Long Term Agreement for the provision of Climate Services, Analyses, Knowledge and Evidence

DESCRIPTION: Climate analysis is a core activity at IRI. The institute’s initial aim to make the science of climate prediction available to society motivated the necessity to ground credibility of seasonal forecasts in physical understanding of the mechanisms that give rise to climate anomalies such as droughts, floods or heat waves, and their relation to predictable phenomena such as the El Niño-Southern Oscillation (ENSO). Climate analysis at IRI has evolved to include the characterization of time scales of variation shorter and longer than seasonal-to-interannual. Work on the intra-seasonal (1 week up to 3 months) time scale encompasses variations in the frequency and intensity of extreme events, and is more relevant to early warning in disaster risk reduction. Work on the near-term (the next 10 to 20 years), with decadal variability a confounding factor in the interpretation of long-term/anthropogenic trends, is more relevant to long-term planning, infrastructure investments, and for improving climate adaptation. IRI scientists have developed and refined statistical tools to partition interannual, decadal and long-term time scales of temporal variation on spatial scales from global to regional. We are pioneers in the predictability of intra-seasonal climate characteristics such as frequency and intensity of daily precipitation. We analyze long-term observations and model simulations of the climatic response to anthropogenic forcing, as used in IPCC assessments, as well as ad-hoc experiments to better understand regional responses to specific persistent anomalies in sea-surface temperatures, for example, to interpret recent rainfall or temperature anomalies and current and projected trends.

At the IRI, climate scientists employ an array of quantitative tools, statistical and dynamical, to characterize variations in climate over all time scales, from intra-seasonal to multi-decadal. Our approach involves the characterization of variation and trends, combined with their physical interpretation and assessment of climate model capabilities to simulate features of the climate that are relevant to policy makers. Such an approach allows for the development of qualitative ‘worst case’ and ‘best guess’ scenario narratives that can then facilitate engagement with policy-makers in both disaster risk reduction and climate change adaptation. If needed, these qualitative assessments can be made quantitative using stochastic modeling that partitions the relative relevance of natural variability and anthropogenic response in a regional context.