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Peter Good, Chris Jones, Jason Lowe, Richard Betts, and Nicola Gedney

Abstract

Future changes in atmospheric greenhouse gas concentrations and associated influences on climate could affect the future sustainability of tropical forests. The authors report on tropical forest projections from the new Hadley Centre Global Environmental Model version 2 Earth System configuration (HadGEM2-ES) and compare them to results from the previous generation model [third climate configuration of the Met Office Unified Model in lower resolution with carbon cycle (HadCM3LC)], which had projected near-complete dieback of the Amazon rain forest for a business as usual scenario. In contrast, HadGEM2-ES projects minimal change in Amazon forest extent. The main aim of this study is a preliminary investigation of this difference between the two models. It is found that around 40% of the difference in forest dieback projections is associated with differences in the projected change in dry-season length. Differences in control climatologies of temperature and dry-season length, projected regional warming, and the forest response to climate and CO2 also all contribute to the increased survival of forest in HadGEM2-ES. However, HadGEM2-ES does not invalidate HadCM3LC: Amazon dieback remains a possible scenario of dangerous change that requires further understanding. The authors discuss the relevance to assessments of dieback risk and future work toward narrowing uncertainty about the fate of the Amazon forest.

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Nikolaos Christidis, Richard A. Betts, and Peter A. Stott
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Pete Falloon, Richard Betts, Andrew Wiltshire, Rutger Dankers, Camilla Mathison, Doug McNeall, Paul Bates, and Mark Trigg

Abstract

The Total Runoff Integrating Pathways (TRIP) global river-routing scheme in the third climate configuration of the Met Office Unified Model (HadCM3) and the newer Hadley Centre Global Environmental Model version 1 (HadGEM1) general circulation models (GCMs) have been validated against long-term average measured river discharge data from 40 stations on 24 major river basins from the Global Runoff Data Centre (GRDC). TRIP was driven by runoff produced directly by the two GCMs in order to assess both the skill of river flows produced within GCMs in general and to test this as a method for validating large-scale hydrology in GCMs. TRIP predictions of long-term-averaged annual discharge were improved at 28 out of 40 gauging stations on 24 of the world’s major rivers in HadGEM1 compared to HadCM3, particularly for low- and high-latitude basins, with predictions ranging from “good” (within 20% of observed values) to “poor” (biases exceeding 50%). For most regions, the modeled annual average river flows tended to be exaggerated in both models, largely reflecting inflated estimates of precipitation, although lack of human interventions in this modeling setup may have been an additional source of error. Within individual river basins, there were no clear trends in the accuracy of HadGEM1 versus HadCM3 predictions at up- or downstream gauging stations. Relative root-mean-square error (RRMSE) scores for the annual cycle of river flow ranged from poor (>50%) to “fair” (20%–50%) with an overall range of 20.7%–1023.5%, comparable to that found in similar global-scale studies. In both models, simulations of the annual cycle of river flow were generally better for high-latitude basins than in low or midlatitudes. There was a relatively small improvement in the annual cycle of river flow in HadGEM1 compared to HadCM3, mostly in the low-latitude rivers. The findings suggest that there is still substantial work to be done to enable GCMs to simulate monthly discharge consistently well over the majority of basins, including improvements to both (i) GCM simulation of basin-scale precipitation and evaporation and (ii) hydrological processes (e.g., representation of dry land hydrology, floodplain inundation, lakes, snowmelt, and human intervention).

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Peter Good, Chris Jones, Jason Lowe, Richard Betts, Ben Booth, and Chris Huntingford

Abstract

Future changes in atmospheric greenhouse gas concentrations, and their associated influences on climate, will affect the future sustainability of tropical forests. While dynamic global vegetation models (DGVMs) represent the processes by which climate and vegetation interact, there is limited quantitative understanding of how specific environmental drivers each affect the simulated patterns of vegetation behavior and the resultant tropical forest fraction. Here, an attempt is made to improve on the qualitative understanding of how changes in dry season length, temperature, and CO2 combine to drive forest changes. Investigation of these topics is undertaken by integrating the Hadley Centre Climate Model version 3, run at lower spatial resolution with a coupled climate–carbon cycle (HadCM3LC), to steady state. This represents the situation where vegetation has adjusted fully to the prevailing climate and vice versa, permitting direct analysis of how climate and vegetation interact. These links are quantified by fitting the simulated tropical broadleaf tree fraction with a simple function of CO2 concentration, surface temperature, and dry season length. The resulting empirical function (denoted dry season resilience or DSR) is able to predict a sustainable tropical broadleaf fraction in this model across a very wide range of climates. The DSR function can also be used to compare the importance of different environmental drivers and to explore other emissions scenarios. While this DSR function is specific to the vegetation–land surface scheme in HadCM3LC, the method employed in this work is applicable to steady-state simulations from other vegetation–land surface schemes.

The DSR metric is applied first as a framework to evaluate the DGVM by comparison of the simulated and observed forest fractions. For tropical broadleaf resilience in this model, a warming of 1°C is approximately equivalent to a 2-week increase in dry season. In HadCM3LC climate model projections under the International Panel on Climate Change’s (IPCC’s) Special Report on Emissions Scenarios (SRES) A1B scenario, twenty-first-century increases in forest resilience due to CO2 fertilization approximately balance the tropical mean decrease from warming (the relative importance of rainfall and temperature changes depends on the uncertain spatial pattern of rainfall change). DSR is a tool that could be applied to different vegetation models to help us understand and narrow uncertainty in tropical forest projections.

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Jose A. Marengo, Luiz E.O.C. Aragão, Peter M. Cox, Richard Betts, Duarte Costa, Neil Kaye, Lauren T. Smith, Lincoln M. Alves, and Vera Reis
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Peter Uhe, Dann Mitchell, Paul D. Bates, Myles R. Allen, Richard A. Betts, Chris Huntingford, Andrew D. King, Benjamin M. Sanderson, and Hideo Shiogama

Abstract

Precipitation events cause disruption around the world and will be altered by climate change. However, different climate modeling approaches can result in different future precipitation projections. The corresponding “method uncertainty” is rarely explicitly calculated in climate impact studies and major reports but can substantially change estimated precipitation changes. A comparison across five commonly used modeling activities shows that, for changes in mean precipitation, less than half of the regions analyzed had significant changes between the present climate and 1.5°C global warming for the majority of modeling activities. This increases to just over half of the regions for changes between present climate and 2°C global warming. There is much higher confidence in changes in maximum 1-day precipitation than in mean precipitation, indicating the robust influence of thermodynamics in the climate change effect on extremes. We also find that none of the modeling activities captures the full range of estimates from the other methods in all regions. Our results serve as an uncertainty map to help interpret which regions require a multimethod approach. Our analysis highlights the risk of overreliance on any single modeling activity and the need for confidence statements in major synthesis reports to reflect this method uncertainty. Considering multiple sources of climate projections should reduce the risks of policymakers being unprepared for impacts of warmer climates relative to using single-method projections to make decisions.

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