Introduction and systematic assessment for IAP numerical annual climate prediction system

2004 ◽  
Vol 48 (25) ◽  
pp. 56
Author(s):  
Hong CHEN
Keyword(s):  
Climate Prediction ◽  
Prediction System ◽  
Systematic Assessment

scholarly journals Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system

2018 ◽  
Vol 12 (4) ◽  
pp. 1137-1156 ◽  
Author(s):  
Paul J. Kushner ◽  
Lawrence R. Mudryk ◽  
William Merryfield ◽  
Jaison T. Ambadan ◽  
Aaron Berg ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Climate Prediction ◽  
Earth System Model ◽  
System Model ◽  
Prediction System ◽  
Earth System ◽  
Seasonal Snow ◽  
Observational Uncertainty ◽  
Seasonal Snow Cover

Abstract. The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.

scholarly journals A Drift-Free Decadal Climate Prediction System for the Community Earth System Model

2019 ◽  
Vol 32 (18) ◽  
pp. 5967-5995 ◽  
Author(s):  
Yoshimitsu Chikamoto ◽  
Axel Timmermann ◽  
Matthew J. Widlansky ◽  
Shaoqing Zhang ◽  
Magdalena A. Balmaseda
Keyword(s):  
Three Dimensional ◽  
Climate Prediction ◽  
System Model ◽  
Prediction System ◽  
Ocean Temperature ◽  
Perfect Model ◽  
Community Earth System Model ◽  
Decadal Climate ◽  
Decadal Climate Prediction

Abstract Performance of a newly developed decadal climate prediction system is examined using the low-resolution Community Earth System Model (CESM). To identify key sources of predictability and determine the role of upper and deeper ocean data assimilation, we first conduct a series of perfect model experiments. These experiments reveal the importance of upper ocean temperature and salinity assimilation in reducing sea surface temperature biases. However, to reduce biases in the sea surface height, data assimilation below 300 m in the ocean is necessary, in particular for high-latitude regions. The perfect model experiments clearly emphasize the key role of combined three-dimensional ocean temperature and salinity assimilation in reproducing mean state and model trajectories. Applying this knowledge to the realistic decadal climate prediction system, we conducted an ensemble of ocean assimilation simulations with the fully coupled CESM covering the period 1960–2014. In this system, we assimilate three-dimensional ocean temperature and salinity data into the ocean component of CESM. Instead of assimilating direct observations, we assimilate temperature and salinity anomalies obtained from the ECMWF Ocean Reanalysis version 4 (ORA-S4). Anomalies are calculated relative to the sum of the ORA-S4 climatology and an estimate of the externally forced signal. As a result of applying the balanced ocean conditions to the model, our hindcasts show only very little drift and initialization shocks. This new prediction system exhibits multiyear predictive skills for decadal climate variations of the Atlantic meridional overturning circulation (AMOC) and North Pacific decadal variability.

scholarly journals Bias and Drift of the Medium-Range Decadal Climate Prediction System (MiKlip) validated by European Radiosonde Data

2016 ◽  
Vol 25 (6) ◽  
pp. 709-720 ◽  
Author(s):  
Margit Pattantyús-Ábrahám ◽  
Christopher Kadow ◽  
Sebastian Illing ◽  
Wolfgang A. Müller ◽  
Holger Pohlmann ◽  
...  
Keyword(s):  
Climate Prediction ◽  
Radiosonde Data ◽  
Prediction System ◽  
Medium Range ◽  
Decadal Climate ◽  
Decadal Climate Prediction

The IRI Seasonal Climate Prediction System

WRPMD'99 ◽  
1999 ◽  
Author(s):  
Simon J. Mason ◽  
Lisa Goddard ◽  
Nicholas E. Graham ◽  
Elena Yulaeva ◽  
Liqiang Sun ◽  
...  
Keyword(s):  
Climate Prediction ◽  
Prediction System ◽  
Seasonal Climate ◽  
Seasonal Climate Prediction

The Republic of Korea-Pacific Islands Climate Prediction Services Project

2018 ◽  
Vol 99 (2) ◽  
pp. 253-257
Author(s):  
Soo-Jin Sohn ◽  
WonMoo Kim ◽  
Jin Ho Yoo ◽  
Yun-Young Lee ◽  
Sang Myeong Oh ◽  
...  
Keyword(s):  
Pacific Islands ◽  
Seasonal Prediction ◽  
Climate Prediction ◽  
Tropical Pacific ◽  
Republic Of Korea ◽  
Prediction System ◽  
Physical Relationship ◽  
The Pacific ◽  
The Republic ◽  
The Tropical Pacific

Abstract Seasonal prediction provides critical information for the tropical Pacific region, where the economy and livelihood is highly dependent on climate variability. While the highest skills of dynamical prediction systems are usually found in the tropical Pacific, National Hydrological and Meteorological Services (NHMS) in the Pacific Islands Countries (PICs) do not take full advantage of such scientific achievements. The Republic of Korea-Pacific Islands Climate Prediction Services (ROK-PI CliPS) project aims to help PICs produce regionally tailored climate prediction information using a dynamical seasonal prediction system. The project is being jointly implemented by the APEC Climate Center (APCC) and the Secretariat of the Pacific Regional Environment Programme (SPREP), in close collaboration with NHMSs in PICs. The regionally tailored, dynamical-statistical hybrid climate prediction system uses predictors that were identified through communications with NHMSs. The predictors were selected based on the empirical physical relationship of the local climate fluctuations, indicated by multi-institutional and multimodel ensembles. This hybrid system makes full use of dynamical seasonal predictions, which have not been commonly utilized in current operation in PICs. In accordance with system development, additional efforts have been made for PIC NHMSs to build capacity by increasing their knowledge and skill needed to develop such methodologies and systems. Nonetheless, the successive and strategic efforts to sustain and further improve climate predictions in the Pacific Islands region are required.

scholarly journals Predicting Climate Change over the multi-annual range: a perspective from CMCC Decadal Prediction System

2021 ◽  
Author(s):  
Dario Nicolì ◽  
Alessio Bellucci ◽  
Paolo Ruggieri ◽  
Panos Athanasiadis ◽  
Giusy Fedele ◽  
...  
Keyword(s):  
Climate Change ◽  
North Atlantic ◽  
Decadal Variability ◽  
Climate Prediction ◽  
Added Value ◽  
Prediction System ◽  
Decadal Prediction ◽  
Predictive Skill ◽  
Annual Range ◽  
Decadal Climate

<p>After the early pioneering studies during the 2000s, and the first coordinated multi-model effort within the framework of the 5th Coupled Model Inter-comparison Project (CMIP5) in early 2010s, decadal climate predictions are now entering a more mature phase of their historical development. Near-term climate prediction activities have been recently endorsed by the World Climate Research Programme (WCRP) as one of the Grand Challenges in climate science research, and the Lead Centre for Annual-to-Decadal Climate Prediction, collecting hindcasts and forecasts from several contributing centres worldwide has been established by the WMO.</p><p>Here we present results from the CMIP6 DCPP-A decadal hindcasts produced with the CMCC decadal prediction system (CMCC DPS), based on the fully-coupled CMCC-CM2-SR5 dynamical model. A 10-member suite of 10-year retrospective forecasts, initialized every year from 1960 to 2019, is performed using a full-field initialization strategy.</p><p>The predictive skill for key quantities is assessed and compared with a non-initialized historical simulation, so as to verify the added value of initialization. In particular, the CMCC DPS is capable to skilfully reproduce past-climate surface temperature over the North Atlantic ocean, the Indian ocean and the Western Pacific ocean, as well as over most part of the continents. Beyond the contribution of the climate change, predictive skill emerges, among other regions, for the subpolar North Atlantic sea-surface temperatures, resembling the imprint of the extra-tropical part of the Atlantic Multidecadal Variability.</p><p>In terms of precipitation, CMCC DPS is able to capture most of the decadal variability over the Northern part of the Eurasian continent. Indeed, a set of regional diagnostics is aimed to investigate the process at stake behind this high predictive skill.</p>

Sign in / Sign up

Export Citation Format

Share Document