The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model

2014 ◽  
Vol 44 (9-10) ◽  
pp. 2723-2735 ◽  
Author(s):  
J. Baehr ◽  
K. Fröhlich ◽  
M. Botzet ◽  
D. I. V. Domeisen ◽  
L. Kornblueh ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Climate Model ◽  
Seasonal Prediction ◽  
Prediction System ◽  
Coupled Climate Model

scholarly journals The IITM Earth System Model: Transformation of a Seasonal Prediction Model to a Long-Term Climate Model

2015 ◽  
Vol 96 (8) ◽  
pp. 1351-1367 ◽  
Author(s):  
P. Swapna ◽  
M. K. Roxy ◽  
K. Aparna ◽  
K. Kulkarni ◽  
A. G. Prajeesh ◽  
...  
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Mixed Layer ◽  
Climate Model ◽  
Seasonal Prediction ◽  
Earth System Model ◽  
System Model ◽  
Cold Bias ◽  
Earth System ◽  
Global Mean

Abstract With the goal of building an Earth system model appropriate for detection, attribution, and projection of changes in the South Asian monsoon, a state-of-the-art seasonal prediction model, namely the Climate Forecast System version 2 (CFSv2) has been adapted to a climate model suitable for extended climate simulations at the Indian Institute of Tropical Meteorology (IITM), Pune, India. While the CFSv2 model has been skillful in predicting the Indian summer monsoon (ISM) on seasonal time scales, a century-long simulation with it shows biases in the ocean mixed layer, resulting in a 1.5°C cold bias in the global mean surface air temperature, a cold bias in the sea surface temperature (SST), and a cooler-than-observed troposphere. These biases limit the utility of CFSv2 to study climate change issues. To address biases, and to develop an Indian Earth System Model (IITM ESMv1), the ocean component in CFSv2 was replaced at IITM with an improved version, having better physics and interactive ocean biogeochemistry. A 100-yr simulation with the new coupled model (with biogeochemistry switched off) shows substantial improvements, particularly in global mean surface temperature, tropical SST, and mixed layer depth. The model demonstrates fidelity in capturing the dominant modes of climate variability such as the ENSO and Pacific decadal oscillation. The ENSO–ISM teleconnections and the seasonal leads and lags are also well simulated. The model, a successful result of Indo–U.S. collaboration, will contribute to the IPCC’s Sixth Assessment Report (AR6) simulations, a first for India.

scholarly journals A realistic Greenland ice sheet and surrounding glaciers and ice caps melting in a coupled climate model

2021 ◽  
Author(s):  
Marion Devilliers ◽  
Didier Swingedouw ◽  
Juliette Mignot ◽  
Julie Deshayes ◽  
Gilles Garric ◽  
...  
Keyword(s):  
Climate Model ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Coupled Climate Model ◽  
Ice Caps

scholarly journals Tropical Cyclone Count Forecasting Using a Dynamical Seasonal Prediction System: Sensitivity to Improved Ocean Initialization

2011 ◽  
Vol 24 (12) ◽  
pp. 2963-2982 ◽  
Author(s):  
Andrea Alessandri ◽  
Andrea Borrelli ◽  
Silvio Gualdi ◽  
Enrico Scoccimarro ◽  
Simona Masina
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Wind Shear ◽  
Large Scale ◽  
Initial Conditions ◽  
North Pacific Ocean ◽  
Convective Available Potential Energy ◽  
Seasonal Prediction ◽  
Boreal Summer ◽  
Prediction System

Abstract This study investigates the predictability of tropical cyclone (TC) seasonal count anomalies using the Centro Euro-Mediterraneo per i Cambiamenti Climatici–Istituto Nazionale di Geofisica e Vulcanologia (CMCC-INGV) Seasonal Prediction System (SPS). To this aim, nine-member ensemble forecasts for the period 1992–2001 for two starting dates per year were performed. The skill in reproducing the observed TC counts has been evaluated after the application of a TC location and tracking detection method to the retrospective forecasts. The SPS displays good skill in predicting the observed TC count anomalies, particularly over the tropical Pacific and Atlantic Oceans. The simulated TC activity exhibits realistic geographical distribution and interannual variability, thus indicating that the model is able to reproduce the major basic mechanisms that link the TCs’ occurrence with the large-scale circulation. TC count anomalies prediction has been found to be sensitive to the subsurface assimilation in the ocean for initialization. Comparing the results with control simulations performed without assimilated initial conditions, the results indicate that the assimilation significantly improves the prediction of the TC count anomalies over the eastern North Pacific Ocean (ENP) and northern Indian Ocean (NI) during boreal summer. During the austral counterpart, significant progresses over the area surrounding Australia (AUS) and in terms of the probabilistic quality of the predictions also over the southern Indian Ocean (SI) were evidenced. The analysis shows that the improvement in the prediction of anomalous TC counts follows the enhancement in forecasting daily anomalies in sea surface temperature due to subsurface ocean initialization. Furthermore, the skill changes appear to be in part related to forecast differences in convective available potential energy (CAPE) over the ENP and the North Atlantic Ocean (ATL), in wind shear over the NI, and in both CAPE and wind shear over the SI.

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