From Theory to Practice in Anthropogenic Climate Change: More than a Century of Monitoring and Prediction
Abstract: Svante Arrhenius proposed a detailed theory of “the influence of carbonic acid in the air upon the temperature of the ground” in his groundbreaking paper published in 1896. He not only modeled the direct links between carbonic acid (CO2) concentrations, atmospheric absorption of infrared radiation, and surface temperature, but also worked to account for the interactive effects of humidity, cloud cover, and albedo. He developed predictions of temperature change by latitude and season and for both decreases and increases in CO2 levels, and discussed the potential for snow-ice albedo feedback and changes in the temperature differences between day and night. However, it was not until the early 1970s that serious consideration was given to the potential for “inadvertent climate modification,” driven in part by long-term measurements of increasing atmospheric CO2 at Mauna Loa by David Keeling. In 1979, the U.S. National Research Council published a consensus study led by Jule Charney, Carbon Dioxide and Climate: A Scientific Assessment, which reviewed available models and evidence about climate change and estimated that “the most probable global warming for a doubling of CO2 to be near 3°C with a probable error of ±1.5°C.” Also during the 1970s, a number of glaciologists proposed that the West Antarctic Ice Sheet could be susceptible to deglaciation should the climate warm, which would lead to significant sea level rise. Now, nearly 50 years later, assessments by the Intergovernmental Panel on Climate Change (IPCC) have clearly established that the climate is indeed warming due to greenhouse gas emissions, with likely profound impacts on both environmental and human systems. Many changes in the cryosphere are now evident, including signs of deglaciation predicted in the 1970s. Given the changes under way, should the scientific community rethink its approach to monitoring and prediction and their role in the policy realm? Are there ways to provide more systematic, timely, cross-disciplinary and cross-sector metrics, better integrated with model predictions and uncertainty estimates, and delivered in ways that facilitate decision making about mitigation and adaptation? Can new sources of data and rapidly evolving data science and artificial intelligence methods help the world reach its sustainable development goals in the face of climate change?