Barrier layer characteristics of the Indian Ocean sector of the Southern Ocean during austral summer and autumn

Barrier layer in the Enderby Basin (EB) and the Australian Antarctic Basin (AAB) during late summer (December & January) and early autumn (February & March) are studied using temperature-salinity profiles collected between 1975 and 2012. A distinct difference in mixed layer depth is observed over the eastern (i.e. the EB) compared to western (i.e. the AAB) side of the Kerguelen Plateau (KP), with shallower mixed layer depths on the eastern side. Mixed layers show an increase from less than 50 m–∼150 m from south to north in the EB. During autumn, the wind strengthens and the upwelling over the eastern side of the KP (i.e. in the AAB) weakens, resulting in deeper mixed layers (∼80 m–100 m) compared to summer. During summer, deep barrier layer (BL) values (∼50 m or more) with porosity less than 0.3 was seen over the Chun Spur region. The fresher melt water from the EB brought by the Fawn trough current (FTC) across the KP may be responsible for the occurrence of BL over the region. During autumn, BL is spread over a much larger area around the Chun Spur, which could be attributed to the increase in the strength of FTC due to the intensification of wind over the region. A thorough study of BL condition over this region is required to understand the processes behind the discrepancies in sea ice conditions

The CLIVAR C20C Project: Which components of the Asian-Australian monsoon circulation variations are forced and reproducible?

A multi-model set of atmospheric simulations forced by historical sea surface temperature (SST) or SSTs plus Greenhouse gases and aerosol forcing agents for the period of 1950–1999 is studied to identify and understand which components of the Asian–Australian monsoon (A–AM) variability are forced and reproducible. The analysis focuses on the summertime monsoon circulations, comparing model results against the observations. The priority of different components of the A–AM circulations in terms of reproducibility is evaluated. Among the subsystems of the wide A–AM, the South Asian monsoon and the Australian monsoon circulations are better reproduced than the others, indicating they are forced and well modeled. The primary driving mechanism comes from the tropical Pacific. The western North Pacific monsoon circulation is also forced and well modeled except with a slightly lower reproducibility due to its delayed response to the eastern tropical Pacific forcing. The simultaneous driving comes from the western Pacific surrounding the maritime continent region. The Indian monsoon circulation has a moderate reproducibility, partly due to its weakened connection to June–July–August SSTs in the equatorial eastern Pacific in recent decades. Among the A–AM subsystems, the East Asian summer monsoon has the lowest reproducibility and is poorly modeled. This is mainly due to the failure of specifying historical SST in capturing the zonal land-sea thermal contrast change across the East Asia. The prescribed tropical Indian Ocean SST changes partly reproduce the meridional wind change over East Asia in several models. For all the A–AM subsystem circulation indices, generally the MME is always the best except for the Indian monsoon and East Asian monsoon circulation indices

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models

A new method for determining the optimal lagged ensemble

Abstract We propose a general methodology for determining the lagged ensemble that minimizes the mean square forecast error. The MSE of a lagged ensemble is shown to depend only on a quantity called the cross‐lead error covariance matrix, which can be estimated from a short hindcast data set and parameterized in terms of analytic functions of time. The resulting parameterization allows the skill of forecasts to be evaluated for an arbitrary ensemble size and initialization frequency. Remarkably, the parameterization also can estimate the MSE of a burst ensemble simply by taking the limit of an infinitely small interval between initialization times. This methodology is applied to forecasts of the Madden Julian Oscillation (MJO) from version 2 of the Climate Forecast System version 2 (CFSv2). For leads greater than a week, little improvement is found in the MJO forecast skill when ensembles larger than 5 days are used or initializations greater than 4 times per day. We find that if the initialization frequency is too infrequent, important structures of the lagged error covariance matrix are lost. Lastly, we demonstrate that the forecast error at leads ≥10 days can be reduced by optimally weighting the lagged ensemble members. The weights are shown to depend only on the cross‐lead error covariance matrix. While the methodology developed here is applied to CFSv2, the technique can be easily adapted to other forecast systems

More reliable coastal SST forecasts from the North American multimodel ensemble

The skill of monthly sea surface temperature (SST) anomaly predictions for large marine ecosystems (LMEs) in coastal regions of the United States and Canada is assessed using simulations from the climate models in the North American Multimodel Ensemble (NMME). The forecasts based on the full ensemble are generally more skillful than predictions from even the best single model. The improvement in skill is particularly noteworthy for probability forecasts that categorize SST anomalies into upper (warm) and lower (cold) terciles. The ensemble provides a better estimate of the full range of forecast values than any individual model, thereby correcting for the systematic over-confidence (under-dispersion) of predictions from an individual model. Probability forecasts, including tercile predictions from the NMME, are used frequently in seasonal forecasts for atmospheric variables and may have many uses in marine resource management