Workshop: How climate change is shaping Africa

Children in the Kibera slum in Nairobi, Kenya, which is prone to flooding. Photo by Erik Kolstad

UPDATE: The seminar is postponed due to the Corona crisis.

The Bjerknes Centre for Climate Research at the University of Bergen and Christian Michelsen Institute (CMI) are organizing a one-day, invitation-only workshop entitled How climate change is shaping Africa. The workshop will take place at Litteraturhuset in Bergen from 10 a.m. to 6 p.m. on 21 April 2020, ending with a workshop dinner. Norway’s minister of international development, Dag Inge Ulstein, has agreed to open the workshop, and Henrik Urdal – the director of PRIO – will give one of the keynote presentations.

The goal of the workshop is to identify new opportunities for cross-disciplinary research on climate change and African development in Norway. Structurally, the workshop will consist of two parts: 1) Invited keynote presentations covering policy, scientific challenges, and knowledge gaps and needs; and 2) Group work to discuss concrete opportunities for research proposals (identify relevant calls, construct proposal outlines). We also aim to write a policy brief or perspective article based on the discussions.

If you would like to participate (or if you know someone who might), please get in contact with Erik Kolstad at the Bjerknes Centre and/or Aslak Orre at CMI as soon as possible.

Programme (subject to change):

  • 10.00: Opening keynote by Dag Inge Ulstein
  • 10.15: Keynote presentation by Henrik Urdal
  • 10.45: Two more keynote presentations
  • 11.45: Break
  • 12.00: Plenary discussion
  • 12.30: Lunch
  • 13.30: 6 x 10+5 minute presentations
  • 15.00: Break
  • 15.15: Group work
  • 16.45: Break
  • 17.00: Plenary discussion
  • 18.00: Dinner at Colonialen

June 2019 forecast

One of our projects, Seasonal Forecasting Engine, recently published their June forecast based on five numerical weather prediction models. The June forecast was remarkable in that it predicted almost no temperature anomalies in Northern Europe. When we sum up in July, we will probably find that some regions experienced either warm or cold anomalies, but there was no consistent signal across the models this time.

In Climate Futures, we will make use of advanced statistics and machine learning to create algorithms that improve the skill of forecasts going from 10 days to 10 years into the future. The key will be to learn how different climate variables interact and then to use that knowledge to make better use of the physical models. Erik Kolstad, the PI of the SFE project, was the lead author of a 2017 article on the different roles that snow cover, soil moisture and soil temperature play in dictating the persistence of temperature from month to month.

A key figure in that paper showed which of these variable were the most important mediator of persistence in winter and summer:

The colours in each panel show that dominant mediator in winter (top row) and summer (bottom row) for two different reanalysis products, ERA-20C (left column) and 20CR (right column). Yellow colours indicate that snow depth is the most important mediator, while red and blue colours mean that soil temperature and soil moisture, respectively, dominate.

At midlatitudes, snow depth plays an important role. This means that when there is a lot of snow on the ground, the temperature will probably stay anomalously cold from January to February. Or if there is thin snow or no snow at all, the temperature will be anomalously warm in both months. The specific role of snow was examined in a follow-up study. In summer, soil variables dominate completely.

Our long-term goal is to assign a higher weight to the models that represent these physical mechanisms well while assigning a lower weight to less realistic models. To be able to plough through the huge data amounts available from models, observations and satellite imagery, we need to let computers do the heavy lifting. Another important reason not to do this manually is that humans are prone to making mistakes, often in the form of being too subjective. Machines, for better or worse, do not have this bias, which we believe makes them ideally suited for this work.

Stay tuned for our July forecast and other developments!