The weather forecast that you see on TV or on your mobile actually starts with collecting data using satellites, weather radars, and ground and upper-air in situ measurements. Weather forecast is an initial value problem. To produce a forecast, a numerical weather prediction model processes today’s weather observations (hence the name initial value) based on millions of highly nonlinear differential equations that evolve the dynamics, thermodynamics, chemical, and radiative processes over time, allowing the atmosphere, ocean surface, land surface, and snow and ice to interact with each other. For each round of weather forecast, many billions of data points are integrated into the model. This type of model is very computation intensive and run on supercomputers.
The accuracy of weather forecast still has much to be desired. This is because within the description above, there are several ways that errors could be introduced. (1) Weather observations are not perfect. (2) The equations that describe the atmosphere, ocean, and land at regular intervals over the earth also have gaps between them. (3) Over time, the highly nonlinear equations will magnify any computational errors or errors introduced into the initial values. (4) Imperfect understanding—and hence imperfect mathematical representations—of the physical processes of the atmosphere, ocean, and land in the model.
Climate prediction uses similar type of models as in weather prediction. But the similarity stops here. Compared to the initial-value approach in weather prediction, climate prediction does not use today’s climate. It uses a time-projection of the atmospheric greenhouse gas content (e.g., over the next 80 years to the end of the 21st century) to force the climate system. In respond to the continuous increase in greenhouse gases, the model’s radiative processes warm up the atmosphere, land, and ocean. In addition, climate prediction is concerned with the responses of the climate system to this forcing over large areas and in the long term, not the weather in a week’s time over your location.
There are still many uncertainties in climate prediction. Climate models still do not sufficiently account for several of the climate feedback effects because their physics are not yet well understood. These feedback effects include (1) the melting of polar icecaps in reducing the earth’s reflectivity, (2) the thawing of permafrost in releasing CO2 and CH4 into the atmosphere, and (3) the ocean’s capacity to continue to absorb CO2 from the atmosphere under warming condition. Even with these uncertainties, we know these feedback effects will accelerate climate change.
Climate prediction is not the only tool we have to understand climate change. Observations in the past 50 years have shown that sea level is rising slowly, water stress around the world is worsening, and coastal storms are becoming stronger. We are now facing the uncertainty of not whether climate change is real, but how bad it will become.