2. Climate

 

Data assembling, seasonal forecast and climate projections

This module provides seasonal meteorological predictions and makes it possible to better predict the nature, onset and withdrawal of recurring weather phenomena such as the monsoon and El Niño and associated extreme events. In addition, trends caused by anthropogenic climate change are broken down into regional scenarios.


MONSOON FORECASTING

Prior knowledge of the date of monsoon onset is of vital importance in India. More lead time for monsoon forecasts is crucial for planning agriculture, water and energy resources management. The EPICC project aims to provide Indian population with a long-term forecast of the onset and withdrawal of the Indian Summer Monsoon (the Southwest Monsoon) for the central part of India. The long-term forecast means 40 days in advance for the onset date, and 70 days in advance for the withdrawal date. Our earliest forecast will be spread over India for taking appropriate decisions at various levels from farmers' fields to the Government of India.

The forecasting methodology for predicting the onset and withdrawal of the Indian Summer Monsoon was upscaled to predict timing of the rainfall season in southern Tanzania, Msimu rains. A study on rainfall season in the Southern Highlands of Tanzania - the grain basket of Tanzania, is part of the EPICC project. Tanzania's economy depends heavily on agriculture, and the livelihood of the Tanzanian farmer largely depends on the rains. The EPICC monsoon research group offers a unique methodology to identify tipping elements in climate variables, which allows a long-term prediction of the rainy season's timing. A long-term forecast means 40 days in advance for the start of the rainy season and 70 days in advance for its end. Such long-term forecasts aid agriculture planning in consolidating resources and strengthening capacity to respond effectively to disasters such as floods and droughts. This novel approach is welcomed by local ministries and stakeholders in anticipation of using the forecast for better risk management. 

Related information

Indian Summer Monsoon Forecasts, 2020

Withdrawal of the Indian Summer Monsoon 2020
On the 14th of August 2020, Prof. Dr. Surovyatkina published a forecast of the withdrawal date of the Indian Summer Monsoon 2020 from the central part of India. According to the forecast, the Indian Summer Monsoon (Southwest Monsoon) was likely to withdraw from the Central part of India (20N, 80E) between 3rd and 13th of October 2020. The actual withdrawal date was announced as the 13th of October, after which other post-monsoon isolated rainfalls appeared in some parts of central & northwest India.

Onset of the Indian Summer Monsoon 2020
The May 8 forecast released by EPICC Monsoon researcher Prof. Dr. Surovyatkina, announced the onset of the Indian Summer Monsoon in 2020 for the central part of India, the Eastern Ghats region (20°N,80°E) between the 18th and 26th of June 2020. The Summer Monsoon in Northern Telangana was predicted to set over between 16th and 24th of June 2020. The research group predicted delayed Monsoon onset and indeed the Northern Limit of Monsoon declared by IMD appeared over Eastern Ghats on 12th of June and resulted in rainfall in many regions in central India, however from 17-26 June, a dry spell appeared unexpectedly. The Monsoon rainfall in fact began over the Eastern Ghats on the 26 of June, confirming that the prediction of delayed Monsoon onset dates between 18th and 26th of June 2020 published by Prof. Dr. Surovyatkina was correct.

Prediction of Rainfall Season in Tanzania, 2020

Elena Surovyatkina and Nitin Babu performed the first long-term forecast for Africa on the 20th of October, 40 days in advance of the rainfall season. They predicted that the rainfall season in southern Tanzania was likely begin after the 10 of December 2020. Isolated rainfall and dry spells were expected between the 10th to the 31st of December. The actual rainfall season in southern Tanzania was delayed and began after the 10th of December 2020; continuous rainfalls were expected after the 31st of December. On the initial phase, isolated rains alternated with periods of dry spells, as was predicted on the 20th of October, 2020.

Contact persons

Prof. Dr. Elena Surovyatkina

Monsoon Forecasting

elena.surovyatkina[at]pik-potsdam.de


Nitin Babu George

Monsoon Forecasting, PhD Student

george[at]pik-potsdam.de



EL NIÑO FORECASTING

El Niño and La Niña episodes can cause extreme weather events like floods and droughts in Peru and other parts of the world. Our aim is to provide an automated algorithm for the forecasting of El Niño events with a pre-warning time of about one year. This algorithm was published in Ludescher et al. 2013; 2014 and has provided correct predictions since. We also aim to develop and provide new long term (around one year ahead) forecasts of El Niño events, including their onset, magnitude and impacts. Our research framework is based on complexity science and network theory.

Onset forecasting
The algorithm published in Ludescher et al. (2013, 2014) provided predictions with a high forecasting skill since 2012. In addition, Meng et al. (2018) introduced a new method, which correctly predicted the onset of the 2018/2019 El Niño one year ahead.

Magnitude forecasting
To improve the forecasting, in particular, to enable a magnitude forecasting with a one year lead time, Meng et al. (2019) developed the SysSampEn method. This method, for instance, forecasted for 2018-2019 an El Niño with a magnitude of 1.11 ± 0.23 ℃, which turned out to be correct with the observed value being 0.9°C.

Indian summer monsoon rainfall forecasting
Based on the complex network approach and the ENSO-monsoon teleconnections, Fan et al. (2021) developed the South-West Atlantic Subtropical Index (SWAS-index) to forecast the amount of the Indian summer monsoon rainfall with a 5-month lead-time  and a forecasting skill (correlation between forecasted and observed value) of 0.54. Remarkably, it was discovered, that the significant warming trend in the SWAS area yields to an improvement of the prediction skill.

Related information

  • PIK Press release: Breakthrough in El Niño Forecasting
  • PIK Press release: Unprecedented early warning of El Niño succeeds
  • Fan, J., Meng, J., Ashkenazy, Y., Havlin, S. and Schellnhuber, H. (2018). Climate network percolation reveals the expansion and weakening of the tropical component under global warming. Proceedings of the National Academy of Sciences, 115(52), pp.E12128-E12134. DOI: 10.1073/pnas.1811068115
  • Fan, J., Meng, J., Ludescher, J., Chen, X., Ashkenazy, Y., Kurths, J., Havlin, S. and Schellnhuber, H. (2021). Statistical physics approaches to the complex Earth system. Physics Reports, 896, pp.1-84. DOI: 10.1016/j.physrep.2020.09.005
  • Ludescher, J., Gozolchiani, A., Bogachev, M., Bunde, A., Havlin, S. and Schellnhuber, H. (2013). Improved El Nino forecasting by cooperativity detection. Proceedings of the National Academy of Sciences, 110(29), pp.11742-11745. DOI: 10.1073/pnas.1309353110
  • Ludescher, J., Gozolchiani, A., Bogachev, M., Bunde, A., Havlin, S. and Schellnhuber, H. (2014). Very early warning of next El Niño. Proceedings of the National Academy of Sciences, p.201323058. DOI: 10.1073/pnas.1323058111
  • Meng, J., Fan, J., Ashkenazy, Y., Bunde, A. and Havlin, S. (2018). Forecasting the magnitude and onset of El Niño based on climate network. New Journal of Physics, 20(4), p.043036. DOI: 10.1088/1367-2630/aabb25
  • Meng, J., Fan, J., Ludescher, J., Agarwal, A., Chen, X., Bunde, A., Kurths, J. and Schellnhuber, H. (2019). Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier. Proceedings of the National Academy of Sciences, 117(1), pp.177-183. DOI: 10.1073/pnas.1917007117


Contact persons

Dr. Josef Ludescher

El Niño Forecasting

ludescher[at]pik-potsdam.de




Prof. Dr. Jingfang Fan

System Analysis, Guest Researcher
jingfang[at]pik-potsdam.de




REGIONAL CLIMATE INFORMATION

Climate information is a basic component of the EPICC project and of great importance for stakeholders in all three partner countries. We will work closely with the Visualization Group (→ 1. Capacity building and knowledge transfer) to provide relevant climate information from observations, seasonal forecasts and future climate scenarios. This data will also serve as a basis for modelling, predicting and analyzing river discharge ( → 3. Hydrology and Water Resources), crop yields (→ 4. Agriculture) and migration patterns (→ 5. Migration).

Seasonal forecasts will be based on statistical models (→ Monsoon and ENSO prediction) and dynamical models. In particular, we aim to deliver customized seasonal climate forecasts for India, Peru and Tanzania based on the global forecast of the German Climate Forecast System (GCFS) in close co-operation with the German Meteorological Service (DWD).

The long-term climate predictions will be based on CMIP5 and CORDEX data and be processed in accordance with ISIMIP guidelines.

Related information

Contact person

Dr. Stephanie Gleixner

Regional Climate Data Scientist

gleixner[at]pik-potsdam.de





Scientific advisors
 

Prof. Dr. Stefan Rahmstorf
Climate Change
rahmstorf[at]pik-potsdam.de

Prof. Dr. Jürgen Kurths
Complex Systems Science
juergen.kurths[at]pik-potsdam.de

Dr. Peter Hoffmann
Regional Climate Forecasting
peterh[at]pik-potsdam.de





TERI IKI BMU