Enso Prediction ◽  
Probabilistic Forecasts ◽  
Intermediate Coupled Model ◽  
Sst Anomaly

Abstract Probabilistic forecasts, which are usually initialized by an ensemble Kalman filter (EnKF), are known to be better than deterministic (or one member) forecasts for the El Niño–Southern Oscillation (ENSO) phenomenon. Because of sampling errors caused by a finite ensemble and the errors related to model biases associated with the physical parameterizations, dynamic core, model resolution, and so on, a state-of-the-art inflation method is commonly used in the standard EnKF to increase the prior variance so as to avoid filter divergence. However, the optimal inflation factor is almost prohibitive in reality because of vast computational cost. An adaptive EnKF and multigrid analysis hybrid approach without inflation is presented to compensate for the abovementioned limitations of the standard EnKF. In this study, the adaptive approach is applied to an intermediate coupled model for ENSO prediction. Gridded observations of daily-mean sea surface temperature (SST) anomalies from the Advanced Very High Resolution Radiometer (AVHRR) during January 1982–December 2012 are assimilated into the model to initialize a 2-yr ENSO hindcast. Results show that compared to the standard EnKF that uses multiplicative variance inflation, the adaptive method can reduce analysis errors by 63% for both the daily SST anomaly and the Niño-1+2 SST anomaly. The prediction skill of Niño-1+2 SST anomaly is consistently enhanced, especially for phase forecast. For SST anomaly forecasting, the advantage of the adaptive method mainly occurs in the eastern equatorial Pacific and the northern boundary of the intermediate coupled model.

scholarly journals Improved ENSO Prediction by Singular Vector Analysis in a Hybrid Coupled Model

2009 ◽  
Vol 26 (3) ◽  
pp. 626-634
Author(s):  
Xiaobing Zhou ◽  
Youmin Tang ◽  
Yanjie Cheng ◽  
Ziwang Deng
Keyword(s):  
Prediction Model ◽  
Singular Vector ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Optimal Growth ◽  
Growth Patterns ◽  
Prediction Skill ◽  
Vector Analysis ◽  
Forecast Errors ◽  
Enso Prediction

Abstract In this study, a method based on singular vector analysis is proposed to improve El Niño–Southern Oscillation (ENSO) predictions. Its essential idea is that the initial errors are projected onto their optimal growth patterns, which are propagated by the tangent linear model (TLM) of the original prediction model. The forecast errors at a given lead time of predictions are obtained, and then removed from the raw predictions. This method is applied to a realistic ENSO prediction model for improving prediction skill for the period from 1980 to 1999. This correction method considerably improves the ENSO prediction skill, compared with the original predictions without the correction.

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