Representing Climate and Extreme Weather Events in Integrated Assessment Models: A Review of Existing Methods and Options for Development

Abstract

The lack of information about future changes in extreme weather is a major constraint of Integrated Assessment Models (IAMs) of climate change. The generation of descriptions of future climate in current IAMs is assessed. We also review recent work on scenario development methods for weather extremes, focusing on those issues which are most relevant to the needs of IAMs. Finally, some options for implementing scenarios of weather extremes in IAMs are considered.Keywords: Integrated Assessment Models, climate change, scenarios, extreme weather events, general circulation models, regional climate models. INTEGRATED ASSESSMENT AND CLIMATE CHANGEOne of the essential characteristics of integrated assessment is the simultaneous consideration of the multiple dimensions of environmental problems such as climate change. A number of formal integrated assessment models (IAMs) for climate change have been developed over the last decade, starting with the models IMAGE 1.0 and ESCAPE in the early 1990s [1,2]. These models were constructed using modules that are reduced-form versions of more complex models, for example of the climate system, the economy and ecosystems. Most climate modules in IAMs generate zero (i.e., globally-averaged; e.g., PAGE) or one (i.e., zonallyaveraged; e.g., IMAGE) dimensional descriptions of future climate, usually at a mean (e.g., 30-year average) seasonal or annual resolution [3][4][5]. Some IAMs (e.g., IMAGE 2.2 and AIM) then generate spatially explicit, i.e., two-dimensional, descriptions of future climate, usually by accessing stored patterns of climate change derived from more complex General Circulation Model (GCM) experiments [6][7][8].These approaches to generating future climate descriptions in IAMs, which are reviewed in Section 2, are computationally efficient and allow multiple experiments to be easily conducted in an integrated framework. The climate output is then input into an ecosystem, agriculture or health impacts module (e.g., AIM), or used directly to estimate the economic cost of climate damage from a lookup climate damage function (e.g., DICE). In either case, the current lack of any information about changes in daily or extreme weather (the focus of this review) is a major weakness. Agriculture, for example, is likely to be as, or more, sensitive to changes in daily weather sequences and the occurrence of extreme weather events than to changes in mean monthly or seasonal climate [9][10][11]. Potential changes in extremes and the ensuing changes in risk are also important for sectors such as water resources and insurance [12,13]. Climate damage functions that express the economic impact of climate change as a function of global-(or regional-) mean climate alone are likely to underestimate the economic damage associated with extreme events such as flooding and storms (e.g., in the UK, the October 1987 windstorm event is estimated to have cost insurers 3.1 billion US dollars, with further economic losses of 2.7 billion US dollars [14]). The lack of information about chan...

Why economic dynamics matter in assessing climate change damages: Illustration on extreme events

“…We concentrated however on the latter because they attract attention to the linkages between short-run responses to shocks (capital destruction, break-down of essential services like electricity or drinking water) and long-term dynamics. Another reason is that they are both poorly represented in current integrated assessment models (Goodess et al, 2003) and far more documented than other types of climate impacts.…”

The role of interactions in a world implementing adaptation and mitigation solutions to climate change

“…Simple IAMs are not capable of representing such processes in detail, although PAGE attempts to provide a scenario that accounts for increases in extremes. Cumulative distribution functions might be constructed from statistical relationships between extremes and predictor variables used in these simple IAMs [3], enabling them to better represent the impacts of extremes. -the climate change projections provided to the model are at the appropriate temporal resolution (monthly for droughts, and daily for most other extreme weather events), and include projected increases in extremes.…”

“…Integrated assessment models (IAMs) were developed to encompass an interdisciplinary approach to the study of climate change and climate change policy. Goodess et al [3] provide a full review and categorization of these models. A brief summary of the types of models is given here (table 1).…”

“…Biophysically based IAMs such as IMAGE, AIM, ICLIPS, GCAM and CLIMPACTS ( [3] and references therein) variously examine sectors and/or regions of the world, spanning multiple disciplines, and thus theoretically allowing models to capture interactions between sectors. However, few have yet exploited their full potential to study interactions.…”

Developing site‐specific future temperature scenarios for Northern Ireland: addressing key issues employing a statistical downscaling approach

“…Results were mapped according to magnitude of warming from the 1961-1990 modelled baseline period. In addition, three of the Statistical and regional dynamical downscaling of extremes for European regions (STARDEX) diagnostic tests (Goodess et al, 2003) for temperature were Temp 9 9 temp 9 9 Rhum 3 8 s500 9 6 p5zh 8 2 s850 9 3 s850 3 7 p5zh 8 2 p5 v 8 0 p500 2 8 examined in order to investigate changes in future temperature extremes. An overview of the downscaling process and the key model choices is presented in Table IV.…”